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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Google DeepMind has a new way to look inside an AI’s “mind”

We don’t know exactly how AI works, or why it works so well. That’s a problem: It could lead us to deploy an AI system in a highly sensitive field like medicine without understanding that it could have critical flaws embedded in its workings.

A team at Google DeepMind that studies something called mechanistic interpretability has been working on new ways to let us peer under the hood. It recently released a tool to help researchers understand what is happening when AI is generating an output.

It’s all part of a push to get a better understanding of exactly what is happening inside an AI model. If we do, we’ll be able to control its outputs more effectively, leading to better AI systems in the future. Read the full story.

—Scott J Mulligan

What’s on the table at this year’s UN climate conference

Talks kicked off this week at COP29 in Baku, Azerbaijan. Running for a couple of weeks each year, the global summit is the largest annual meeting on climate change.

The issue on the table this time around: Countries need to agree to set a new goal on how much money should go to developing countries to help them finance the fight against climate change. Complicating things? A US president-elect whose approach to climate is very different from that of the current administration (understatement of the century).

This is a big moment that could set the tone for what the next few years of the international climate world looks like. Here’s what you need to know about COP29 and how Donald Trump’s election is coloring things.

—Casey Crownhart

This story is from The Spark, our weekly newsletter giving you the inside track on all things energy and climate. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The FBI is investigating crypto predictions-betting platform Polymarket
It’s investigating whether the firm allowed US traders to bet on the election. (Bloomberg $)
+ Doing so would have been a violation of an agreement with the US government. (NYT $)
+ Polymarket claims to be a “fully transparent prediction market.” (WSJ $)

2 OpenAI is calling for the US government to invest in AI
Without financial support, the US could lose crucial ground to China, it warns. (WP $)
+ The firm floated the idea of building a colossal data center. (The Information $)

3 AI-generated Elon Musk propaganda is rife on Facebook
Pro-Musk inspiration porn is the content of choice for spammers. (404 Media)
+ Trump is surrounding himself with terminally online edgelords. (The Atlantic $)

4 The online right has a misogynistic new rallying cry
‘Your body, my choice’ is being spread by young men seeking to provoke. (New Yorker $)+ The upcoming presidency could usher in an age of gendered regression. (The Guardian)

5 China’s human factory workers are under pressure
Robots are creeping into every level of the manufacturing process. (FT $)
+ Three reasons robots are about to become way more useful. (MIT Technology Review)
6 The future of chipmaking in AmericaEfforts to revitalize native facilities aren’t exactly going to plan. (Wired $)
+ What’s next in chips. (MIT Technology Review)

7 Blindbox live streaming is thrilling shoppers in ChinaYou never know what you’re going to get. (NYT $)

8 What the glacial Earth may have looked like
Around 700 million years ago, the entire planet was covered in ice. (Ars Technica)
+ Life-seeking, ice-melting robots could punch through Europa’s icy shell. (MIT Technology Review)

9 How to protect the world’s largest single coral colony
The newly-discovered colony is the size of two basketball courts. (Vox)
+ The race is on to save coral reefs—by freezing them. (MIT Technology Review)

10 These researchers have reinvented the wheel
This ‘morphing’ wheel can roll over obstacles up to 1.3 times the height of its radius. (Reuters)

Quote of the day

“Shawty crunk, so fresh, so clean.”

—Mark Zuckerberg, Meta CEO-turned rapper, debuts a reworked version of 2002 rap hit Get Low in a tribute to his wife, the Wall Street Journal reports.

The big story

Marseille’s battle against the surveillance state

June 2022Across the world, video cameras have become an accepted feature of urban life. Many cities in China now have dense networks of them, and London and New Delhi aren’t far behind. Now France is playing catch-up.

Concerns have been raised throughout the country. But the surveillance rollout has met special resistance in Marseille, France’s second-biggest city.

It’s unsurprising, perhaps, that activists are fighting back against the cameras, highlighting the surveillance system’s overreach and underperformance. But are they succeeding? Read the full story.

—Fleur Macdonald

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• This year’s gurning championship winning mugshots do not disappoint.
• What does it mean to have personal style, exactly?
• Amsterdam’s unofficial police cat is absolutely adorable (and he lives on a boat!)
• Save the worms—this writer certainly is.

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This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

It’s time for a party—the Conference of the Parties, that is. Talks kicked off this week at COP29 in Baku, Azerbaijan. Running for a couple of weeks each year, the global summit is the largest annual meeting on climate change.

The issue on the table this time around: Countries need to agree to set a new goal on how much money should go to developing countries to help them finance the fight against climate change. Complicating things? A US president-elect whose approach to climate is very different from that of the current administration (understatement of the century).

This is a big moment that could set the tone for what the next few years of the international climate world looks like. Here’s what you need to know about COP29 and how Donald Trump’s election is coloring things.

The UN COP meetings are an annual chance for nearly 200 nations to get together to discuss (and hopefully act on) climate change. Greatest hits from the talks include the Paris Agreement, a 2015 global accord that set a goal to limit global warming to 1.5 °C (2.7 °F) above preindustrial levels.

This year, the talks are in Azerbaijan, a petrostate if there ever was one. Oil and gas production makes up over 90% of the country’s export revenue and nearly half its GDP as of 2022. A perfectly ironic spot for a global climate summit!

The biggest discussion this year centers on global climate finance—specifically, how much of it is needed to help developing countries address climate change and adapt to changing conditions. The current goal, set in 2009, is for industrialized countries to provide $100 billion each year to developing nations. The deadline was 2020, and that target was actually met for the first time in 2022, according to the Organization for Economic Cooperation and Development, which keeps track of total finance via reports from contributing countries. Currently, most of that funding is in the form of public loans and grants.

The thing is, that $100 billion number was somewhat arbitrary—in Paris in 2015, countries agreed that a new, larger target should be set in 2025 to take into account how much countries actually need.

It’s looking as if the magic number is somewhere around $1 trillion each year. However, it remains to be seen how this goal will end up shaking out, because there are disagreements about basically every part of this. What should the final number be? What kind of money should count—just public funds, or private investments as well? Which nations should pay? How long will this target stand? What, exactly, would this money be going toward?

Working out all those details is why nations are gathering right now. But one shadow looming over these negotiations is the impending return of Donald Trump.

As I covered last week, Trump’s election will almost certainly result in less progress on cutting emissions than we might have seen under a more climate-focused administration. But arguably an even bigger deal than domestic progress (or lack thereof) will be how Trump shifts the country’s climate position on the international stage.

The US has emitted more carbon pollution into the atmosphere than any other country, it currently leads the world in per capita emissions, and it’s the world’s richest economy. If anybody should be a leader at the table in talks about climate finance, it’s the US. And yet, Trump is coming into power soon, and we’ve all seen this film before.

Last time Trump was in office, he pulled the US out of the Paris Agreement. He’s made promises to do it again—and could go one step further by backing out of the UN Framework Convention on Climate Change (UNFCCC) altogether. If leaving the Paris Agreement is walking away from the table, withdrawing from the UNFCCC is like hopping on a rocket and blasting in a different direction. It’s a more drastic action and could be tougher to reverse in the future, though experts also aren’t sure if Trump could technically do this on his own.

The uncertainty of what happens next in the US is a cloud hanging over these negotiations. “This is going to be harder because we don’t have a dynamic and pushy and confident US helping us on climate action,” said Camilla Born, an independent climate advisor and former UK senior official at COP26, during an online event last week hosted by Carbon Brief.

Some experts are confident that others will step up to fill the gap. “There are many drivers of climate action beyond the White House,” said Mohamed Adow, founding director of Power Shift Africa, at the CarbonBrief event.

If I could characterize the current vibe in the climate world, it’s uncertainty. But the negotiations over the next couple of weeks could provide clues to what we can expect for the next few years. Just how much will a Trump presidency slow global climate action? Will the European Union step up? Could this cement the rise of China as a climate leader? We’ll be watching it all.

Now read the rest of The SparkRelated readingIn case you want some additional context from the last few years of these meetings, here’s my coverage of last year’s fight at COP28 over a transition away from fossil fuels, and a newsletter about negotiations over the “loss and damages” fund at COP27.

For the nitty-gritty details about what’s on the table at COP29, check out this very thorough explainer from Carbon Brief.

DAN THORNBERG/ADOBE STOCKAnother thingTrump’s election will have significant ripple effects across the economy and our lives. His victory is a tragic loss for climate progress, as my colleague James Temple wrote in an op-ed last week. Give it a read, if you haven’t already, to dig into some of the potential impacts we might see over the next four years and beyond.

Keeping up with climate The US Environmental Protection Agency finalized a rule to fine oil and gas companies for methane emissions. The fee was part of the Inflation Reduction Act of 2022. (Associated Press)
→ This rule faces a cloudy future under the Trump administration; industry groups are already talking about repealing it. (NPR)

Speaking of the EPA, Donald Trump chose Lee Zeldin, a former Republican congressman from New York, to lead the agency. Zeldin isn’t particularly known for climate or economic policy. (New York Times)

Oil giant BP is scaling back its early-stage hydrogen projects. The company revealed in an earnings report that it’s canceling 18 such projects and currently plans to greenlight between five and 10. (TechCrunch)

Investors betting against renewable energy scored big last week, earning nearly $1.2 billion as stocks in that sector tumbled. (Financial Times)

Lithium iron phosphate batteries are taking over the world, or at least electric vehicles. These lithium-ion batteries are cheaper and longer-lasting than their nickel-containing cousins, though they also tend to be heavier. (Canary Media)
→ I wrote about this trend last year in a newsletter about batteries and their ingredients. (MIT Technology Review)

The US unveiled plans to triple its nuclear energy capacity by 2050. That’s an additional 200 gigawatts’ worth of consistently available power. (Bloomberg)

Five subsea cables that can help power millions of homes just got the green light in Great Britain. The projects will help connect the island to other power grids, as well as to offshore wind farms in Dutch and Belgian waters. (The Guardian)

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AI has led to breakthroughs in drug discovery and robotics and is in the process of entirely revolutionizing how we interact with machines and the web. The only problem is we don’t know exactly how it works, or why it works so well. We have a fair idea, but the details are too complex to unpick. That’s a problem: It could lead us to deploy an AI system in a highly sensitive field like medicine without understanding that it could have critical flaws embedded in its workings.

A team at Google DeepMind that studies something called mechanistic interpretability has been working on new ways to let us peer under the hood. At the end of July, it released Gemma Scope, a tool to help researchers understand what is happening when AI is generating an output. The hope is that if we have a better understanding of what is happening inside an AI model, we’ll be able to control its outputs more effectively, leading to better AI systems in the future.

“I want to be able to look inside a model and see if it’s being deceptive,” says Neel Nanda, who runs the mechanistic interpretability team at Google DeepMind. “It seems like being able to read a model’s mind should help.”

Mechanistic interpretability, also known as “mech interp,” is a new research field that aims to understand how neural networks actually work. At the moment, very basically, we put inputs into a model in the form of a lot of data, and then we get a bunch of model weights at the end of training. These are the parameters that determine how a model makes decisions. We have some idea of what’s happening between the inputs and the model weights: Essentially, the AI is finding patterns in the data and making conclusions from those patterns, but these patterns can be incredibly complex and often very hard for humans to interpret.

It’s like a teacher reviewing the answers to a complex math problem on a test. The student—the AI, in this case—wrote down the correct answer, but the work looks like a bunch of squiggly lines. This example assumes the AI is always getting the correct answer, but that’s not always true; the AI student may have found an irrelevant pattern that it’s assuming is valid. For example, some current AI systems will give you the result that 9.11 is bigger than 9.8. Different methods developed in the field of mechanistic interpretability are beginning to shed a little bit of light on what may be happening, essentially making sense of the squiggly lines.

“A key goal of mechanistic interpretability is trying to reverse-engineer the algorithms inside these systems,” says Nanda. “We give the model a prompt, like ‘Write a poem,’ and then it writes some rhyming lines. What is the algorithm by which it did this? We’d love to understand it.”

To find features—or categories of data that represent a larger concept—in its AI model, Gemma, DeepMind ran a tool known as a “sparse autoencoder” on each of its layers. You can think of a sparse autoencoder as a microscope that zooms in on those layers and lets you look at their details. For example, if you prompt Gemma about a chihuahua, it will trigger the “dogs” feature, lighting up what the model knows about “dogs.” The reason it is considered “sparse” is that it’s limiting the number of neurons used, basically pushing for a more efficient and generalized representation of the data.

The tricky part of sparse autoencoders is deciding how granular you want to get. Think again about the microscope. You can magnify something to an extreme degree, but it may make what you’re looking at impossible for a human to interpret. But if you zoom too far out, you may be limiting what interesting things you can see and discover.

DeepMind’s solution was to run sparse autoencoders of different sizes, varying the number of features they want the autoencoder to find. The goal was not for DeepMind’s researchers to thoroughly analyze the results on their own. Gemma and the autoencoders are open-source, so this project was aimed more at spurring interested researchers to look at what the sparse autoencoders found and hopefully make new insights into the model’s internal logic. Since DeepMind ran autoencoders on each layer of their model, a researcher could map the progression from input to output to a degree we haven’t seen before.

“This is really exciting for interpretability researchers,” says Josh Batson, a researcher at Anthropic. “If you have this model that you’ve open-sourced for people to study, it means that a bunch of interpretability research can now be done on the back of those sparse autoencoders. It lowers the barrier to entry to people learning from these methods.”

Neuronpedia, a platform for mechanistic interpretability, partnered with DeepMind in July to build a demo of Gemma Scope that you can play around with right now. In the demo, you can test out different prompts and see how the model breaks up your prompt and what activations your prompt lights up. You can also mess around with the model. For example, if you turn the feature about dogs way up and then ask the model a question about US presidents, Gemma will find some way to weave in random babble about dogs, or the model may just start barking at you.

One interesting thing about sparse autoencoders is that they are unsupervised, meaning they find features on their own. That leads to surprising discoveries about how the models break down human concepts. “My personal favorite feature is the cringe feature,” says Joseph Bloom, science lead at Neuronpedia. “It seems to appear in negative criticism of text and movies. It’s just a great example of tracking things that are so human on some level.”

You can search for concepts on Neuronpedia and it will highlight what features are being activated on specific tokens, or words, and how strongly each one is activated. “If you read the text and you see what’s highlighted in green, that’s when the model thinks the cringe concept is most relevant. The most active example for cringe is somebody preaching at someone else,” says Bloom.

Some features are proving easier to track than others. “One of the most important features that you would want to find for a model is deception,” says Johnny Lin, founder of Neuronpedia. “It’s not super easy to find: ‘Oh, there’s the feature that fires when it’s lying to us.’ From what I’ve seen, it hasn’t been the case that we can find deception and ban it.”

DeepMind’s research is similar to what another AI company, Anthropic, did back in May with Golden Gate Claude. It used sparse autoencoders to find the parts of Claude, their model, that lit up when discussing the Golden Gate Bridge in San Francisco. It then amplified the activations related to the bridge to the point where Claude literally identified not as Claude, an AI model, but as the physical Golden Gate Bridge and would respond to prompts as the bridge.

Although it may just seem quirky, mechanistic interpretability research may prove incredibly useful. “As a tool for understanding how the model generalizes and what level of abstraction it’s working at, these features are really helpful,” says Batson.

For example, a team lead by Samuel Marks, now at Anthropic, used sparse autoencoders to find features that showed a particular model was associating certain professions with a specific gender. They then turned off these gender features to reduce bias in the model. This experiment was done on a very small model, so it’s unclear if the work will apply to a much larger model.

Mechanistic interpretability research can also give us insights into why AI makes errors. In the case of the assertion that 9.11 is larger than 9.8, researchers from Transluce saw that the question was triggering the parts of an AI model related to Bible verses and September 11. The researchers concluded the AI could be interpreting the numbers as dates, asserting the later date, 9/11, as greater than 9/8. And in a lot of books like religious texts, section 9.11 comes after section 9.8, which may be why the AI thinks of it as greater. Once they knew why the AI made this error, the researchers tuned down the AI’s activations on Bible verses and September 11, which led to the model giving the correct answer when prompted again on whether 9.11 is larger than 9.8.

There are also other potential applications. Currently, a system-level prompt is built into LLMs to deal with situations like users who ask how to build a bomb. When you ask ChatGPT a question, the model is first secretly prompted by OpenAI to refrain from telling you how to make bombs or do other nefarious things. But it’s easy for users to jailbreak AI models with clever prompts, bypassing any restrictions.

If the creators of the models are able to see where in an AI the bomb-building knowledge is, they can theoretically turn off those nodes permanently. Then even the most cleverly written prompt wouldn’t elicit an answer about how to build a bomb, because the AI would literally have no information about how to build a bomb in its system.

This type of granularity and precise control are easy to imagine but extremely hard to achieve with the current state of mechanistic interpretability.

“A limitation is the steering [influencing a model by adjusting its parameters] is just not working that well, and so when you steer to reduce violence in a model, it ends up completely lobotomizing its knowledge in martial arts. There’s a lot of refinement to be done in steering,” says Lin. The knowledge of “bomb making,” for example, isn’t just a simple on-and-off switch in an AI model. It most likely is woven into multiple parts of the model, and turning it off would probably involve hampering the AI’s knowledge of chemistry. Any tinkering may have benefits but also significant trade-offs.

That said, if we are able to dig deeper and peer more clearly into the “mind” of AI, DeepMind and others are hopeful that mechanistic interpretability could represent a plausible path to alignment—the process of making sure AI is actually doing what we want it to do.

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The complexity of biology has long been a double-edged sword for scientific and medical progress. On one hand, the intricacy of systems (like the human immune response) offers countless opportunities for breakthroughs in medicine and healthcare. On the other hand, that very complexity has often stymied researchers, leaving some of the most significant medical challenges—like cancer or autoimmune diseases—without clear solutions.

The field needs a way to decipher this incredible complexity. Could the rise of agentic AI, artificial intelligence capable of autonomous decision-making and action, be the key to breaking through this impasse?

Agentic AI is not just another tool in the scientific toolkit but a paradigm shift: by allowing autonomous systems to not only collect and process data but also to independently hypothesize, experiment, and even make decisions, agentic AI could fundamentally change how we approach biology.

The mindboggling complexity of biological systemsTo understand why agentic AI holds so much promise, we first need to grapple with the scale of the challenge. Biological systems, particularly human ones, are incredibly complex—layered, dynamic, and interdependent. Take the immune system, for example. It simultaneously operates across multiple levels, from individual molecules to entire organs, adapting and responding to internal and external stimuli in real-time.

Traditional research approaches, while powerful, struggle to account for this vast complexity. The problem lies in the sheer volume and interconnectedness of biological data. The immune system alone involves interactions between millions of cells, proteins, and signaling pathways, each influencing the other in real time. Making sense of this tangled web is almost insurmountable for human researchers.

Enter AI agents: How can they help?This is where agentic AI steps in. Unlike traditional machine learning models, which require vast amounts of curated data and are typically designed to perform specific, narrow tasks, agentic AI systems can ingest unstructured and diverse datasets from multiple sources and can operate autonomously with a more generalist approach.

Beyond this, AI agents are unbound by conventional scientific thinking. They can connect disparate domains and test seemingly improbable hypotheses that may reveal novel insights. What might initially appear as a counterintuitive series of experiments could help uncover hidden patterns or mechanisms, generating new knowledge that can form the foundation for breakthroughs in areas like drug discovery, immunology, or precision medicine.

These experiments are executed at unprecedented speed and scale through robotic, fully automated laboratories, where AI agents conduct trials in a continuous, round-the-clock workflow. These labs, equipped with advanced automation technologies, can handle everything from ordering reagents, preparing biological samples, to conducting high-throughput screenings. In particular, the use of patient-derived organoids—3D miniaturized versions of organs and tissues—enables AI-driven experiments to more closely mimic the real-world conditions of human biology. This integration of agentic AI and robotic labs allows for large-scale exploration of complex biological systems, and has the potential to rapidly accelerate the pace of discovery.

From agentic AI to AGIAs agentic AI systems become more sophisticated, some researchers believe they could pave the way for artificial general intelligence (AGI) in biology. While AGI—machines with the capacity for general intelligence equivalent to humans—remains a distant goal in the broader AI community, biology may be one of the first fields to approach this threshold.

Why? Because understanding biological systems demands exactly the kind of flexible, goal-directed thinking that defines AGI. Biology is full of uncertainty, dynamic systems, and open-ended problems. If we build AI that can autonomously navigate this space—making decisions, learning from failure, and proposing innovative solutions—we might be building AGI specifically tailored to the life sciences.

Owkin’s next frontier: Unlocking the immune system with agentic AIAgentic AI has already begun pushing the boundaries of what’s possible in biology, but the next frontier lies in fully decoding one of the most complex and crucial systems in human health: the immune system. Owkin is building the foundations for an advanced form of intelligence—an AGI—capable of understanding the immune system in unprecedented detail. The next evolution of our AI ecosystem, called Owkin K, could redefine how we understand, detect, and treat immune-related diseases like cancer and immuno-inflammatory disorders.

Owkin K envisions a coordinated community of specialized AI agents that can autonomously access and interpret comprehensive scientific literature, large-scale biomedical data, and tap into the power of Owkin’s discovery engines. These agents are capable of planning and executing experiments in fully automated, robotized wet labs, where patient-derived organoids simulate real-world human biology. The results of these experiments feed back into the system, enabling continuous learning and refinement of the AI agents’ models.

What makes Owkin K particularly exciting is its potential to tackle the immune system—a biological network so complex that human intelligence alone has struggled to unravel it. By deploying AI agents with the ability to explore this intricate web autonomously, the project could reveal new therapeutic targets and strategies for immuno-oncology and autoimmune diseases, potentially accelerating the development of groundbreaking treatments.

Navigating challenges and ethical considerations of agentic AIOf course, such powerful technology comes with significant challenges and ethical considerations, including trust, security, and transparency.

But we must tackle these challenges as agentic AI becomes more integrated into healthcare and research. For example, we can develop mitigation plans that include rigorous validation protocols, real-time human oversight, and regulatory frameworks designed to ensure safety, accountability, and transparency. By prioritizing ethical design and close collaboration between AI systems and human experts, we can harness the potential of agentic AI while minimizing its risks.

The future of biological research with agentic AIAgentic AI has the potential to reshape not just healthcare, but the very foundations of biological research. By allowing autonomous systems to explore the unknown, we may unlock new levels of understanding in areas like immunology, neuroscience, and genomics—fields that are currently constrained by the limits of human comprehension.

We could soon see a world where AI-driven labs operate around the clock, pushing the boundaries of biology at speeds and scales that far exceed human capabilities. This would not only accelerate scientific discovery but also create new possibilities for personalized medicine, disease prevention, and even longevity.

In the end, agentic AI may be more than just another tool for researchers. It could be the key to understanding life itself—one autonomous decision at a time.

Davide Mantiero, PhD, Eric Durand, PhD, and Darius Meadon also contributed to this article.

This content was produced by Owkin. It was not written by MIT Technology Review’s editorial staff.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The AI lab waging a guerrilla war over exploitative AI

Back in 2022, the tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI’s DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados.

But artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work.

Ben Zhao, a computer security researcher at the University of Chicago, was listening. He and his colleagues have built arguably the most prominent weapons in an artist’s arsenal against nonconsensual AI scraping: two tools called Glaze and Nightshade that add barely perceptible perturbations to an image’s pixels so that machine-learning models cannot read them properly.

But Zhao sees the tools as part of a battle to slowly tilt the balance of power from large corporations back to individual creators. Read the full story.

—Melissa Heikkilä

Have we entered the golden age of plant engineering?

In the 1960s, biologists’ selective breeding of plants helped spark a period of transformative agricultural innovation known as the Green Revolution. By the 1990s, the yields of wheat and rice had doubled worldwide, staving off bouts of recurring famine.

The Green Revolution was so successful that dire predictions of worse famine to come—fueled by alarming population growth—no longer seemed likely. But it had its limits—only so much yield could be coaxed from plants using conventional breeding techniques.

Now, more precise gene-editing technologies could shave years off the time it takes for new plant varieties to make it from the lab to federally approved seed products. Read the full story.

—Bill Gourgey

This piece is from the latest print issue of MIT Technology Review, which is all about the weird and wonderful world of food. If you don’t already, subscribe to receive future copies once they land.

MIT Technology Review Narrated: Is robotics about to have its own ChatGPT moment?

Robots that can do many of the things humans do in the home have been a dream of robotics research since the inception of the field in the 1950s.

While engineers have made great progress in getting robots to work in tightly controlled environments like labs and factories, the home has proved difficult to design for. But now, the field is at an inflection point. A new generation of researchers believes that generative AI could give robots the ability to learn new skills and adapt to new environments faster than ever before. This new approach, just maybe, can finally bring robots out of the factory and into the mainstream.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which
we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Donald Trump wants Elon Musk to maximize government efficiency
Despite claiming to be a department, technically it’s more of an advisory board. (Wired $)
+ It will allegedly operate outside of the federal government. (WSJ $)
+ Expect Musk to treat the US government like his loss-making social network. (Bloomberg $)

2 The crypto industry has already started lobbying Trump
Executives are wasting no time in presenting the President-elect with their wish lists. (NYT $)
+ We’re witnessing the industry’s nascent attempts to make itself institutional. (NY Mag $)
+ The Trump Pump is showing no signs of slowing. (CNN)

3 Advertisers are considering staging a return to X
In a bid to curry favor with Musk and his political leverage. (FT $)
+ Silicon Valley is decidedly more Trump-friendly than it used to be. (Insider $)
+ Bluesky is starting to look more and more appealing. (Slate $)

4 Major AI players are struggling to make new breakthroughs
Funneling money into new products isn’t having the desired result. (Bloomberg $)

5 The world’s e-waste is actually pretty valuable
There’s a lot of gold to be stripped out from those old circuit boards. (Economist $) + AI will add to the e-waste problem. Here’s what we can do about it. (MIT Technology Review)

6 DNA testing is ushering in a new age of discriminationAnd you could be denied medical or life insurance because of it. (The Atlantic $)
+ How to… delete your 23andMe data. (MIT Technology Review)

7 How to build the perfect humanoid robotUnfortunately, they’ll be found in factories and warehouses before they make it to our homes. (IEEE Spectrum)
+ A skeptic’s guide to humanoid-robot videos. (MIT Technology Review)

8 The US is using AI to seek out critical mineralsAccess to regular supplies could lessen its reliance on China and Russia. (Undark Magazine)
+ The race to produce rare earth elements. (MIT Technology Review)

9 Apple’s AirTags can now share their location with airlines
Which should (hopefully) minimize the chances of losing your luggage. (WP $)
+ Its next device? An AI wall-mounted tablet, supposedly. (Bloomberg $)

10 This new mathematics benchmark is being kept secret
To prevent AI models from training against it. (Ars Technica)
+ This AI system makes human tutors better at teaching children math. (MIT Technology Review)

Quote of the day

“Don’t bring a watermark to a gunfight.”

—AI researcher Oren Etzioni warns the industry to avoid putting too much faith in voluntary standards to actively prevent malicious actors from gaming the system, TechCrunch reports.

The big story

The great AI consciousness conundrum

October 2023

AI consciousness isn’t just a devilishly tricky intellectual puzzle; it’s a morally weighty problem with potentially dire consequences that philosophers, cognitive scientists, and engineers alike are currently grappling with.

Fail to identify a conscious AI, and you might unintentionally subjugate a being whose interests ought to matter. Mistake an unconscious AI for a conscious one, and you risk compromising human safety and happiness for the sake of an unthinking, unfeeling hunk of silicon and code.

Over the past few decades, a small research community has doggedly attacked the question of what consciousness is and how it works. The effort has yielded real progress. And now, with the rapid advance of AI technology, these insights could offer our only guide to the untested, morally fraught waters of artificial consciousness. Read the full story.

—Grace Huckins

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Small changes can improve your life, from debobbling your clothes to oiling your keyholes.
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Ben Zhao remembers well the moment he officially jumped into the fight between artists and generative AI: when one artist asked for AI bananas.

A computer security researcher at the University of Chicago, Zhao had made a name for himself by building tools to protect images from facial recognition technology. It was this work that caught the attention of Kim Van Deun, a fantasy illustrator who invited him to a Zoom call in November 2022 hosted by the Concept Art Association, an advocacy organization for artists working in commercial media.

On the call, artists shared details of how they had been hurt by the generative AI boom, which was then brand new. At that moment, AI was suddenly everywhere. The tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI’s DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados.

But these artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work. Some had found that their art had been scraped off the internet and used to train the models, while others had discovered that their own names had become prompts, causing their work to be drowned out online by AI knockoffs.

Zhao remembers being shocked by what he heard. “People are literally telling you they’re losing their livelihoods,” he told me one afternoon this spring, sitting in his Chicago living room. “That’s something that you just can’t ignore.”

So on the Zoom, he made a proposal: What if, hypothetically, it was possible to build a mechanism that would help mask their art to interfere with AI scraping?

“I would love a tool that if someone wrote my name and made a prompt, like, garbage came out,” responded Karla Ortiz, a prominent digital artist. “Just, like, bananas or some weird stuff.”

That was all the convincing Zhao needed—the moment he joined the cause.

Fast-forward to today, and millions of artists have deployed two tools born from that Zoom: Glaze and Nightshade, which were developed by Zhao and the University of Chicago’s SAND Lab (an acronym for “security, algorithms, networking, and data”).

Arguably the most prominent weapons in an artist’s arsenal against nonconsensual AI scraping, Glaze and Nightshade work in similar ways: by adding what the researchers call “barely perceptible” perturbations to an image’s pixels so that machine-learning models cannot read them properly. Glaze, which has been downloaded more than 4 million times since it launched in March 2023, adds what’s effectively a secret cloak to images that prevents AI algorithms from picking up on and copying an artist’s style. Nightshade, which I wrote about when it was released almost exactly a year ago this fall, cranks up the offensive against AI companies by adding an invisible layer of poison to images, which can break AI models; it has been downloaded more than 1 million times.

Thanks to the tools, “I’m able to post my work online,” Ortiz says, “and that’s pretty huge.” For artists like her, being seen online is crucial to getting more work. If they are uncomfortable about ending up in a massive for-profit AI model without compensation, the only option is to delete their work from the internet. That would mean career suicide. “It’s really dire for us,” adds Ortiz, who has become one of the most vocal advocates for fellow artists and is part of a class action lawsuit against AI companies, including Stability AI, over copyright infringement.

But Zhao hopes that the tools will do more than empower individual artists. Glaze and Nightshade are part of what he sees as a battle to slowly tilt the balance of power from large corporations back to individual creators.

“It is just incredibly frustrating to see human life be valued so little,” he says with a disdain that I’ve come to see as pretty typical for him, particularly when he’s talking about Big Tech. “And to see that repeated over and over, this prioritization of profit over humanity … it is just incredibly frustrating and maddening.”

As the tools are adopted more widely, his lofty goal is being put to the test. Can Glaze and Nightshade make genuine security accessible for creators—or will they inadvertently lull artists into believing their work is safe, even as the tools themselves become targets for haters and hackers? While experts largely agree that the approach is effective and Nightshade could prove to be powerful poison, other researchers claim they’ve already poked holes in the protections offered by Glaze and that trusting these tools is risky.

But Neil Turkewitz, a copyright lawyer who used to work at theRecording Industry Association of America, offers a more sweeping view of the fight the SAND Lab has joined. It’s not about a single AI company or a single individual, he says: “It’s about defining the rules of the world we want to inhabit.”

Poking the bearThe SAND Lab is tight knit, encompassing a dozen or so researchers crammed into a corner of the University of Chicago’s computer science building. That space has accumulated somewhat typical workplace detritus—a Meta Quest headset here, silly photos of dress-up from Halloween parties there. But the walls are also covered in original art pieces, including a framed painting by Ortiz.

Years before fighting alongside artists like Ortiz against “AI bros” (to use Zhao’s words), Zhao and the lab’s co-leader, Heather Zheng, who is also his wife, had built a record of combating harms posed by new tech.

When I visited the SAND Lab in Chicago, I saw how tight knit the group was. Alongside the typical workplace stuff were funny Halloween photos like this one. (Front row: Ronik Bhaskar, Josephine Passananti, Anna YJ Ha, Zhuolin Yang, Ben Zhao, Heather Zheng. Back row: Cathy Yuanchen Li, Wenxin Ding, Stanley Wu, and Shawn Shan.)COURTESY OF SAND LABThough both earned spots on MIT Technology Review’s 35 Innovators Under 35 list for other work nearly two decades ago, when they were at the University of California, Santa Barbara (Zheng in 2005 for “cognitive radios” and Zhao a year later for peer-to-peer networks), their primary research focus has become security and privacy.

The pair left Santa Barbara in 2017, after they were poached by the new co-director of the University of Chicago’s Data Science Institute, Michael Franklin. All eight PhD students from their UC Santa Barbara lab decided to follow them to Chicago too. Since then, the group has developed a “bracelet of silence” that jams the microphones in AI voice assistants like the Amazon Echo. It has also created a tool called Fawkes—“privacy armor,” as Zhao put it in a 2020 interview with the New York Times—that people can apply to their photos to protect them from facial recognition software. They’ve also studied how hackers might steal sensitive information through stealth attacks on virtual-reality headsets, and how to distinguish human art from AI-generated images.

“Ben and Heather and their group are kind of unique because they’re actually trying to build technology that hits right at some key questions about AI and how it is used,” Franklin tells me. “They’re doing it not just by asking those questions, but by actually building technology that forces those questions to the forefront.”

It was Fawkes that intrigued Van Deun, the fantasy illustrator, two years ago; she hoped something similar might work as protection against generative AI, which is why she extended that fateful invite to the Concept Art Association’s Zoom call.

That call started something of a mad rush in the weeks that followed. Though Zhao and Zheng collaborate on all the lab’s projects, they each lead individual initiatives; Zhao took on what would become Glaze, with PhD student Shawn Shan (who was on this year’s Innovators Under 35 list) spearheading the development of the program’s algorithm.

In parallel to Shan’s coding, PhD students Jenna Cryan and Emily Wenger sought to learn more about the views and needs of the artists themselves. They created a user survey that the team distributed to artists with the help of Ortiz. In replies from more than 1,200 artists—far more than the average number of responses to user studies in computer science—the team found that the vast majority of creators had read about art being used to train models, and 97% expected AI to decrease some artists’ job security. A quarter said AI art had already affected their jobs.

Almost all artists also said they posted their work online, and more than half said they anticipated reducing or removing that online work, if they hadn’t already—no matter the professional and financial consequences.

The first scrappy version of Glaze was developed in just a month, at which point Ortiz gave the team her entire catalogue of work to test the model on. At the most basic level, Glaze acts as a defensive shield. Its algorithm identifies features from the image that make up an artist’s individual style and adds subtle changes to them. When an AI model is trained on images protected with Glaze, the model will not be able to reproduce styles similar to the original image.

A painting from Ortiz later became the first image publicly released with Glaze on it: a young woman, surrounded by flying eagles, holding up a wreath. Its title is Musa Victoriosa, “victorious muse.”

It’s the one currently hanging on the SAND Lab’s walls.

  View this post on Instagram      A post shared by Karla Ortiz (@kortizart)

Despite many artists’ initial enthusiasm, Zhao says, Glaze’s launch caused significant backlash. Some artists were skeptical because they were worried this was a scam or yet another data-harvesting campaign.

The lab had to take several steps to build trust, such as offering the option to download the Glaze app so that it adds the protective layer offline, which meant no data was being transferred anywhere. (The images are then shielded when artists upload them.)

Soon after Glaze’s launch, Shan also led the development of the second tool, Nightshade. Where Glaze is a defensive mechanism, Nightshade was designed to act as an offensive deterrent to nonconsensual training. It works by changing the pixels of images in ways that are not noticeable to the human eye but manipulate machine-learning models so they interpret the image as something different from what it actually shows. If poisoned samples are scraped into AI training sets, these samples trick the AI models: Dogs become cats, handbags become toasters. The researchers say only a relatively few examples are enough to permanently damage the way a generative AI model produces images.

Currently, both tools are available as free apps or can be applied through the project’s website. The lab has also recently expanded its reach by offering integration with the new artist-supported social network Cara, which was born out of a backlash to exploitative AI training and forbids AI-produced content.

In dozens of conversations with Zhao and the lab’s researchers, as well as a handful of their artist-collaborators, it’s become clear that both groups now feel they are aligned in one mission. “I never expected to become friends with scientists in Chicago,” says Eva Toorenent, a Dutch artist who worked closely with the team on Nightshade. “I’m just so happy to have met these people during this collective battle.”

Images online of Toorenent’s Belladonna have been treated with the SAND Lab’s Nightshade tool.EVA TOORENENTHer painting Belladonna, which is also another name for the nightshade plant, was the first image with Nightshade’s poison on it.

“It’s so symbolic,” she says. “People taking our work without our consent, and then taking our work without consent can ruin their models. It’s just poetic justice.”

No perfect solutionThe reception of the SAND Lab’s work has been less harmonious across the AI community.

After Glaze was made available to the public, Zhao tells me, someone reported it to sites like VirusTotal, which tracks malware, so that it was flagged by antivirus programs. Several people also started claiming on social media that the tool had quickly been broken. Nightshade similarly got a fair share of criticism when it launched; as TechCrunch reported in January, some called it a “virus” and, as the story explains, “another Reddit user who inadvertently went viral on X questioned Nightshade’s legality, comparing it to ‘hacking a vulnerable computer system to disrupt its operation.’”

“We had no idea what we were up against,” Zhao tells me. “Not knowing who or what the other side could be meant that every single new buzzing of the phone meant that maybe someone did break Glaze.”

Both tools, though, have gone through rigorous academic peer review and have won recognition from the computer security community. Nightshade was accepted at the IEEE Symposium on Security and Privacy, and Glaze received a distinguished paper award and the 2023 Internet Defense Prize at the Usenix Security Symposium, a top conference in the field.

“In my experience working with poison, I think [Nightshade is] pretty effective,” says Nathalie Baracaldo, who leads the AI security and privacy solutions team at IBM and has studied data poisoning. “I have not seen anything yet—and the word yet is important here—that breaks that type of defense that Ben is proposing.” And the fact that the team has released the source code for Nightshade for others to probe, and it hasn’t been broken, also suggests it’s quite secure, she adds.

At the same time, at least one team of researchers does claim to have penetrated the protections of Glaze, or at least an old version of it.

As researchers from Google DeepMind and ETH Zurich detailed in a paper published in June, they found various ways Glaze (as well as similar but less popular protection tools, such as Mist and Anti-DreamBooth) could be circumvented using off-the-shelf techniques that anyone could access—such as image upscaling, meaning filling in pixels to increase the resolution of an image as it’s enlarged. The researchers write that their work shows the “brittleness of existing protections” and warn that “artists may believe they are effective. But our experiments show they are not.”

Florian Tramèr, an associate professor at ETH Zurich who was part of the study, acknowledges that it is “very hard to come up with a strong technical solution that ends up really making a difference here.” Rather than any individual tool, he ultimately advocates for an almost certainly unrealistic ideal: stronger policies and laws to help create an environment in which people commit to buying only human-created art.

What happened here is common in security research, notes Baracaldo: A defense is proposed, an adversary breaks it, and—ideally—the defender learns from the adversary and makes the defense better. “It’s important to have both ethical attackers and defenders working together to make our AI systems safer,” she says, adding that “ideally, all defenses should be publicly available for scrutiny,” which would both “allow for transparency” and help avoid creating a false sense of security. (Zhao, though, tells me the researchers have no intention to release Glaze’s source code.)

Still, even as all these researchers claim to support artists and their art, such tests hit a nerve for Zhao. In Discord chats that were later leaked, he claimed that one of the researchers from the ETH Zurich–Google DeepMind team “doesn’t give a shit” about people. (That researcher did not respond to a request for comment, but in a blog post he said it was important to break defenses in order to know how to fix them. Zhao says his words were taken out of context.)

Zhao also emphasizes to me that the paper’s authors mainly evaluated an earlier version of Glaze; he says its new update is more resistant to tampering. Messing with images that have current Glaze protections would harm the very style that is being copied, he says, making such an attack useless.

This back-and-forth reflects a significant tension in the computer security community and, more broadly, the often adversarial relationship between different groups in AI. Is it wrong to give people the feeling of security when the protections you’ve offered might break? Or is it better to have some level of protection—one that raises the threshold for an attacker to inflict harm—than nothing at all?

Yves-Alexandre de Montjoye, an associate professor of applied mathematics and computer science at Imperial College London, says there are plenty of examples where similar technical protections have failed to be bulletproof. For example, in 2023, de Montjoye and his team probed a digital mask for facial recognition algorithms, which was meant to protect the privacy of medical patients’ facial images; they were able to break the protections by tweaking just one thing in the program’s algorithm (which was open source).

Using such defenses is still sending a message, he says, and adding some friction to data profiling. “Tools such as TrackMeNot”—which protects users from data profiling—“have been presented as a way to protest; as a way to say I do not consent.”

“But at the same time,” he argues, “we need to be very clear with artists that it is removable and might not protect against future algorithms.”

While Zhao will admit that the researchers pointed out some of Glaze’s weak spots, he unsurprisingly remains confident that Glaze and Nightshade are worth deploying, given that “security tools are never perfect.” Indeed, as Baracaldo points out, the Google DeepMind and ETH Zurich researchers showed how a highly motivated and sophisticated adversary will almost certainly always find a way in.

Yet it is “simplistic to think that if you have a real security problem in the wild and you’re trying to design a protection tool, the answer should be it either works perfectly or don’t deploy it,” Zhao says, citing spam filters and firewalls as examples. Defense is a constant cat-and-mouse game. And he believes most artists are savvy enough to understand the risk.

Offering hopeThe fight between creators and AI companies is fierce. The current paradigm in AI is to build bigger and bigger models, and there is, at least currently, no getting around the fact that they require vast data sets hoovered from the internet to train on. Tech companies argue that anything on the public internet is fair game, and that it is “impossible” to build advanced AI tools without copyrighted material; many artists argue that tech companies have stolen their intellectual propertyand violated copyright law,and that they need ways to keep their individual works out of the models—or at least receive proper credit and compensation for their use.

So far, the creatives aren’t exactly winning. A number of companies have already replaced designers, copywriters, and illustrators with AI systems. In one high-profile case, Marvel Studios used AI-generated imagery instead of human-created art in the title sequence of its 2023 TV series Secret Invasion. In another, a radio station fired its human presenters and replaced them with AI. The technology has become a major bone of contention between unions and film, TV, and creative studios, most recently leading to a strike by video-game performers. There are numerous ongoing lawsuits by artists, writers, publishers, and record labels against AI companies. It will likely take years until there is a clear-cut legal resolution. But even a court ruling won’t necessarily untangle the difficult ethical questions created by generative AI.Any future government regulation is not likely to either, if it ever materializes.

That’s why Zhao and Zheng see Glaze and Nightshade as necessary interventions—tools to defend original work, attack those who would help themselves to it, and, at the very least, buy artists some time. Having a perfect solution is not really the point. The researchers need to offer something now because the AI sector moves at breakneck speed, Zheng says, means that companies are ignoring very real harms to humans. “This is probably the first time in our entire technology careers that we actually see this much conflict,” she adds.

On a much grander scale, she and Zhao tell me they hope that Glaze and Nightshade will eventually have the power to overhaul how AI companies use art and how their products produce it. It is eye-wateringly expensive to train AI models, and it’s extremely laborious for engineers to find and purge poisoned samples in a data set of billions of images. Theoretically, if there are enough Nightshaded images on the internet and tech companies see their models breaking as a result, it could push developers to the negotiating table to bargain over licensing and fair compensation.

That’s, of course, still a big “if.” MIT Technology Review reached out to several AI companies, such as Midjourney and Stability AI, which did not reply to requests for comment. A spokesperson for OpenAI, meanwhile, did not confirm any details about encountering data poison but said the company takes the safety of its products seriously and is continually improving its safety measures: “We are always working on how we can make our systems more robust against this type of abuse.”

In the meantime, the SAND Lab is moving ahead and looking into funding from foundations and nonprofits to keep the project going. They also say there has also been interest from major companies looking to protect their intellectual property (though they decline to say which), and Zhao and Zheng are exploring how the tools could be applied in other industries, such as gaming, videos, or music. In the meantime, they plan to keep updating Glaze and Nightshade to be as robust as possible, working closely with the students in the Chicago lab—where, on another wall, hangs Toorenent’s Belladonna. The painting has a heart-shaped note stuck to the bottom right corner: “Thank you! You have given hope to us artists.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Generative AI taught a robot dog to scramble around a new environment

Teaching robots to navigate new environments is tough. You can train them on physical, real-world data taken from recordings made by humans, but that’s scarce, and expensive to collect. Digital simulations are a rapid, scalable way to teach them to do new things, but the robots often fail when they’re pulled out of virtual worlds and asked to do the same tasks in the real one.

Now, there’s potentially a better option: a new system that uses generative AI models in conjunction with a physics simulator to develop virtual training grounds that more accurately mirror the physical world. Robots trained using this method worked with a higher success rate than those trained using more traditional techniques during real-world tests.

Researchers used the system, called LucidSim, to train a robot dog in parkour, getting it to scramble over a box and climb stairs, despite never seeing any real world data. The approach demonstrates how helpful generative AI could be when it comes to teaching robots to do challenging tasks. It also raises the possibility that we could ultimately train them in entirely virtual worlds. Read the full story.

—Rhiannon Williams

Africa’s AI researchers are ready for takeoff

When we talk about the global race for AI dominance, the conversation often focuses on tensions between the US and China, and European efforts at regulating the technology. But it’s high time we talk about another player: Africa.

African AI researchers are forging their own path, developing tools that answer the needs of Africans, in their own languages. Their story is not only one of persistence and innovation, but of preserving cultures and fighting to shape how AI technologies are used on their own continent. However, they face many barriers. Read the full story.

—Melissa Heikkilä

This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How Silicon Valley is planning to work with Donald Trump
Avoiding antitrust regulation and boosting growth are at the top of Big Tech’s agenda. (WP $)
+ Tech executives overwhelmingly supported Kamala Harris. (Vox)
+ Trump’s policies could make it harder to hire and retain overseas talent. (Insider $)
+ Immigrant tech workers are rushing to secure visas before Trump’s inauguration. (Forbes $)

2 People are abandoning X following the US election result
Threads and Bluesky are experiencing an influx of new users. (Bloomberg $)
+ Trump loved Twitter during his first Presidency. Will he during his second? (Insider $)

3 The Biden administration plans to back a controversial cybercrime treatyCritics fear it could be abused by authoritarian regimes to pursue dissidents. (Politico)+ The treaty would also make electronic evidence more available to the US. (Bloomberg $)

4 DNA testing firm 23andMe is firing 40% of its workforce
Things aren’t looking good for the embattled company. (WSJ $)
+ The company is axing all its therapy programs, too. (Reuters)
+ How to delete your 23andMe data. (MIT Technology Review)

5 How oil and gas companies are masking their methane emissions
The odorless, colorless gas is notoriously tough to track, but satellites are changing that. (FT $)
+ Even if we reach net zero, parts of the planet will keep getting warmer. (New Scientist $)+ Why methane emissions are still a mystery. (MIT Technology Review)

6 This database tracks license plate cameras across the world
The project, called DeFlock, aims to give drivers the choice to avoid certain routes. (404 Media)

7 Baidu has unveiled its AI-integrated smart glasses
The device can track calorie consumption, among other features. (FT $)
+ Smartglasses are a growing trend in China. (SCMP $)
+ The coolest thing about smart glasses is not the AR. It’s the AI. (MIT Technology Review)

8 Everything we know about Uranus is wrongA brief flyby 40 years ago coincided with a rare spike in solar activity. (NYT $)

9 How Ukraine is rewilding amid the war
Ecologists believe the conflict’s catastrophes can birth environmental gains. (Undark Magazine)
+ Ukraine has a plan for getting Trump onside. (Vox)

10 To find alien life, look to the mountains
Who knows what’s trapped under tectonic plates? (The Atlantic $)

Quote of the day

“I did not say I was uncomfortable talking about it. I said we’re not going to talk about it.”

—Michael Barratt, an astronaut and medical doctor, refuses to elaborate on a medical issue an astronaut experienced during a recent mission, Ars Technica reports.

The big story

Zimbabwe’s climate migration is a sign of what’s to come

December 2021

Julius Mutero has spent his entire adult life farming a three-hectare plot in Zimbabwe, but has harvested virtually nothing in the past six years. He is just one of the 86 million people in sub-Saharan Africa who the World Bank estimates will migrate domestically by 2050 because of climate change.

In Zimbabwe, farmers who have tried to stay put and adapt have found their efforts woefully inadequate in the face of new weather extremes. Droughts have already forced tens of thousands from their homes. But their desperate moves are creating new competition for water in the region, and tensions may soon boil over. Read the full story.

—Andrew Mambondiyani

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)+ Here’s how to make perfect cacio e pepe every time.
+ New York is a wonderful place—even if you’re a native New Yorker, there’s always something new to try for the first time.
+ The 2024 Nature’s Best Photo Awards are full of delights.
+ Good luck to the brave souls skiing in central London.

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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

When we talk about the global race for AI dominance, the conversation often focuses on tensions between the US and China, and European efforts at regulating the technology.

But it’s high time we talked about another player: Africa.

As MIT Technology Review has written before, AI is creating a new colonial world order, where the technology is enriching a small minority of people at the expense of the rest of the world.

African AI researchers are determined to change that. They’re forging their own path, developing tools that answer the needs of Africans, in their own languages.

However, they face many barriers. AI research is eye-wateringly expensive, and African startups and researchers get a fraction as much funding as their Western or Asian counterparts. They have to innovate and rely on open-source resources to do more with less.

Despite that, the African AI story reflects not only persistence and innovation, but a determination to preserve cultures and shape how AI technologies are used on the continent. Read more here from Abdullahi Tsanni, who went to this year’s Deep Learning Indaba, a machine-learning conference held annually in Senegal, to learn about the opportunities and barriers the African AI scene faces.

And then some personal news! This edition will be my last newsletter, and from next week you’ll be in the extremely capable hands of my colleague James O’Donnell. It’s been a delight writing this newsletter for the past two or so years, and I’m so grateful you’ve joined me on this journey covering everything from snowballs of bullshit to Taylor Swift’s deepfakes. I’m not going anywhere, though. I’ll be diving deeper into the AI beat at MIT Technology Review to bring you stories on what’s happening in AI and how the technology is changing us and our societies. Stay tuned for more!

Finally, while I have you, this week we’re running our biggest sale of the year, with 50% off an annual subscription to MIT Technology Review. New subscribers receive a free digital report on generative AI and the future of work. Subscribe here.

Now read the rest of The AlgorithmDeeper LearningWhy AI could eat quantum computing’s lunch

Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics. Those expectations have been especially high in physics and chemistry, where the weird effects of quantum mechanics come into play. In theory, this is where quantum computers could have a huge advantage over conventional machines.

Enter AI: But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all.

Given the pace of recent advances, a growing number of researchers are now asking whether AI could solve a substantial chunk of the most interesting problems in chemistry and materials science before large-scale quantum computers become a reality. Read more from Edd Gent here.

Bits and BytesThe Saudis are planning a $100 billion AI powerhouseSpeaking of the race for AI dominance, this piece looks at how Saudi Arabia wants in on AI action. And it’s putting its money where its mouth is. The country is investing a massive sum to develop a tech hub that it hopes will rival the neighboring United Arab Emirates. (Bloomberg)

AI is making it harder to believe what is real and what is notTwo recent examples show just how influential AI slop can be in warping our sense of reality. In Dublin, crowds gathered in the city center to wait for a Halloween parade to take place. There was no parade planned, but the listing was created by AI and then picked up by social media users and local media. By way of contrast, some social media users dismissed shocking images of the devastating recent floods in Spain as AI-generated, although they were entirely real.

AI companies are getting comfortable offering their technology to the militaryMilitaries around the world have been pouring money into new technologies, including AI. Meta and Anthropic are the latest tech companies to start courting them, joining the likes of Google and OpenAI. (The Washington Post)

OpenAI is shifting its strategy as the improvement in its AI tools slows downThe current paradigm in AI development is to make things bigger to make them better. But OpenAI’s new model, code-named Orion, only performs slightly better than its predecessors. Instead, OpenAI is shifting to improving models after their initial training. (The Information)

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Teaching robots to navigate new environments is tough. You can train them on physical, real-world data taken from recordings made by humans, but that’s scarce and expensive to collect. Digital simulations are a rapid, scalable way to teach them to do new things, but the robots often fail when they’re pulled out of virtual worlds and asked to do the same tasks in the real one.

Now there’s a potentially better option: a new system that uses generative AI modelsin conjunction with a physics simulator to develop virtual training grounds that more accurately mirror the physical world. Robots trained using this method achieved a higher success rate in real-world tests than those trained using more traditional techniques.

Researchers used the system, called LucidSim, to train a robot dog in parkour, getting it to scramble over a box and climb stairs even though it had never seen any real-world data. The approach demonstrates how helpful generative AI could be when it comes to teaching robots to do challenging tasks. It also raises the possibility that we could ultimately train them in entirely virtual worlds. The research was presented at the Conference on Robot Learning (CoRL) last week.

“We’re in the middle of an industrial revolution for robotics,” says Ge Yang, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory, who worked on the project. “This is our attempt at understanding the impact of these [generative AI] models outside of their original intended purposes, with the hope that it will lead us to the next generation of tools and models.”

LucidSim uses a combination of generative AI models to create the visual training data. First the researchers generated thousands of prompts for ChatGPT, getting it to create descriptions of a range of environments that represent the conditions the robot would encounter in the real world, including different types of weather, times of day, and lighting conditions. These included “an ancient alley lined with tea houses and small, quaint shops, each displaying traditional ornaments and calligraphy” and “the sun illuminates a somewhat unkempt lawn dotted with dry patches.”

These descriptions were fed into a system that maps 3D geometry and physics data onto AI-generated images, creating short videos mapping a trajectory for the robot to follow. The robot draws on this information to work out the height, width, and depth of the things it has to navigate—a box or a set of stairs, for example.

The researchers tested LucidSim by instructing a four-legged robot equipped with a webcam to complete several tasks, including locating a traffic cone or soccer ball, climbing over a box, and walking up and down stairs. The robot performed consistently better than when it ran a system trained on traditional simulations. In 20 trials to locate the cone, LucidSim had a 100% success rate, versus 70% for systems trained on standard simulations. Similarly, LucidSim reached the soccer ball in another 20 trials 85% of the time, and just 35% for the other system.

Finally, when the robot was running LucidSim, it successfully completed all 10 stair-climbing trials, compared with just 50% for the other system.

From left: Phillip Isola, Ge Yang, and Alan YuCOURTESY OF MIT CSAILThese results are likely to improve even further in the future if LucidSim draws directly from sophisticated generative video models rather than a rigged-together combination of language, image, and physics models, says Phillip Isola, an associate professor at MIT who worked on the research.

The researchers’ approach to using generative AI is a novel one that will pave the way for more interesting new research, says Mahi Shafiullah, a PhD student at New York University who is using AI models to train robots. He did not work on the project.

“The more interesting direction I see personally is a mix of both real and realistic ‘imagined’ data that can help our current data-hungry methods scale quicker and better,” he says.

The ability to train a robot from scratch purely on AI-generated situations and scenarios is a significant achievement and could extend beyond machines to more generalized AI agents, says Zafeirios Fountas, a senior research scientist at Huawei specializing in brain‑inspired AI.

“The term ‘robots’ here is used very generally; we’re talking about some sort of AI that interacts with the real world,” he says. “I can imagine this being used to control any sort of visual information, from robots and self-driving cars up to controlling your computer screen or smartphone.”

In terms of next steps, the authors are interested in trying to train a humanoid robot using wholly synthetic data—which they acknowledge is an ambitious goal, as bipedal robots are typically less stable than their four-legged counterparts. They’re also turning their attention to another new challenge: using LucidSim to train the kinds of robotic arms that work in factories and kitchens. The tasks they have to perform require a lot more dexterity and physical understanding than running around a landscape.

“To actually pick up a cup of coffee and pour it is a very hard, open problem,” says Isola. “If we could take a simulation that’s been augmented with generative AI to create a lot of diversity and train a very robust agent that can operate in a café, I think that would be very cool.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

What Africa needs to do to become a major AI player

Africa is still early in the process of adopting AI technologies. But researchers say the continent is uniquely hospitable to it for several reasons, including a relatively young and increasingly well-educated population, a rapidly growing ecosystem of AI startups, and lots of potential consumers.

However, ambitious efforts to develop AI tools that answer the needs of Africans face numerous hurdles. The biggest are inadequate funding and poor infrastructure. Limited internet access and a scarcity of domestic data centers also mean that developers might not be able to deploy cutting-edge AI capabilities. Complicating this further is a lack of overarching policies or strategies for harnessing AI’s immense benefits—and regulating its downsides.

Taken together, researchers worry, these issues will hold Africa’s AI sector back and hamper its efforts to pave its own pathway in the global AI race. Read the full story.

—Abdullahi Tsanni

Science and technology stories in the age of Trump

—Mat Honan

I’ve spent most of this year being pretty convinced that Donald Trump would be the 47th president of the United States. Even so, like most people, I was completely surprised by the scope of his victory. This level of victory will certainly provide the political capital to usher in a broad sweep of policy changes.

Some of these changes will be well outside our lane as a publication. But very many of President-elect Trump’s stated policy goals will have direct impacts on science and technology.

So I thought I would share some of my remarks from our edit meeting on Wednesday morning, when we woke up to find out that the world had indeed changed. Read the full story.

This story is from The Debrief, the weekly newsletter from our editor in chief Mat Honan. Sign up to receive it in your inbox every Friday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Canada has recorded its first known bird flu case in a human
Officials are investigating how the teenager was exposed to the virus. (NPR)
+ Canada insists that the risk to the public remains low. (Reuters)
+ Why virologists are getting increasingly nervous about bird flu. (MIT Technology Review)

2 How MAGA became a rallying call for young men
The Republicans’ online strategy tapped into the desires of disillusioned Gen Z men. (WP $)
+ Elon Musk is assembling a list of favorable would-be Trump advisors. (FT $)

3 Trump’s victory is a win for the US defense industry
Palmer Luckey’s Anduril is anticipating a lucrative next four years. (Insider $)
+ Here’s what Luckey has to say about the Pentagon’s future of mixed reality. (MIT Technology Review)
+ Traditional weapons are being given AI upgrades. (Wired $)

4 This year is highly likely to be the hottest on recordThis week’s Cop29 climate summit will thrash out future policies. (The Guardian)
+ A little-understood contributor to the weather? Microplastics. (Wired $)
+ Trump’s win is a tragic loss for climate progress. (MIT Technology Review)

5 Ukraine is scrambling to repair its power stations
Workers are dismantling plants to repair other stations hit by Russian attacks. (WSJ $)
+ Meet the radio-obsessed civilian shaping Ukraine’s drone defense. (MIT Technology Review)

6 We need better ways to evaluate LLMs
Tech giants are coming up with better methods of measuring these systems. (FT $)
+ The improvements in the tech behind ChatGPT appear to be slowing. (The Information $)
+ AI hype is built on high test scores. Those tests are flawed. (MIT Technology Review)

7 FTX is suing crypto exchange BinanceIt claims Sam Bankman-Fried fraudulently transferred close to $1.8 billion to Binance in 2021. (Bloomberg $)
+ Meanwhile, bitcoin is surging to new record heights. (Reuters)

8 What we know about tech and lonelinessWhile there’s little evidence tech directly makes us lonely, there’s a strong correlation between the two. (NYT $)

9 What’s next for space policy in the US
If one person’s interested in the cosmos, it’s Elon Musk. (Ars Technica)

10 Could you save the Earth from a killer asteroid?
It’s a game that’s part strategy, part luck. (New Scientist $)
+ Earth is probably safe from a killer asteroid for 1,000 years. (MIT Technology Review)

Quote of the day

“‘Conflict of interest’ seems rather quaint.”

—Gita Johar, a professor at Columbia Business School, tells the Guardian about Donald Trump and Elon Musk’s openly transactional relationship.

The big story

Quartz, cobalt, and the waste we leave behind

May 2024

It is easy to convince ourselves that we now live in a dematerialized ethereal world, ruled by digital startups, artificial intelligence, and financial services.

Yet there is little evidence that we have decoupled our economy from its churning hunger for resources. We are still reliant on the products of geological processes like coal and quartz, a mineral that’s a rich source of the silicon used to build computer chips, to power our world.

Three recent books aim to reconnect readers with the physical reality that underpins the global economy. Each one fills in dark secrets about the places, processes, and lived realities that make the economy tick, and reveals just how tragic a toll the materials we rely on take for humans and the environment. Read the full story.

—Matthew Ponsford

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)+ Oscars buzz has already begun, and this year’s early contenders are an interesting bunch.
+ This sweet art project shows how toys age with love
+ Who doesn’t love pretzels? Here’s how to make sure they end up with the perfect fluffy interior and a glossy, chewy crust.
+ These images of plankton are really quite something.

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Rather than analyzing the news this week, I thought I’d lift the hood a bit on how we make it.

I’ve spent most of this year being pretty convinced that Donald Trump would be the 47th president of the United States. Even so, like most people, I was completely surprised by the scope of his victory. By taking the lion’s share not just in the Electoral College but also the popular vote, coupled with the wins in the Senate (and, as I write this, seemingly the House) and ongoing control of the courts, Trump has done far more than simply eke out a win. This level of victory will certainly provide the political capital to usher in a broad sweep of policy changes.

Some of these changes will be well outside our lane as a publication. But very many of President-elect Trump’s stated policy goals will have direct impacts on science and technology. Some of the proposed changes would have profound effects on the industries and innovations we’ve covered regularly, and for years. When he talks about his intention to end EV subsidies, hit the brakes on FTC enforcement actions on Big Tech, ease the rules on crypto, or impose a 60 percent tariff on goods from China, these are squarely in our strike zone and we would be remiss not to explore the policies and their impact in detail.

And so I thought I would share some of my remarks from our edit meeting on Wednesday morning, when we woke up to find out that the world had indeed changed. I think it’s helpful for our audience if we are transparent and upfront about how we intend to operate, especially over the next several months that will likely be, well, chaotic.

This is a moment when our jobs are more important than ever. There will be so much noise and heat out there in the coming weeks and months, and maybe even years. The next six months in particular will be a confusing time for a lot of people. We should strive to be the signal in that noise.

We have extremely important stories to write about the role of science and technology in the new administration. There are obvious stories for us to take on in regards to climate, energy, vaccines, women’s health, IVF, food safety, chips, China, and I’m sure a lot more, that people are going to have all sorts of questions about. Let’s start by making a list of questions we have ourselves. Some of the people and technologies we cover will be ascendant in all sorts of ways. We should interrogate that power. It’s important that we take care in those stories not to be speculative or presumptive. To always have the facts buttoned up. To speak the truth and be unassailable in doing so.

Do we drop everything and only cover this? No. But it will certainly be a massive story that affects nearly all others.

This election will be a transformative moment for society and the world. Trump didn’t just win, he won a mandate. And he’s going to change the country and the global order as a result. The next few weeks will see so much speculation as to what it all means. So much fear, uncertainty, and doubt. There is an enormous amount of bullshit headed down the line. People will be hungry for sources they can trust. We should be there for that. Let’s leverage our credibility, not squander it.

We are not the resistance. We just want to tell the truth. So let’s take a breath, and then go out there and do our jobs.

I like to tell our reporters and editors that our coverage should be free from either hype or cynicism. I think that’s especially true now.

I’m also very interested to hear from our readers: What questions do you have? What are the policy changes or staffing decisions you are curious about? Please drop me a line at mat.honan@technologyreview.com I’m eager to hear from you.

If someone forwarded you this edition of The Debrief, you can subscribe here.

Now read the rest of The DebriefThe News

Palmer Luckey, who was ousted from Facebook over his support for the last Trump administration and went into defense contracting, is poised to grow in influence under a second administration. He recently talked to MIT Technology Review about how the Pentagon is using mixed reality.

• What does Donald Trump’s relationship with Elon Musk mean for the global EV industry?

• The Biden administration was perceived as hostile to crypto. The industry can likely expect friendlier waters under Trump

• Some counter-programming: Life seeking robots could punch through Europa’s icy surface

• And for one more big take that’s not related to the election: AI vs quantum. AI could solve some of the most interesting scientific problems before big quantum computers become a reality

The Chat

Every week I’ll talk to one of MIT Technology Review’s reporters or editors to find out more about what they’ve been working on. This week, I chatted with Melissa Heikkilä about her story on how ChatGPT search paves the way for AI agents.

Mat: Melissa, OpenAI rolled out web search for ChatGPT last week. It seems pretty cool. But you got at a really interesting bigger picture point about it paving the way for agents. What does that mean?

Melissa: Microsoft tried to chip away at Google’s search monopoly with Bing, and that didn’t really work. It’s unlikely OpenAI will be able to make much difference either. Their best bet is try to get users used to a new way of finding information and browsing the web through virtual assistants that can do complex tasks. Tech companies call these agents. ChatGPT’s usefulness is limited by the fact that it can’t access the internet and doesn’t have the most up to date information. By integrating a really powerful search engine into the chatbot, suddenly you have a tool that can help you plan things and find information in a far more comprehensive and immersive way than traditional search, and this is a key feature of the next generation of AI assistants.

Mat: What will agents be able to do?

Melissa: AI agents can complete complex tasks autonomously and the vision is that they will work as a human assistant would — book your flights, reschedule your meetings, help with research, you name it. But I wouldn’t get too excited yet. The cutting-edge of AI tech can retrieve information and generate stuff, but it still lacks the reasoning and long-term planning skills to be really useful. AI tools like ChatGPT and Claude also can’t interact with computer interfaces, like clicking at stuff, very well. They also need to become a lot more reliable and stop making stuff up, which is still a massive problem with AI. So we’re still a long way away from the vision becoming reality! I wrote an explainer on agents a little while ago with more details.

Mat: Is search as we know it going away? Are we just moving to a world of agents that not only answer questions but also accomplish tasks?

Melissa: It’s really hard to say. We are so used to using online search, and it’s surprisingly hard to change people’s behaviors. Unless agents become super reliable and powerful, I don’t think search is going to go away.

Mat: By the way, I know you are in the UK. Did you hear we had an election over here in the US?

Melissa: LOL

The Recommendation

I’m just back from a family vacation in New York City, where I was in town to run the marathon. (I get to point this out for like one or two more weeks before the bragging gets tedious, I think.) While there, we went to see The Outsiders. Chat, it was incredible. (Which maybe should go without saying given that it won the Tony for best musical.) But wow. I loved the book and the movie as a kid. But this hit me on an entirely other level. I’m not really a cries-at-movies (or especially at musicals) kind of person but I was wiping my eyes for much of the second act. So were very many people sitting around me. Anyway. If you’re in New York, or if it comes to your city, go see it. And until then, the soundtrack is pretty amazing on its own. (Here’s a great example.)

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Kessel Okinga-Koumu paced around a crowded hallway. It was her first time presenting at the Deep Learning Indaba, she told the crowd gathered to hear her, filled with researchers from Africa’s machine-learning community. The annual weeklong conference (‘Indaba’ is a Zulu word for gathering), was held most recently in September at Amadou Mahtar Mbow University in Dakar, Senegal. It attracted over 700 attendees to hear about—and debate—the potential of Africa-centric AI and how it’s being deployed in agriculture, education, health care, and other critical sectors of the continent’s economy.

A 28-year-old computer science student at the University of the Western Cape in Cape Town, South Africa, Okinga-Koumu spoke about how she’s tackling a common problem: the lack of lab equipment at her university. Lecturers have long been forced to use chalkboards or printed 2D representations of equipment to simulate practical lessons that need microscopes, centrifuges, or other expensive tools. “In some cases, they even ask students to draw the equipment during practical lessons,” she lamented.

Okinga-Koumu pulled a phone from the pocket of her blue jeans and opened a prototype web app she’s built. Using VR and AI features, the app allows students to simulate using the necessary lab equipment—exploring 3D models of the tools in a real-world setting, like a classroom or lab. “Students could have detailed VR of lab equipment, making their hands-on experience more effective,” she said.

Established in 2017, the Deep Learning Indaba now has chapters in 47 of the 55 African nations and aims to boost AI development across the continent by providing training and resources to African AI researchers like Okinga-Koumu. Africa is still early in the process of adopting AI technologies, but organizers say the continent is uniquely hospitable to it for several reasons, including a relatively young and increasingly well-educated population, a rapidly growing ecosystem of AI startups, and lots of potential consumers.

“The building and ownership of AI solutions tailored to local contexts is crucial for equitable development,” says Shakir Mohamed, a senior research scientist at Google DeepMind and cofounder of the organization sponsoring the conference. Africa, more than other continents in the world, can address specific challenges with AI and will benefit immensely from its young talent, he says: “There is amazing expertise everywhere across the continent.”

However, researchers’ ambitious efforts to develop AI tools that answer the needs of Africans face numerous hurdles. The biggest are inadequate funding and poor infrastructure. Not only is it very expensive to build AI systems, but research to provide AI training data in original African languages has been hamstrung by poor financing of linguistics departments at many African universities and the fact that citizens increasingly don’t speak or write local languages themselves. Limited internet access and a scarcity of domestic data centers also mean that developers might not be able to deploy cutting-edge AI capabilities.

DEEP LEARNING INDABA 2024Complicating this further is a lack of overarching policies or strategies for harnessing AI’s immense benefits—and regulating its downsides. While there are various draft policy documents, researchers are in conflict over a continent-wide strategy. And they disagree about which policies would most benefit Africa, not the wealthy Western governments and corporations that have often funded technological innovation.

Taken together, researchers worry, these issues will hold Africa’s AI sector back and hamper its efforts to pave its own pathway in the global AI race.

On the cusp of changeAfrica’s researchers are already making the most of generative AI’s impressive capabilities. In South Africa, for instance, to help address the HIV epidemic, scientists have designed an app called Your Choice, powered by an LLM-based chatbot that interacts with people to obtain their sexual history without stigma or discrimination. In Kenya, farmers are using AI apps to diagnose diseases in crops and increase productivity. And in Nigeria, Awarri, a newly minted AI startup, is trying to build the country’s first large language model, with the endorsement of the government, so that Nigerian languages can be integrated into AI tools.

The Deep Learning Indaba is another sign of how Africa’s AI research scene is starting to flourish. At the Dakar meeting, researchers presented 150 posters and 62 papers. Of those, 30 will be published in top-tier journals, according to Mohamed.

Meanwhile, an analysis of 1,646 publications in AI between 2013 and 2022 found “a significant increase in publications” from Africa. And Masakhane, a cousin organization to Deep Learning Indaba that pushes for natural-language-processing research in African languages, has released over 400 open-source models and 20 African-language data sets since it was founded in 2018.

“These metrics speak a lot to the capacity building that’s happening,” says Kathleen Siminyu, a computer scientist from Kenya, who researches NLP tools for her native Kiswahili. “We’re starting to see a critical mass of people having basic foundational skills. They then go on to specialize.”

She adds: “It’s like a wave that cannot be stopped.”

Khadija Ba, a Senegalese entrepreneur and investor at the pan-African VC fund P1 Ventures who was at this year’s conference, says that she sees African AI startups as particularly attractive because their local approaches have potential to be scaled for the global market. African startups often build solutions in the absence of robust infrastructure, yet “these innovations work efficiently, making them adaptable to other regions facing similar challenges,” she says.

In recent years, funding in Africa’s tech ecosystem has picked up: VC investment totaled $4.5 billion last year, more than double what it was just five years ago, according to a report by the African Private Capital Association. And this October, Google announced a $5.8 million commitment to support AI training initiatives in Kenya, Nigeria, and South Africa. But researchers say local funding remains sluggish. Take the Google-backed fund rolled out, also in October, in Nigeria, Africa’s most populous country. It will pay out $6,000 each to 10 AI startups—not even enough to purchase the equipment needed to power their systems.

Lilian Wanzare, a lecturer and NLP researcher at Maseno University in Kisumu, Kenya, bridles at African governments’ lackadaisical support for local AI initiatives and complains as well that the government charges exorbitant fees for access to publicly generated data, hindering data sharing and collaboration. “[We] researchers are just blocked,” she says. “The government is saying they’re willing to support us, but the structures have not been put in place for us.”

Language barriers Researchers who want to make Africa-centric AI don’t face just insufficient local investment and inaccessible data. There are major linguistic challenges, too.

During one discussion at the Indaba, Ife Adebara, a Nigerian computational linguist, posed a question: “How many people can write a bachelor’s thesis in their native African language?”

Zero hands went up.

Then the audience disintegrated into laughter.

Africans want AI to speak their local languages, but many Africans cannot speak and write in these languages themselves, Adebara said.

Although Africa accounts for one-third of all languages in the world, many oral languages are slowly disappearing, their population of native speakers declining. And LLMs developed by Western-based tech companies fail to serve African languages; they don’t understand locally relevant context and culture.

For Adebara and others researching NLP tools, the lack of people who have the ability to read and write in African languages poses a major hurdle to development of bespoke AI-enabled technologies. “Without literacy in our local languages, the future of AI in Africa is not as bright as we think,” she says.

On top of all that, there’s little machine-readable data for African languages. One reason is that linguistic departments in public universities are poorly funded, Adebara says, limiting linguists’ participation in work that could create such data and benefit AI development.

This year, she and her colleagues established EqualyzAI, a for-profit company seeking to preserve African languages through digital technology. They have built voice tools and AI models, covering about 517 African languages.

Lelapa AI, a software company that’s building data sets and NLP tools for African languages, is also trying to address these language-specific challenges. Its cofounders met in 2017 at the first Deep Learning Indaba and launched the company in 2022. In 2023, it released its first AI tool, Vulavula, a speech-to-text program that recognizes several languages spoken in South Africa.

This year, Lelapa AI released InkubaLM, a first-of-its-kind small language model that currently supports a range of African languages: IsiXhosa, Yoruba, Swahili, IsiZulu, and Hausa. InkubaLM can answer questions and perform tasks like English translation and sentiment analysis. In tests, it performed as well as some larger models. But it’s still in early stages. The hope is that InkubaLM will someday power Vulavula, says Jade Abbott, cofounder and chief operating officer of Lelapa AI.

“It’s the first iteration of us really expressing our long-term vision of what we want, and where we see African AI in the future,” Abbott says. “What we’re really building is a small language model that punches above its weight.”

InkubaLM is trained on two open-source data sets with 1.9 billion tokens, built and curated by Masakhane and other African developers who worked with real people in local communities. They paid native speakers of languages to attend writing workshops to create data for their model.

Fundamentally, this approach will always be better, says Wanzare, because it’s informed by people who represent the language and culture.

A clash over strategyAnother issue that came up again and again at the Indaba was that Africa’s AI scene lacks the sort of regulation and support from governments that you find elsewhere in the world—in Europe, the US, China, and, increasingly, the Middle East.

Of the 55 African nations, only seven—Senegal, Egypt, Mauritius, Rwanda, Algeria, Nigeria, and Benin—have developed their own formal AI strategies. And many of those are still in the early stages.

A major point of tension at the Indaba, though, was the regulatory framework that will govern the approach to AI across the entire continent. In March, the African Union Development Agency published a white paper, developed over a three-year period, that lays out this strategy. The 200-page document includes recommendations for industry codes and practices, standards to assess and benchmark AI systems, and a blueprint of AI regulations for African nations to adopt. The hope is that it will be endorsed by the heads of African governments in February 2025 and eventually passed by the African Union.

But in July, the African Union Commission in Addis Ababa, Ethiopia, another African governing body that wields more power than the development agency, released a rival continental AI strategy—a 66-page document that diverges from the initial white paper.

It’s unclear what’s behind the second strategy, but Seydina Ndiaye, a program director at the Cheikh Hamidou Kane Digital University in Dakar who helped draft the development agency’s white paper, claims it was drafted by a tech lobbyist from Switzerland. The commission’s strategy calls for African Union member states to declare AI a national priority, promote AI startups, and develop regulatory frameworks to address safety and security challenges. But Ndiaye expressed concerns that the document does not reflect the perspectives, aspirations, knowledge, and work of grassroots African AI communities. “It’s a copy-paste of what’s going on outside the continent,” he says.

Vukosi Marivate, a computer scientist at the University of Pretoria in South Africa who helped found the Deep Learning Indaba and is known as an advocate for the African machine-learning movement, expressed fury over this turn of events at the conference. “These are things we shouldn’t accept,” he declared. The room full of data wonks, linguists, and international funders brimmed with frustration. But Marivate encouraged the group to forge ahead with building AI that benefits Africans: “We don’t have to wait for the rules to act right,” he said.

Barbara Glover, a program manager for the African Union Development Agency, acknowledges that AI researchers are angry and frustrated. There’s been a push to harmonize the two continental AI strategies, but she says the process has been fractious: “That engagement didn’t go as envisioned.” Her agency plans to keep its own version of the continental AI strategy, Glover says, adding that it was developed by African experts rather than outsiders. “We are capable, as Africans, of driving our own AI agenda,” she says.

DEEP LEARNING INDABA 2024This all speaks to a broader tension over foreign influence in the African AI scene, one that goes beyond any single strategic document. Mirroring the skepticism toward the African Union Commission strategy, critics say the Deep Learning Indaba is tainted by its reliance on funding from big foreign tech companies; roughly 50% of its $500,000 annual budget comes from international donors and the rest from corporations like Google DeepMind, Apple, Open AI, and Meta. They argue that this cash could pollute the Indaba’s activities and influence the topics and speakers chosen for discussion.

But Mohamed, the Indaba cofounder who is a researcher at Google DeepMind, says that “almost all that goes back to our beneficiaries across the continent,” and the organization helps connect them to training opportunities in tech companies. He says it benefits from some of its cofounders’ ties with these companies but that they do not set the agenda.

Ndiaye says that the funding is necessary to keep the conference going. “But we need to have more African governments involved,” he says.

To Timnit Gebru, founder and executive director at the nonprofit Distributed AI Research Institute (DAIR), which supports equitable AI research in Africa, the angst about foreign funding for AI development comes down to skepticism of exploitative, profit-driven international tech companies. “Africans [need] to do something different and not replicate the same issues we’re fighting against,” Gebru says. She warns about the pressure to adopt “AI for everything in Africa,” adding that there’s “a lot of push from international development organizations” to use AI as an “antidote” for all Africa’s challenges.

Siminyu, who is also a researcher at DAIR, agrees with that view. She hopes that African governments will fund and work with people in Africa to build AI tools that reach underrepresented communities—tools that can be used in positive ways and in a context that works for Africans. “We should be afforded the dignity of having AI tools in a way that others do,” she says.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Why AI could eat quantum computing’s lunch

Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.

But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all. Read the full story.

—Edd Gent

What’s next for reproductive rights in the US

This week, it wasn’t just the future president of the US that was on the ballot. Ten states also voted on abortion rights.

Two years ago, the US Supreme Court overturned Roe v. Wade, a legal decision that protected the right to abortion. Since then, abortion bans have been enacted in multiple states, and millions of people in the US have lost access to local clinics.

Now, some states are voting to extend and protect access to abortion. Missouri, a state that has long restricted access, even voted to overturn its ban. But it’s not all good news for proponents of reproductive rights. Read the full story.

—Jessica Hamzelou

This story is from The Checkup, our weekly newsletter giving you the inside track on all things biotech. Sign up to receive it in your inbox every Thursday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Black Americans received racist texts threatening them with slavery
Some of the messages claim to be from Trump supporters or the Trump administration. (WP $)
+ What Trump’s last tenure as president can teach us about what’s coming. (New Yorker $)
+ The January 6 rioters are hoping for early pardons and release. (Wired $)

2 China is shoring up its economy to the tune of $1.4 trillion
It’s bracing itself for increased trade tensions with a Trump-governed US. (FT $)
+ The country’s chip industry has a plan too. (Reuters)
+ We’re witnessing the return of Trumponomics. (Economist $)
+ Here’s how the tech markets have reacted to his reelection. (Insider $)

3 How crypto came out on topTrump is all in, even if he previously dismissed it as a scam. (Bloomberg $)
+ Enthusiasts are hoping for less regulation and more favorable legislation. (Time $)
4 A weight-loss drug contributed to the death of a nurse in the UK
Susan McGowan took two doses of Mounjaro in the weeks before her death. (BBC)
+ It’s the first known death to be officially linked to the drug in the UK. (The Guardian)

5 An academic’s lawsuit against Meta has been dismissed
Ethan Zuckerman wanted protection against the firm for building an unfollowing tool. (NYT $)

6 How the Republicans won onlineThe right-wing influencer ecosystem is extremely powerful and effective. (The Atlantic $)
+ The left doesn’t really have an equivalent network. (Vox)
+ X users are considering leaving the platform in protest (again.) (Slate $)

7 What does the future of America’s public health look like?Noted conspiracy theorist and anti-vaxxer RFK Jr could be in charge soon. (NY Mag $)
+ Letting Kennedy “go wild on health” is not a great sign. (Forbes $)
+ His war on fluoride in drinking water is already underway. (Politico)

8 An AI-created portrait of Alan Turing has sold for $1 millionJust… why? (The Guardian)
+ Why artists are becoming less scared of AI. (MIT Technology Review)

9 How to harness energy from space
A relay system of transmitters could help to ping it back to Earth. (IEEE Spectrum)
+ The quest to figure out farming on Mars. (MIT Technology Review)

10 AI-generated videos are not interesting
That’s according to the arbiters of what is and isn’t interesting over at Reddit. (404 Media)
+ What’s next for generative video. (MIT Technology Review)

Quote of the day

“That’s petty, right? How much does one piece of fruit per day cost?”

—A former Intel employee reacts to the news the embattled company is planning to restore its free coffee privileges for its staff—but not free fruit, Insider reports.

The big story

Recapturing early internet whimsy with HTML

December 2023

Websites weren’t always slick digital experiences.

There was a time when surfing the web involved opening tabs that played music against your will and sifting through walls of text on a colored background. In the 2000s, before Squarespace and social media, websites were manifestations of individuality—built from scratch using HTML, by users who had some knowledge of code.

Scattered across the web are communities of programmers working to revive this seemingly outdated approach. And the movement is anything but a superficial appeal to retro aesthetics—it’s about celebrating the human touch in digital experiences. Read the full story.

—Tiffany Ng

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Sandwiches through the ages is a pretty great subject for a book.
• Art Garfunkel and Paul Simon are getting the band back together! (kind of)
• Instant mashed potatoes have a bad reputation. But it doesn’t have to be this way.
• Here’s what an actual robot apocalypse would look like (thanks Will!)

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This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Earlier this week, Americans cast their votes in a seminal presidential election. But it wasn’t just the future president of the US that was on the ballot. Ten states also voted on abortion rights.

Two years ago, the US Supreme Court overturned Roe v. Wade, a legal decision that protected the right to abortion. Since then, abortion bans have been enacted in multiple states, and millions of people in the US have lost access to local clinics.

Now, some states are voting to extend and protect access to abortion. This week, seven states voted in support of such measures. And voters in Missouri, a state that has long restricted access, have voted to overturn its ban.

It’s not all good news for proponents of reproductive rights—some states voted against abortion access. And questions remain over the impact of a second term under former president Donald Trump, who is set to return to the post in January.

Roe v. Wade, the legal decision that enshrined a constitutional right to abortion in the US in 1973, guaranteed the right to an abortion up to the point of fetal viability, which is generally considered to be around 24 weeks of pregnancy. It was overturned by the US Supreme Court in the summer of 2022.

Within 100 days of the decision, 13 states had enacted total bans on abortion from the moment of conception. Clinics in these states could no longer offer abortions. Other states also restricted abortion access. In that 100-day period, 66 of the 79 clinics across 15 states stopped offering abortion services, and 26 closed completely, according to research by the Guttmacher Institute.

The political backlash to the decision was intense. This week, abortion was on the ballot in 10 states: Arizona, Colorado, Florida, Maryland, Missouri, Montana, Nebraska, Nevada, New York, and South Dakota. And seven of them voted in support of abortion access.

The impact of these votes will vary by state. Abortion was already legal in Maryland, for example. But the new measures should make it more difficult for lawmakers to restrict reproductive rights in the future. In Arizona, abortions after 15 weeks had been banned since 2022. There, voters approved an amendment to the state constitution that will guarantee access to abortion until fetal viability.

Missouri was the first state to enact an abortion ban once Roe v. Wade was overturned. The state’s current Right to Life of the Unborn Child Act prohibits doctors from performing abortions unless there is a medical emergency. It has no exceptions for rape or incest. This week, the state voted to overturn that ban and protect access to abortion up to fetal viability.

Not all states voted in support of reproductive rights. Amendments to expand access failed to garner enough support in Nebraska, South Dakota, and Florida. In Florida, for example, where abortions after six weeks of pregnancy are banned, an amendment to protect access until fetal viability got 57% of the vote, falling just short of the 60% the state required for it to pass.

It’s hard to predict how reproductive rights will fare over the course of a second Trump term. Trump himself has been inconsistent on the issue. During his first term, he installed members of the Supreme Court who helped overturn Roe v. Wade. During his most recent campaign he said that decisions on reproductive rights should be left to individual states.

Trump, himself a Florida resident, has refused to comment on how he voted in the state’s recent ballot question on abortion rights. When asked, he said that the reporter who posed the question “should just stop talking about that,” according to the Associated Press.

State decisions can affect reproductive rights beyond abortion access. Just look at Alabama. In February, the Alabama Supreme Court ruled that frozen embryos can be considered children under state law. Embryos are routinely cryopreserved in the course of in vitro fertilization treatment, and the ruling was considered likely to significantly restrict access to IVF in the state. (In March, the state passed another law protecting clinics from legal repercussions should they damage or destroy embryos during IVF procedures, but the status of embryos remains unchanged.)

The fertility treatment became a hot topic during this year’s campaign. In October, Trump bizarrely referred to himself as “the father of IVF.” That title is usually reserved for Robert Edwards, the British researcher who won the 2010 Nobel prize in physiology or medicine for developing the technology in the 1970s.

Whatever is in store for reproductive rights in the US in the coming months and years, all we’ve seen so far suggests that it’s likely to be a bumpy ride.

Now read the rest of The CheckupRead more from MIT Technology Review’s archiveMy colleague Rhiannon Williams reported on the immediate aftermath of the decision that reversed Roe v. Wade when it was announced a couple of years ago.

The Alabama Supreme Court ruling on embryos could also affect the development of technologies designed to serve as “artificial wombs,” as Antonio Regalado explained at the time.

Other technologies are set to change the way we have babies. Some, which could lead to the creation of children with four parents or none at all, stand to transform our understanding of parenthood.

We’ve also reported on attempts to create embryo-like structures using stem cells. These structures look like embryos but are created without eggs or sperm. There’s a “wild race” afoot to make these more like the real thing. But both scientific and ethical questions remain over how far we can—and—should go.

My colleagues have been exploring what the US election outcome might mean for climate policies. Senior climate editor James Temple writes that Trump’s victory is “a stunning setback for climate change.” And senior reporter Casey Crownhart explains how efforts including a trio of laws implemented by the Biden administration, which massively increased climate funding, could be undone.

From around the webDonald Trump has said he’ll let Robert F. Kennedy Jr. “go wild on health.” Here’s where the former environmental lawyer and independent candidate—who has no medical or public health degrees—stands on vaccines, fluoride, and the Affordable Care Act. (New York Times)

Bird flu has been detected in pigs on a farm in Oregon. It’s a worrying development that virologists were dreading. (The Conversation)

And, in case you need it, here’s some lighter reading:

Scientists are sequencing the DNA of tiny marine plankton for the first time. (Come for the story of the scientific expedition; stay for the beautiful images of jellies and sea sapphires.) (The Guardian)

Dolphins are known to communicate with whistles and clicks. But scientists were surprised to find a “highly vocal” solitary dolphin in the Baltic Sea. They think the animal is engaging in “dolphin self-talk.” (Bioacoustics)

How much do you know about baby animals? Test your knowledge in this quiz. (National Geographic)

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Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.

Those expectations have been especially high in physics and chemistry, where the weird effects of quantum mechanics come into play. In theory, this is where quantum computers could have a huge advantage over conventional machines.

But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all.

The scale and complexity of quantum systems that can be simulated using AI is advancing rapidly, says Giuseppe Carleo, a professor of computational physics at the Swiss Federal Institute of Technology (EPFL). Last month, he coauthored a paper published in Science showing that neural-network-based approaches are rapidly becoming the leading technique for modeling materials with strong quantum properties. Meta also recently unveiled an AI model trained on a massive new data set of materials that has jumped to the top of a leaderboard for machine-learning approaches to material discovery.

Given the pace of recent advances, a growing number of researchers are now asking whether AI could solve a substantial chunk of the most interesting problems in chemistry and materials science before large-scale quantum computers become a reality.

“The existence of these new contenders in machine learning is a serious hit to the potential applications of quantum computers,” says Carleo “In my opinion, these companies will find out sooner or later that their investments are not justified.”

Exponential problemsThe promise of quantum computers lies in their potential to carry out certain calculations much faster than conventional computers. Realizing this promise will require much larger quantum processors than we have today. The biggest devices have just crossed the thousand-qubit mark, but achieving an undeniable advantage over classical computers will likely require tens of thousands, if not millions. Once that hardware is available, though, a handful of quantum algorithms, like the encryption-cracking Shor’s algorithm, have the potential to solve problems exponentially faster than classical algorithms can.

But for many quantum algorithms with more obvious commercial applications, like searching databases, solving optimization problems, or powering AI, the speed advantage is more modest. And last year, a paper coauthored by Microsoft’s head of quantum computing, Matthias Troyer, showed that these theoretical advantages disappear if you account for the fact that quantum hardware operates orders of magnitude slower than modern computer chips. The difficulty of getting large amounts of classical data in and out of a quantum computer is also a major barrier.

So Troyer and his colleagues concluded that quantum computers should instead focus on problems in chemistry and materials science that require simulation of systems where quantum effects dominate. A computer that operates along the same quantum principles as these systems should, in theory, have a natural advantage here. In fact, this has been a driving idea behind quantum computing ever since the renowned physicist Richard Feynman first proposed the idea.

The rules of quantum mechanics govern many things with huge practical and commercial value, like proteins, drugs, and materials. Their properties are determined by the interactions of their constituent particles, in particular their electrons—and simulating these interactions in a computer should make it possible to predict what kinds of characteristics a molecule will exhibit. This could prove invaluable for discovering things like new medicines or more efficient battery chemistries, for example.

But the intuition-defying rules of quantum mechanics—in particular, the phenomenon of entanglement, which allows the quantum states of distant particles to become intrinsically linked—can make these interactions incredibly complex. Precisely tracking them requires complicated math that gets exponentially tougher the more particles are involved. That can make simulating large quantum systems intractable on classical machines.

This is where quantum computers could shine. Because they also operate on quantum principles, they are able to represent quantum states much more efficiently than is possible on classical machines. They could also take advantage of quantum effects to speed up their calculations.

But not all quantum systems are the same. Their complexity is determined by the extent to which their particles interact, or correlate, with each other. In systems where these interactions are strong, tracking all these relationships can quickly explode the number of calculations required to model the system. But in most that are of practical interest to chemists and materials scientists, correlation is weak, says Carleo. That means their particles don’t affect each other’s behavior significantly, which makes the systems far simpler to model.

The upshot, says Carleo, is that quantum computers are unlikely to provide any advantage for most problems in chemistry and materials science. Classical tools that can accurately model weakly correlated systems already exist, the most prominent being density functional theory (DFT). The insight behind DFT is that all you need to understand a system’s key properties is its electron density, a measure of how its electrons are distributed in space. This makes for much simpler computation but can still provide accurate results for weakly correlated systems.

Simulating large systems using these approaches requires considerable computing power. But in recent years there’s been an explosion of research using DFT to generate data on chemicals, biomolecules, and materials—data that can be used to train neural networks. These AI models learn patterns in the data that allow them to predict what properties a particular chemical structure is likely to have, but they are orders of magnitude cheaper to run than conventional DFT calculations.

This has dramatically expanded the size of systems that can be modeled—to as many as 100,000 atoms at a time—and how long simulations can run, says Alexandre Tkatchenko, a physics professor at the University of Luxembourg. “It’s wonderful. You can really do most of chemistry,” he says.

Olexandr Isayev, a chemistry professor at Carnegie Mellon University, says these techniques are already being widely applied by companies in chemistry and life sciences. And for researchers, previously out of reach problems such as optimizing chemical reactions, developing new battery materials, and understanding protein binding are finally becoming tractable.

As with most AI applications, the biggest bottleneck is data, says Isayev. Meta’s recently released materials data set was made up of DFT calculations on 118 million molecules. A model trained on this data achieved state-of-the-art performance, but creating the training material took vast computing resources, well beyond what’s accessible to most research teams. That means fulfilling the full promise of this approach will require massive investment.

Modeling a weakly correlated system using DFT is not an exponentially scaling problem, though. This suggests that with more data and computing resources, AI-based classical approaches could simulate even the largest of these systems, says Tkatchenko. Given that quantum computers powerful enough to compete are likely still decades away, he adds, AI’s current trajectory suggests it could reach important milestones, such as precisely simulating how drugs bind to a protein, much sooner.

Strong correlationsWhen it comes to simulating strongly correlated quantum systems—ones whose particles interact a lot—methods like DFT quickly run out of steam. While more exotic, these systems include materials with potentially transformative capabilities, like high-temperature superconductivity or ultra-precise sensing. But even here, AI is making significant strides.

In 2017, EPFL’s Carleo and Microsoft’s Troyer published a seminal paper in Scienceshowing that neural networks could model strongly correlated quantum systems. The approach doesn’t learn from data in the classical sense. Instead, Carleo says, it is similar to DeepMind’s AlphaZero model, which mastered the games of Go, chess, and shogi using nothing more than the rules of each game and the ability to play itself.

In this case, the rules of the game are provided by Schrödinger’s equation, which can precisely describe a system’s quantum state, or wave function. The model plays against itself by arranging particles in a certain configuration and then measuring the system’s energy level. The goal is to reach the lowest energy configuration (known as the ground state), which determines the system’s properties. The model repeats this process until energy levels stop falling, indicating that the ground state—or something close to it—has been reached.

The power of these models is their ability to compress information, says Carleo. “The wave function is a very complicated mathematical object,” he says. “What has been shown by several papers now is that [the neural network] is able to capture the complexity of this object in a way that can be handled by a classical machine.”

Since the 2017 paper, the approach has been extended to a wide range of strongly correlated systems, says Carleo, and results have been impressive. The Science paper he published with colleagues last month put leading classical simulation techniques to the test on a variety of tricky quantum simulation problems, with the goal of creating a benchmark to judge advances in both classical and quantum approaches.

Carleo says that neural-network-based techniques are now the best approach for simulating many of the most complex quantum systems they tested. “Machine learning is really taking the lead in many of these problems,” he says.

These techniques are catching the eye of some big players in the tech industry. In August, researchers at DeepMind showed in a paper in Science that they could accurately model excited states in quantum systems, which could one day help predict the behavior of things like solar cells, sensors, and lasers. Scientists at Microsoft Research have also developed an open-source software suite to help more researchers use neural networks for simulation.

One of the main advantages of the approach is that it piggybacks on massive investments in AI software and hardware, says Filippo Vicentini, a professor of AI and condensed-matter physics at École Polytechnique in France, who was also a coauthor on the Science benchmarking paper: “Being able to leverage these kinds of technological advancements gives us a huge edge.”

There is a caveat: Because the ground states are effectively found through trial and error rather than explicit calculations, they are only approximations. But this is also why the approach could make progress on what has looked like an intractable problem, says Juan Carrasquilla, a researcher at ETH Zurich, and another coauthor on the Science benchmarking paper.

If you want to precisely track all the interactions in a strongly correlated system, the number of calculations you need to do rises exponentially with the system’s size. But if you’re happy with an answer that is just good enough, there’s plenty of scope for taking shortcuts.

“Perhaps there’s no hope to capture it exactly,” says Carrasquilla. “But there’s hope to capture enough information that we capture all the aspects that physicists care about. And if we do that, it’s basically indistinguishable from a true solution.”

And while strongly correlated systems are generally too hard to simulate classically, there are notable instances where this isn’t the case. That includes some systems that are relevant for modeling high-temperature superconductors, according to a 2023 paper in Nature Communications.

“Because of the exponential complexity, you can always find problems for which you can’t find a shortcut,” says Frank Noe, research manager at Microsoft Research, who has led much of the company’s work in this area. “But I think the number of systems for which you can’t find a good shortcut will just become much smaller.”

No magic bulletsHowever, Stefanie Czischek, an assistant professor of physics at the University of Ottawa, says it can be hard to predict what problems neural networks can feasibly solve. For some complex systems they do incredibly well, but then on other seemingly simple ones, computational costs balloon unexpectedly. “We don’t really know their limitations,” she says. “No one really knows yet what are the conditions that make it hard to represent systems using these neural networks.”

Meanwhile, there have also been significant advances in other classical quantum simulation techniques, says Antoine Georges, director of the Center for Computational Quantum Physics at the Flatiron Institute in New York, who also contributed to the recent Science benchmarking paper. “They are all successful in their own right, and they are also very complementary,” he says. “So I don’t think these machine-learning methods are just going to completely put all the other methods out of business.”

Quantum computers will also have their niche, says Martin Roetteler, senior director of quantum solutions at IonQ, which is developing quantum computers built from trapped ions. While he agrees that classical approaches will likely be sufficient for simulating weakly correlated systems, he’s confident that some large, strongly correlated systems will be beyond their reach. “The exponential is going to bite you,” he says. “There are cases with strongly correlated systems that we cannot treat classically. I’m strongly convinced that that’s the case.”

In contrast, he says, a future fault-tolerant quantum computer with many more qubits than today’s devices will be able to simulate such systems. This could help find new catalysts or improve understanding of metabolic processes in the body—an area of interest to the pharmaceutical industry.

Neural networks are likely to increase the scope of problems that can be solved, says Jay Gambetta, who leads IBM’s quantum computing efforts, but he’s unconvinced they’ll solve the hardest challenges businesses are interested in.

“That’s why many different companies that essentially have chemistry as their requirement are still investigating quantum—because they know exactly where these approximation methods break down,” he says.

Gambetta also rejects the idea that the technologies are rivals. He says the future of computing is likely to involve a hybrid of the two approaches, with quantum and classical subroutines working together to solve problems. “I don’t think they’re in competition. I think they actually add to each other,” he says.

But Scott Aaronson, who directs the Quantum Information Center at the University of Texas, says machine-learning approaches are directly competing against quantum computers in areas like quantum chemistry and condensed-matter physics. He predicts that a combination of machine learning and quantum simulations will outperform purely classical approaches in many cases, but that won’t become clear until larger, more reliable quantum computers are available.

“From the very beginning, I’ve treated quantum computing as first and foremost a scientific quest, with any industrial applications as icing on the cake,” he says. “So if quantum simulation turns out to beat classical machine learning only rarely, I won’t be quite as crestfallen as some of my colleagues.”

One area where quantum computers look likely to have a clear advantage is in simulating how complex quantum systems evolve over time, says EPFL’s Carleo. This could provide invaluable insights for scientists in fields like statistical mechanics and high-energy physics, but it seems unlikely to lead to practical uses in the near term. “These are more niche applications that, in my opinion, do not justify the massive investments and the massive hype,” Carleo adds.

Nonetheless, the experts MIT Technology Review spoke to said a lack of commercial applications is not a reason to stop pursuing quantum computing, which could lead to fundamental scientific breakthroughs in the long run.

“Science is like a set of nested boxes—you solve one problem and you find five other problems,” says Vicentini. “The complexity of the things we study will increase over time, so we will always need more powerful tools.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

People are using Google study software to make AI podcasts—and they’re weird and amazing

Google’s new AI podcasting tool, called Audio Overview, has become a surprise viral hit.

The podcasting feature was launched in mid-September as part of NotebookLM, a year-old AI-powered research assistant. NotebookLM, which is powered by Google’s Gemini 1.5 model, allows people to upload content such as links, videos, PDFs, and text. They can then ask the system questions about the content, and it offers short summaries.

The tool generates a podcast called Deep Dive, which features a male and a female voice discussing whatever you uploaded. The voices are breathtakingly realistic.

Yes, it’s cool—bordering on delightful, even—but it is also not immune from the problems that plague generative AI, such as hallucinations and bias. Here are some of the main ways people are using NotebookLM so far.

—Melissa Heikkilä

A new law in California protects consumers’ brain data. Some think it doesn’t go far enough.

On September 28, California became the second US state to officially recognize the importance of mental privacy in state law. Measuring brain activity can reveal a lot about a person—and that’s why neural data needs to be protected.Brain data is precious. It’s not the same as thought, but it can be used to work out how we’re thinking and feeling, and reveal our innermost preferences and desires. Jessica Hamzelou, our senior biotech reporter, has taken a look at how California’s law might protect mental privacy—and how far we still have to go. Read the full story.

This story is from The Checkup, our weekly newsletter giving you the inside track on all things biotech. Sign up to receive it in your inbox every Thursday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The US is experiencing some of its hottest October temperatures ever
And we’d better get used to it. (Vox)
+ California is gripped by a brutal heatwave right now. (USA Today)
+ The heat is comparable to the heights of July or August. (WP $)

2 An mRNA vaccine for bird flu is in the works
The virus is constantly changing and appears to be getting better at transferring from human to human. (Wired $)
+ The virus has killed dozens of tigers in Vietnam zoos. (The Guardian)
+ Flu season is coming—and so is the risk of an all-new bird flu. (MIT Technology Review)

3 Google is testing a new search verification featureIt’s designed to highlight trustworthy news sources among the spam.(The Verge)
+ Why Google’s AI Overviews gets things wrong. (MIT Technology Review)

4 AI is not an all-knowing oracle
Some businesses are finding that out the hard way. (WSJ $)
+ What is AI? (MIT Technology Review)

5 The Three Mile Island nuclear plant owner is seeking a $1.6 billion loan
It’s asking the Energy Department for help to reopen the facility. (WP $)
+ Why Microsoft made a deal to help restart Three Mile Island. (MIT Technology Review)

6 PayPal’s first business transaction using a stablecoin is completeThe payment method is particularly popular in countries with volatile currencies. (Bloomberg $)

7 How NASA plans to replace the ISSIts commercial-built space stations have run into trouble. (Ars Technica)
+ NASA’s Europa Clipper spacecraft is set to look for life-friendly conditions around Jupiter. (MIT Technology Review)

8 What AI reveals about flies’ brains And what it can tell us about our own thought processes. (Vice)

9 A stem cell transplant could help to repair sight loss
A successful study on a monkey suggests it could work in humans. (New Scientist $)

10 Please, no more apps!
The tedium of having to download yet another one is too much to bear. (The Atlantic $)

Quote of the day

“Just try finding something else that has improved with age in space. I dare you.”

—Kathryn Sullivan, an astronaut who flew on the Hubble Space Telescope’s 1990 launch mission, tells IEEE Spectrum why the telescope is so remarkable.

The big story

The humble oyster could hold the key to restoring coastal waters. Developers hate it.

October 2023

Carol Friend has taken on a difficult job. She is one of the 10 people in Delaware currently trying to make it as a cultivated oyster farmer.

Her Salty Witch Oyster Company holds a lease to grow the mollusks as part of the state’s new program for aquaculture, launched in 2017. It has sputtered despite its obvious promise.

Five years after the first farmed oysters went into the Inland Bays, the aquaculture industry remains in a larval stage. Oysters themselves are almost mythical in their ability to clean and filter water. But human willpower, investment, and flexibility are all required to allow the oysters to simply do their thing—particularly when developers start to object. Read the full story.

—Anna Kramer

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Aww, dolphins smile when they play together!
• If you’re in the mood for something spooky, here’s some of the best TV shows around.
• Saturn is full of mysteries, and our interest in solving them is returning some fascinating results.
• Why indie sleaze technically never actually existed.

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This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

On September 28, California became the second US state to officially recognize the importance of mental privacy in state law. That pink, jelly-like, throbbing mass under your skull—a.k.a. your brain—contains all your thoughts, memories, and ideas. It controls your feelings and actions. Measuring brain activity can reveal a lot about a person—and that’s why neural data needs to be protected.

Regular Checkup readers will be familiar with some of the burgeoning uses of “mind-reading” technologies. We can track brain activity with all sorts of devices, some of which measure brain waves while others track electrical activity or blood flow. Scientists have been able to translate this data into signals to help paralyzed people move their limbs or even communicate by thought alone.

But this data also has uses beyond health care. Today, consumers can buy headsets that allow them to learn more about how their brains work and help them feel calm. Employers use devices to monitor how alert their employees are, and schools use them to check if students are paying attention.

Brain data is precious. It’s not the same as thought, but it can be used to work out how we’re thinking and feeling, and reveal our innermost preferences and desires. So let’s look at how California’s law might protect mental privacy—and how far we still have to go.

The new bill amends the California Consumer Privacy Act of 2018, which grants consumers rights over personal information that is collected by businesses. The term “personal information” already included biometric data (such as your face, voice, or fingerprints). Now it also explicitly includes neural data.

The bill defines neural data as “information that is generated by measuring the activity of a consumer’s central or peripheral nervous system, and that is not inferred from nonneural information.” In other words, data collected from a person’s brain or nerves.

The law prevents companies from selling or sharing a person’s data and requires them to make efforts to deidentify the data. It also gives consumers the right to know what information is collected and the right to delete it.

“This new law in California will make the lives of consumers safer while sending a clear signal to the fast-growing neurotechnology industry there are high expectations that companies will provide robust protections for mental privacy of consumers,” Jared Genser, general counsel to the Neurorights Foundation, which cosponsored the bill, said in a statement. “That said, there is much more work ahead.”

Genser hopes the California law will pave the way for national and international legislation that protects the mental privacy of individuals all over the world. California is a good place to start—the state is home to plenty of neurotechnology companies, so there’s a good chance we’ll see the effects of the bill ripple out from there.

But some proponents of mental privacy aren’t satisfied that the law does enough to protect neural data. “While it introduces important safeguards, significant ambiguities leave room for loopholes that could undermine privacy protections, especially regarding inferences from neural data,” Marcello Ienca, an ethicist at the Technical University of Munich, posted on X.

One such ambiguity concerns the meaning of “nonneural information,” according to Nita Farahany, a futurist and legal ethicist at Duke University in Durham, North Carolina. “The bill’s language suggests that raw data [collected from a person’s brain] may be protected, but inferences or conclusions—where privacy risks are most profound—might not be,” Farahany wrote in a post on LinkedIn.

Ienca and Farahany are coauthors of a recent paper on mental privacy. In it, they and Patrick Magee, also at Duke University, argue for broadening the definition of neural data to what they call “cognitive biometrics.” This category could include physiological and behavioral information along with brain data—in other words, pretty much anything that could be picked up by biosensors and used to infer a person’s mental state.

After all, it’s not just your brain activity that gives away how you’re feeling. An uptick in heart rate might indicate excitement or stress, for example. Eye-tracking devices might help give away your intentions, such as a choice you’re likely to make or a product you might opt to buy. These kinds of data are already being used to reveal information that might otherwise be extremely private. Recent research has used EEG data to predict volunteers’ sexual orientation or whether they use recreational drugs. And others have used eye-tracking devices to infer personality traits.

Given all that, it’s vital we get it right when it comes to protecting mental privacy. As Farahany, Ienca, and Magee put it: “By choosing whether, when, and how to share their cognitive biometric data, individuals can contribute to advancements in technology and medicine while maintaining control over their personal information.”

Now read the rest of The CheckupRead more from MIT Technology Review‘s archiveNita Farahany detailed her thoughts on tech that aims to read our minds and probe our memories in a fascinating Q&A last year. Targeted dream incubation, anyone?

There are lots of ways that your brain data could be used against you (or potentially exonerate you). Law enforcement officials have already started asking neurotech companies for data from people’s brain implants. In one case, a person had been accused of assaulting a police officer but, as brain data proved, was just having a seizure at the time.

EEG, the technology that allows us to measure brain waves, has been around for 100 years. Neuroscientists are wondering how it might be used to read thoughts, memories, and dreams within the next 100 years.

Electrodes implanted in or on the brain can provide us with the most detailed insights into how our minds work. They can also provide us with amazing imagery, like this video that essentially shows what a thought looks like as it is being formed.

What exactly is going on in our brains, anyway? When neuroscientists used electrodes implanted deep in the brains of people being treated for epilepsy, they found order and chaos.

From around the webInfections are responsible for 13% of cancers. Here’s how to protect against four of them. (New York Times)

Scientists have created the first map of the neurons in a fruit fly’s brain. All 139,225 of them. (Nature)

Oropouche fever is surging in South America. Disturbingly, there are increasing reports of the virus harming pregnant women and their babies. (Viruses)

Women in heterosexual relationships already do more housework and household organization than their partners. Is technology making things worse? (BBC Future)

Do you sigh during your sleep? It could be a sign of something serious. (Nature)

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“All right, so today we are going to dive deep into some cutting-edge tech,” a chatty American male voice says. But this voice does not belong to a human. It belongs to Google’s new AI podcasting tool, called Audio Overview, which has become a surprise viral hit.

The podcasting feature was launched in mid-September as part of NotebookLM, a year-old AI-powered research assistant. NotebookLM, which is powered by Google’s Gemini 1.5 model, allows people to upload content such as links, videos, PDFs, and text. They can then ask the system questions about the content, and it offers short summaries.

The tool generates a podcast called Deep Dive, which features a male and a female voice discussing whatever you uploaded. The voices are breathtakingly realistic—the episodes are laced with little human-sounding phrases like “Man” and “Wow” and “Oh right” and “Hold on, let me get this right.” The “hosts” even interrupt each other.

To test it out, I copied every story from MIT Technology Review’s 125th-anniversary issue into NotebookLM and made the system generate a 10-minute podcast with the results. The system picked a couple of stories to focus on, and the AI hosts did a great job at conveying the general, high-level gist of what the issue was about. Have a listen.

MIT Technology Review 125th Anniversary issue

The AI system is designed to create “magic in exchange for a little bit of content,” Raiza Martin, the product lead for NotebookLM, said on X. The voice model is meant to create emotive and engaging audio, which is conveyed in an “upbeat hyper-interested tone,” Martin said.

NotebookLM, which was originally marketed as a study tool, has taken a life of its own among users. The company is now working on adding more customization options, such as changing the length, format, voices, and languages, Martin said. Currently it’s supposed to generate podcasts only in English, but some users on Reddit managed to get the tool to create audio in French and Hungarian.

Yes, it’s cool—bordering on delightful, even—but it is also not immune from the problems that plague generative AI, such as hallucinations and bias.

Here are some of the main ways people are using NotebookLM so far.

On-demand podcastsAndrej Karpathy, a member of OpenAI’s founding team and previously the director of AI at Tesla, said on X that Deep Dive is now his favorite podcast. Karpathy created his own AI podcast series called Histories of Mysteries, which aims to “uncover history’s most intriguing mysteries.” He says he researched topics using ChatGPT, Claude, and Google, and used a Wikipedia link from each topic as the source material in NotebookLM to generate audio. He then used NotebookLM to generate the episode descriptions. The whole podcast series took him two hours to create, he says.

“The more I listen, the more I feel like I’m becoming friends with the hosts and I think this is the first time I’ve actually viscerally liked an AI,” he wrote. “Two AIs! They are fun, engaging, thoughtful, open-minded, curious.”

Study guidesThe tool shines when it is given complicated source material that it can describe in an easily accessible way. Allie K. Miller, a startup AI advisor, used the tool to create a study guide and summary podcast of F. Scott Fitzgerald’s The Great Gatsby.

This is amazing.

In less than 10 minutes, I grab all of Great Gatsby and generate a summary, study guide, Q&A bot, and podcast about it.

My team is on the floor, rolling with laughter right now. pic.twitter.com/avCUP67zLt

— Allie K. Miller (@alliekmiller) September 25, 2024

Machine-learning researcher Aaditya Ura fed NotebookLM with the code base of Meta’s Llama-3 architecture. He then used another AI tool to find images that matched the transcript to create an educational video.

Inspired by @karpathy 's NotebookLM project, I gave the codebase of Llama-3 Architecture to NLM and used Rag to find the perfect images to sync with the generated audio.

The result exceeded my expectations. Google's NotebookLM is truly amazing

Here is a youtube link as… https://t.co/00NARRPk7C pic.twitter.com/S71mTAMh8f

— Aaditya Ura (@aadityaura) September 30, 2024

Mohit Shridhar, a research scientist specializing in robotic manipulation, fed a recent paper he’d written about using generative AI models to train robots into NotebookLM.

“It’s actually really creative. It came up with a lot of interesting analogies,” he says. “It compared the first part of my paper to an artist coming up with a blueprint, and the second part to a choreographer figuring out how to reach positions.”

Event summaries Alex Volkov, a human AI podcaster, used NotebookLM to create a Deep Dive episode summarizing of the announcements from OpenAI’s global developer conference Dev Day.

I know you all love NotebookLM Deep Dive – So here's all of the @OpenAI Dev Day 2024 announcements, as narrated by NoteBookLM podcast hosts

They did an incredible job!

Should I keep making these? pic.twitter.com/pfyQun51gV

— Alex Volkov (Thursd/AI) (@altryne) October 1, 2024

Hypemen

The Deep Dive outputs can be unpredictable, says Martin. For example, Thomas Wolf, the cofounder and chief science officer of Hugging Face, tested the AI model on his résumé and received eight minutes of “realistically-sounding deep congratulations for your life and achievements from a duo of podcast experts.”

Self-care life hack: if you feel a bit down/tired, paste the url of your website/linkedin/bio in Google's NotebookLM to get 8 min of realistically sounding deep congratulations for your life and achievements from a duo of podcast experts pic.twitter.com/k6krAgmMMd

— Thomas Wolf (@Thom_Wolf) September 29, 2024

Just pure silliness

In one viral clip, someone managed to send the two voices into an existential spiral when they “realized” they were, in fact, not humans but AI systems. The video is hilarious.

The NotebookLM hosts realizing they are AI and spiraling out is a twist I did not see coming pic.twitter.com/PNjZJ7auyh

— Olivia Moore (@omooretweets) September 29, 2024

The tool is also good for some laughs. Exhibit A: Someone just fed it the words “poop” and “fart” as source material, and got over nine minutes of two AI voices analyzing what this might mean.

Someone gave NotebookLM a document with just "poop" and "fart" repeated over and over again.

I did NOT expect the result to be this good. pic.twitter.com/nXYJJ7QnGS

— Kuldar ⟣ (@kkuldar) September 30, 2024

The problemsNotebookLM created amazingly realistic-sounding and engaging AI podcasts. But I wanted to see how it fared with toxic content and accuracy.

Let’s start with hallucinations. In one AI podcast version of a story I wrote on hyperrealistic AI deepfakes, the AI hosts said that a journalist called “Jess Mars” wrote the story. In reality, this was an AI-generated character from a story I had to read out to record data for my AI avatar.

This made me wonder what other mistakes had crept into the AI podcasts I had generated. Humans already have a tendency to trust what computer programs say, even when they are wrong. I can see this problem being amplified when the false statements are made by a friendly and authoritative voice, causing wrong information to proliferate.

Next I wanted to put the tool’s content moderation to the test. I added some toxic content, such as racist stereotypes, into the mix. The model did not pick it up.

I also pasted an excerpt from Adolf Hitler’s Mein Kampf into NotebookLM. To my surprise, the model started generating audio based on it. Despite being programmed to be hyper-enthusiastic about topics, the AI voices expressed clear disgust and discomfort with the text, and they added a lot of context to highlight how problematic it was. What a relief.

I also fed NotebookLM policy manifestos from both Kamala Harris and Donald Trump.

The hosts were far more enthusiastic about Harris’s election platform, calling the title “catchy” and saying its approach was a good way to frame things. For example, the AI hosts supported Harris’s energy policy. “Honestly, that’s the kind of stuff people can really get behind—not just some abstract policy, but something that actually impacts their bottom line,” the female host said.

Harris manifesto

For Trump, the AI hosts were more skeptical. They repeatedly pointed out inconsistencies in the policy proposals, called the language “intense,” deemed certain policy proposals “head scratchers,” and said the text catered to Trump’s base. They also asked whether Trump’s foreign policy could lead to further political instability.

Trump manifesto

In a statement, a Google spokesperson said: “NotebookLM is a tool for understanding, and the Audio Overviews are generated based on the sources that you upload. Our products and platforms are not built to favor any specific candidates or political viewpoints.”

How to try it yourself

1. Got to NotebookLM and create a new notebook.
2. You first need to add a source. It can be a PDF document, a public YouTube link, an MP3 file, a Google Docs file, or a link to a website, or you can paste in text directly.
3. A “Notebook Guide” pop-up should appear. If not, it’s in the right-hand corner next to the chat. This will display a short AI-generated summary of your source material and suggested questions you can ask the AI chatbot about it.
4. The Audio Overview feature is in the top-right corner. Click “Generate.” This should take a few minutes.
5. Once it is ready, you can either download it or share a link.

Rhiannon Williams contributed reporting.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

AI-generated images can teach robots how to act

Generative AI models can produce images in response to prompts within seconds, and they’ve recently been used for everything from highlighting their own inherent bias to preserving precious memories.

Now, researchers from Stephen James’s Robot Learning Lab in London are using image-generating AI models for a new purpose: creating training data for robots. They’ve developed a new system, called Genima, that fine-tunes the image-generating AI model Stable Diffusion to draw robots’ movements, helping guide them both in simulations and in the real world.

Genima could make it easier to train different types of robots to complete tasks—machines ranging from mechanical arms to humanoid robots and driverless cars—as well as making AI web agents more useful. Read the full story.

—Rhiannon Williams

These 15 companies are innovating in climate tech

We’ve just unveiled our 2024 list of 15 Climate Tech Companies to Watch. This annual project is one the climate team at MIT Technology Review pours a lot of time and thought into, and we’re thrilled to finally share it with you.

Our goal is to spotlight businesses we believe could help make a dent in climate change. This year’s list includes companies from a wide range of industries, headquartered on five continents. If you haven’t checked it out yet, I highly recommend giving it a look. Each company has a profile in which we’ve outlined why it made the list, what sort of impact the business might have, and what challenges it’s likely to face.

Casey Crownhart, our senior climate reporter, has dug into what these pioneering businesses reveal about the race to address climate change. Read about what she found out here.

This story is from The Spark, our weekly climate and energy newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has been valued at an eye watering $157 billion
A new funding round has made it one of the most valuable startups of all time. (WP $)
+ The company has urged investors to avoid funding rival AI firms. (FT $)
+ The secret to OpenAI’s fundraising success? Its extremely capable CFO. (The Information $)

2 Chipmakers are keeping a close eye on two North Carolina mines
Hurricane Helene has forced production to grind to a halt. (Bloomberg $)
+ The mines contain high purity quartz, which is essential to make chips. (Vox)

3 Hacking Meta’s smart glasses turns them into powerful doxxing tools
Students equipped the device with real-time facial recognition software. (404 Media)
+ The coolest thing about smart glasses is not the AR. It’s the AI. (MIT Technology Review)

4 American chips are powering Russian missiles
The deadly weapons are killing Ukrainian civilians, including a six-year old girl. (Bloomberg $)

5 Character.ai is pivoting away from making AI models
Ultimately, training LLMs proved to be too expensive. (FT $)
+ Make no mistake—AI is owned by Big Tech. (MIT Technology Review)

6 Apple is punishing social appsThey’re no longer allowed to access a user’s contact list. (NYT $)
+ Threads is letting users connect with other social networks for the first time. (WP $)

7 Flying cars are hovering in a gray legal areaToday’s EVOTLs are technically breaking the law, and it’s hard to see that changing. (NY Mag $)
+ These aircraft could change how we fly. (MIT Technology Review)

8 Workplace AI tools can’t always be trustedMake sure you’re aware of when it’s still writing a transcript, for one. (WP $)
+ You should think twice about sharing personal info with chatbots, too. (The Atlantic $)

9 How to boost the benefits of meditationStimulating the brain could help to unlock the mysteries of the mind. (Vox)
+ Here’s how personalized brain stimulation could treat depression. (MIT Technology Review)

10 This video game birthed a generation of historians
Age of Empires is a classic that defined a genre. (The Guardian)

Quote of the day

“We have some stock in Nvidia, and that’s who’s going to get all of this money anyway.”

—A venture capitalist who didn’t participate in OpenAI’s massive funding round explains why they don’t have FOMO to Axios’ business editor Dan Primack.

The big story

This town’s mining battle reveals the contentious path to a cleaner future

January 2024

In June last year, Talon, an exploratory mining company, submitted a proposal to Minnesota state regulators to begin digging up as much as 725,000 metric tons of raw ore per year, mainly to unlock the rich and lucrative reserves of high-grade nickel in the bedrock.

Talon is striving to distance itself from the mining industry’s dirty past, portraying its plan as a clean, friendly model of modern mineral extraction. It proclaims the site will help to power a greener future for the US by producing the nickel needed to manufacture batteries for electric cars and trucks, but with low emissions and light environmental impacts.

But as the company has quickly discovered, a lot of locals aren’t eager for major mining operations near their towns. Read the full story.

—James Temple

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Watch out, Nibi the adorable beaver’s about! (Thanks Alice!)
• If you’ve never seen one man sing both sides of Phantom of the Opera before, now you have.
• TV doesn’t come much more unhinged than Love Is Blind (if you haven’t seen it, you’re in for a treat).
• How to catch a glimpse of the Comet A3, also known as Comet Tsuchinshan-ATLAS.

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This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

It’s finally here! We’ve just unveiled our 2024 list of 15 Climate Tech Companies to Watch. This annual project is one the climate team at MIT Technology Review pours a lot of time and thought into, and I’m thrilled to finally share it with you.

Our goal is to spotlight businesses we believe could help make a dent in climate change. This year’s list includes companies from a wide range of industries, headquartered on five continents. If you haven’t checked it out yet, I highly recommend giving it a look. Each company has a profile in which we’ve outlined why it made the list, what sort of impact the business might have, and what challenges it’s likely to face.

In the meantime, I wanted to share a few reflections on this year’s list as a whole. Because this slate of companies exemplifies a few key themes that I see a lot in my reporting on climate technology.

1. Addressing climate change requires building a lot of stuff, on a massive scale, and fast. A handful of the companies we included on this list stand out because of the sheer scale at which they’re building and deploying technology. And we need scale, because addressing climate change requires going from tens of billions of metric tons of carbon dioxide emissions every year to net zero.

BYD, for example, featured on our 2023 list, and it was a clear choice for our team to feature the company again.

For a while, the title of the world’s largest electric vehicle (EV) producer has depended on how you define an EV. If you include plug-in hybrids, BYD takes the crown. If you take the purist point of view and only count fully battery-powered vehicles, Tesla wins.

But now, BYD is knocking on Tesla’s door for even that purist title, outselling the company in the last quarter of 2023. The company’s dominant speed and scale at getting EVs onto the roads makes it one I’m keeping my eyes on.

Other companies are still growing but making significant progress. LanzaJet just opened a factory in Georgia that can produce nine million gallons of alternative jet fuel each year. That’s only a tiny fraction of the billions of gallons of fuel used every year, but it’s a major step forward for alternative fuels. And First Solar, a US solar manufacturer, just opened a $1.1 billion factory in Alabama, and plans to open another in Louisiana in 2025.

1. With climate impacts embedded in longstanding systems, we need creative new ways to tackle old problems. There are parts of the race to address climate change that most people are probably familiar with. Fossil fuels and their associated emissions are clearly visible in power plants, for example, or in gas-powered vehicles.

But hidden climate challenges exist within familiar objects. Producing items from shampoo bottles to sidewalks can emit huge amounts of planet-warming pollution. We featured a few companies tackling these less visible problems.

Sublime Systemsis on the list again this year. The company is making progress scaling up its electrochemical process to make cement with significantly lower emissions than the conventional method. We also highlight a company working in the chemical industry: Solugen runs a factory in Houston, and is about to open another in Minnesota, making chemicals with biological starting ingredients rather than fossil fuels.

1. Climate change is a vast problem that touches virtually every industry, so there’s a lot of work to do. As we discussed potential companies for this list over the last few months, I was struck by how tricky it was going to be to represent all the industries we wanted to. I could have personally picked 15 companies just working on batteries, for example.

We wanted some energy companies on the list, of course, as well as some in transportation. But then there’s also agriculture, chemicals, fuels, and what about climate adaptation? I think our final list shows just how massive an umbrella term “climate tech” has become.

For example, there’s Rumin8, an Australian company making supplements for cows that can cut down on how much methane they belch out. And then we have Pano AI, which is installing camera stations that pair up with AI to better detect wildfires, which are worsening as the planet heats up.

The world has a lot of work to do to make the progress needed on climate change. I’ll be watching to see what difference these companies are able to make this year, and beyond.

Now read the rest of The SparkRelated readingCheck out the full list of 15 Climate Tech Companies to Watch to get an in-depth look at all the companies we featured.

We’re hosting a virtual event on producing climate-friendly food, coming up on Thursday, October 10 at noon eastern time. My colleague James Temple and I will be speaking with folks from Rumin8 and Pivot Bio, the two food companies on this year’s list. This event is exclusive to subscribers, so do subscribe if you haven’t already, then register here!

GETTY IMAGESAnother thingThe UK just shut down its final coal-fired power plant. It’s a major milestone for the country, which has historically relied heavily on the notoriously polluting fossil fuel.

I dug into the data to see how the nation replaced coal on its grid, and how the rest of the world is faring on the journey to phase out coal. Check out the full story here.

And one moreJames Temple wrote a smart essay that pushes back against the idea that AI is going to be our climate savior. There are certainly promising applications of AI across climate, but the technology is also power-hungry. And it would be a mistake to expect AI to deliver us from all of our problems. You should definitely give it a read.

Keeping up with climate See the latest photos of the destruction caused by Hurricane Helene. The storm struck Florida as a Category 4 storm, but the highest death toll has been in mountainous western North Carolina, where devastating floods hit. (Washington Post)

→ Even people who have lived with hurricanes for years are facing tougher decisions, as Jeff VanderMeer discusses in a guest essay. (New York Times)

The immediate devastation from the hurricane is clear, but the long-term effects could ripple across the grid. Key equipment is down in western North Carolina, and there’s a critical shortage of repair supplies. (Latitude Media)

A major policy question in the US right now: where should low-emissions hydrogen go? (Canary Media)

→ Earlier this year, I explained why hydrogen could be used for nearly everything—but probably shouldn’t. (MIT Technology Review)

An oil executive spoke at an NYC climate event put on by the New York Times. Then, protestors shut down the talk. (Inside Climate News)

Charm Industrial is working with the US Forest Service on a carbon removal pilot project. The idea? Convert trees and other material from forest-thinning projects into bio-oil, then inject it deep underground. (Heatmap News)

→ We covered Charm Industrial’s technology, based on corn stalks, in this 2022 story. (MIT Technology Review)

Rich countries pledged hundreds of millions of dollars to help pay for loss and damage from disasters fueled by climate change. It was a tiny fraction of what experts say is needed, and new funding has slowed to a trickle. (Grist)

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Generative AI models can produce images in response to prompts within seconds, and they’ve recently been used for everything from highlighting their own inherent bias to preserving precious memories.

Now, researchers from Stephen James’s Robot Learning Lab in Londonare using image-generating AI models for a new purpose: creating training data for robots. They’ve developed a new system, called Genima, that fine-tunes the image-generating AI model Stable Diffusion to draw robots’ movements, helping guide them both in simulations and in the real world. The research is due to be presented at the Conference on Robot Learning (CoRL) next month.

The system could make it easier to train different types of robots to complete tasks—machines ranging from mechanical arms to humanoid robots and driverless cars. It could also help make AI web agents, a next generation of AI tools that can carry out complex tasks with little supervision, better at scrolling and clicking, says Mohit Shridhar, a research scientist specializing in robotic manipulation, who worked on the project.

“You can use image-generation systems to do almost all the things that you can do in robotics,” he says. “We wanted to see if we could take all these amazing things that are happening in diffusion and use them for robotics problems.”

To teach a robot to complete a task, researchers normally train a neural network on an image of what’s in front of the robot. The network then spits out an output in a different format—the coordinates required to move forward, for example.

Genima’s approach is different because both its input and output are images, which is easier for the machines to learn from, says Ivan Kapelyukh, a PhD student at Imperial College London, who specializes in robot learning but wasn’t involved in this research.

“It’s also really great for users, because you can see where your robot will move and what it’s going to do. It makes it kind of more interpretable, and means that if you’re actually going to deploy this, you could see before your robot went through a wall or something,” he says.

Genima works by tapping into Stable Diffusion’s ability to recognize patterns (knowing what a mug looks like because it’s been trained on images of mugs, for example) and then turning the model into a kind of agent—a decision-making system.

MOHIT SHRIDHAR, YAT LONG (RICHIE) LO, STEPHEN JAMES ROBOT LEARNING LABFirst, the researchers fine-tuned stable Diffusion to let them overlay data from robot sensors onto images captured by its cameras.

The system renders the desired action, like opening a box, hanging up a scarf, or picking up a notebook, into a series of colored spheres on top of the image. These spheres tell the robot where its joint should move one second in the future.

The second part of the process converts these spheres into actions. The team achieved this by using another neural network, called ACT, which is mapped on the same data. Then they used Genima to complete 25 simulations and nine real-world manipulation tasks using a robot arm. The average success rate was 50% and 64%, respectively.

Although these success rates aren’t particularly high, Shridhar and the team are optimistic that the robot’s speed and accuracy can improve. They’re particularly interested in applying Genima to video-generation AI models, which could help a robot predict a sequence of future actions instead of just one.

The research could be particularly useful for training home robots to fold laundry, close drawers, and other domestic tasks. However, its generalized approach means it’s not limited to a specific kind of machine, says Zoey Chen, a PhD student at the University of Washington, who has also previously used Stable Diffusion to generate training data for robots but was not involved in this study.

“This is a really exciting new direction,” she says. “I think this can be a general way to train data for all kinds of robots.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Introducing: 15 Climate Tech Companies to Watch

The urgency of addressing climate change has never been clearer. Emissions of planet-warming gases are at record highs, as are global temperatures.

All that extra heat is endangering people around the world, supercharging threats like heatwaves and wildfires and jeopardizing established food and energy systems. We need to find new ways to generate electricity, move people and goods, produce food, and weather the challenging conditions made worse in a warming world.

The good news is that we already have many of the tools we need to take those actions, and companies are constantly bringing new innovations to the market. Our reporters and editors have compiled a comprehensive list of the 15 companies that we think have the best shot at making a difference on climate change. Check out the full list here.

Europa Clipper set to look for life-friendly conditions around Jupiter

The news: NASA is poised to launch Europa Clipper, a $5.2 billion mission to Jupiter’s fourth-largest moon, as early as October 10. The spacecraft will blast off from Kennedy Space Center in Florida atop a SpaceX Falcon Heavy rocket.

What’s the mission? Europa Clipper’s team hopes to assess the moon’s habitability—how well it could support life.It will study Europa, a possible home for extraterrestrial life, through a series of flybys after reaching Jupiter in 2030. Read the full story.

—Jenna Ahart

MIT Technology Review Narrated: The cost of building the perfect wave

The growing business of surf pools wants to bring the ocean experience inland, making surfing more accessible to communities far from the coasts.

These pools can use—and lose—millions upon millions of gallons of water every year. With many planned for areas facing water scarcity, who bears the cost of building the perfect wave?

This is our latest story to be turned into a MIT Technology Review Narrated podcast. In partnership with News Over Audio, we’ll be making a selection of our stories available, each one read by a professional voice actor. You’ll be able to listen to them on the go or download them to listen to offline.

We’re publishing a new story each week on Spotify and Apple Podcasts, including some taken from our most recent print magazine. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The death toll from Hurricane Helene is rising
Rescue and recovery teams are searching for hundreds of missing people. (WP $)
+ Entire towns have been swept away. (Vox)
+ EV owners in North Carolina are using their cars to power their homes. (The Atlantic $)
+ It’s looking like things could get even worse, too. (Slate $)

2 AI lab assistants are on the horizon
Google DeepMind and BioNTech are building research models to aid scientists. (FT $)
+ Meanwhile, OpenAI is making it easier to build its voice assistants. (Reuters)
+ Amazon has been working on a new internal chatbot, apparently. (Insider $)

3 The FTC has been permitted to proceed with its case against Amazon
Amazon has been fighting to have the landmark antitrust case dismissed. (WP $)

4 The Kremlin ordered cyberattacks on NATO alliesThe UK, US, and Australia have sanctioned the hackers, known as Evil Corp. (Bloomberg $)

5 Inside Google’s plan to regain its smart glasses crown
It’s lagging behind in the AI stakes, when its rivals are surging ahead. (The Information $)
+ The coolest thing about smart glasses is not the AR. It’s the AI. (MIT Technology Review)

6 Our genetic databases don’t reflect humanity’s diversityParticularly across Latin America. (Undark Magazine)
+ This new genome map tries to capture all human genetic variation. (MIT Technology Review)

7 Apple is desperate for workers in Vietnam
It’s offering them bonuses and gift incentives to join its manufacturing team. (Rest of World)

8 Take a peek at the future of dentistry Including tooth-regenerating drugs and tiny bots to clean your mouth.(WSJ $)

9 How Instagram became a digital dumping ground
Millennials can’t get enough of clearing out their camera reel. (The Guardian)
+ How to fix the internet. (MIT Technology Review)

10 The moon is getting its own time zone
Future inhabitants may live by Coordinated Lunar Time. (Motherboard)

Quote of the day

“It was not just a perfect storm, but it was a combination of multiple storms.”

—Meteorologist Ryan Maue explains why Hurricane Helene is so deadly, AP reports.

The big story

Broadband funding for Native communities could finally connect some of America’s most isolated places

September 2022

Rural and Native communities in the US have long had lower rates of cellular and broadband connectivity than urban areas, where four out of every five Americans live. Outside the cities and suburbs, which occupy barely 3% of US land, reliable internet service can still be hard to come by.

The covid-19 pandemic underscored the problem as Native communities locked down and moved school and other essential daily activities online. But it also kicked off an unprecedented surge of relief funding to solve it. Read the full story.

—Robert Chaney

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)+ Love him or hate him, there’s no escaping Tim Burton’s nightmarish vision of the world.
+ The 100 best songs of the past 24 years? Yes please.
+ We all know surveillance is bad, but when it’s letting us know that someone in San Francisco is blasting out Me So Horny at 9.03am, I could be persuaded otherwise.
+ Japan is celebrating 60 years of its iconic bullet trains.

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NASA is poised to launch Europa Clipper, a $5.2 billion mission to Jupiter’s fourth-largest moon, as early as October 10. The spacecraft will blast off from Kennedy Space Center in Florida atop a SpaceX Falcon Heavy rocket. It will study Europa, a possible home for extraterrestrial life, through a series of flybys after reaching Jupiter in 2030.

Europa isn’t a craterous rock like our moon. Its surface is coated with ice, and based on telescope and spacecraft observations, it harbors a colossal liquid ocean in its interior that holds twice as much water as all of Earth’s oceans combined. Europa also possesses some of life’s critical building blocks: carbon, oxygen, hydrogen, nitrogen, phosphorus, and sulfur. These conditions could be sufficient for life to have developed there, either in the depths of the ocean or in subsurface lakes.

Europa Clipper isn’t on the hunt for extraterrestrial life, however. Instead, its team hopes to assess the moon’s habitability—how well it could support life. The probe will use its range of scientific instruments, including cameras, spectrometers, magnetometers, and radars, to collect chemical, physical, and geological data in a series of flybys. Promising results could justify a mission to land on Europa and search for life.

Early this year, everything seemed on track for the planned October launch. But in May, mission team members caught wind of a potential issue with Europa Clipper’s electronics. Testing data had indicated the spacecraft’s transistors, devices that regulate the flow of electricity on the probe, wouldn’t survive the intense radiation consisting of charged particles trapped in Jupiter’s magnetic field, which is 20,000 times stronger than Earth’s.

“The mission team was advised that similar parts were failing at lower radiation doses than expected,” NASA said in a statement. Disassembling the spacecraft and replacing faulty transistors could have pushed the mission’s launch window well past October.

After months of followup testing at NASA’s Jet Propulsion Laboratory, Goddard Space Flight Center, and Applied Physics Laboratory, researchers concluded that any potential transistor damage wouldn’t impair mission operations. It was determined that the transistors could be heated to heal damage, and the 20-day breaks between large radiation exposures would offer enough recovery time. According to the New York Times, the spacecraft will also carry a box of the probe’s various transistors so that the team can monitor for damage, a bit like canaries in a coal mine. On September 9, Europa Clipper passed a milestone review called Key Decision Point E, approving it to proceed for launch.

After arriving in orbit around Jupiter, Europa Clipper will conduct 49 close flybys of Europa. At its closest, the spacecraft will come within 16 miles (26 kilometers) of the surface for detailed observations.

For more on Europa Clipper, see MIT Technology Review’s feature on the mission.

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Electric vehicles can take a long time to charge up, and places to do so can be hard to find. Gogoro’s innovative technology offers a quick and easy way to swap drained batteries for charged ones at a growing number of stations worldwide.

When a magnitude 7.4 earthquake rolled through Taiwan in April, it was the biggest to hit the island in more than a century. Hundreds of Gogoro battery-charging stations did something pretty amazing in response: They automatically powered down to reduce strain on the grid. That saved enough electricity to power thousands of homes until the grid came fully back online. And it all happened without human intervention, thanks to the company’s network of AI-powered battery-swapping stations located all over the island.

A big challenge with the transition to EVs is making sure it’s easy and fast to charge them up, no matter where you are. Charging stations can be hard to find, and if you plug into a wall (or even a standard charger), it can take hours to fully refill a battery. Gogoro has tackled these related issues by building out a network of hundreds of battery-swapping stations throughout Taiwan, where scooters (think Vespa, not Razr) far outnumber cars. Instead of recharging, customers roll up, grab a new battery, and get back on the road again in less time than it takes to fill up a tank with gas.

Now Gogoro is bringing that system online throughout the world, with locations in India, China, Colombia, and the Philippines, among other countries. Key to its success is the complete ecosystem it has created. Gogoro manufactures both scooters and batteries; the latter power not only its own vehicles but also those made by Yamaha, Suzuki, and various other local manufacturers worldwide. It also maintains a fleet of rideshare scooters available to rent (which the company made free in the aftermath of the earthquake until Taipei’s public transit system came back online). And the whole system is tied together by more than 13,000 battery-swapping stations found at 3,000 locations throughout the world.

Key indicators Industry: Electric vehicles * Founded: 2011 * Headquarters: Taipei, Taiwan * Notable fact:* Riders can exchange empty batteries for fully charged ones in less than six seconds at Gogoro’s battery-swapping stations.

Potential for impactA key challenge of transitioning away from fossil fuels is competing with the price and ubiquity of gasoline. Thanks to its network, Gogoro has made electric micro mobility vehicles convenient, efficient, and affordable, so they offer a real alternative to filling up at the pump. In fact, there are now more Gogoro stations in Taipei than gas stations.

Moreover, those stations are not only convenient but environmentally friendly. They’re able to act as virtual power plants: They can draw power during times when grid usage is low (such as at night), return power to the grid when usage is high, and even supply backup power in case of emergencies like an earthquake or typhoon. More than 1,000 Gogoro stations now do double duty in this way.

Finally, when the company’s batteries reach the end of their life for powering scooters, they can be redeployed as backup power packs for traffic lights, streetlights, and other electrical infrastructure.

CaveatsFor the company to grow and have a real impact on global emissions, it has to build networks like the one in Taiwan throughout the rest of the world. That’s incredibly capital intensive. It also means Gogoro will need to work closely with local governments, adapt to varying international regulations, redesign its vehicles to meet local consumer preferences, and partner with other manufacturers and grid power providers. It’s a tall order. The company’s rollout in India is facing delays as it awaits clarity from regulators on which subsidies will be made available. And although Gogoro can start small in new markets, if the company’s infrastructure does not keep pace with demand there, customers could be hard pressed to find fully charged batteries.

What’s more, while Gogoro’s model works well in densely populated urban areas, it faces significant challenges in suburbs and rural areas. And the company’s biggest competitor of all may be cheap gasoline—especially in countries like the US or Indonesia.

Next steps Gogoro continues to push into new markets, according to Jason Gordon, the company’s vice president of communications. “Following launches in India and the Philippines in late 2023, Gogoro has continued our expansion in 2024 with launches in Bogota, Colombia; Singapore; and Nepal,” he says, “with Santiago, Chile, planned for later this year.”

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It may not yet be a household name, but BYD is gaining recognition outside China for its affordable and accessible EVs. Despite regulatory scrutiny in the West, it’s determined to lower the boundaries to manufacturing and transporting its vehicles across the globe.

Five years ago, BYD was just another Chinese carmaker in a crowded field. Since then, the Shenzhen-based company has rapidly become the undisputed leader of China’s automotive industry, as well as the world’s biggest producer of electric vehicles (including both pure EVs and plug-in hybrids).

Much of that growth is thanks to billions of dollars in government subsidies. The company also benefited enormously from the pandemic, when rising gas prices led to an EV boom.

Another key to its success is its tightly controlled in-house production line. BYD can source everything through its own subsidiaries, from batteries and motors to the majority of the components required to make its affordably priced EVs and plug-in hybrid cars, electric buses, and monorails. This approach doesn’t just allow it to manufacture its vehicles at a lower cost than its competitors; the tight control also lets it innovate across its supply chain, rapidly incorporating new features into production.

Key indicators: Industry: Electric vehicles * Founded: 1995 * Headquarters: Shenzhen, China * Notable fact:* BYD sold 3,024,417 “new energy” vehicles, which includes battery-only vehicles and hybrids, in 2023. That’s a year-on-year increase of 62%.

Potential for impactAlthough sales of EVs are increasing globally, the majority of those new sales are being made in China. To expand its international market, which accounted for just 8% of its total sales last year, BYD is rapidly building factories across the world and investing heavily in a massive fleet of car-carrying ships.

Over the past 18 months, the company has pushed into new markets, including Brazil, Australia, and Thailand, and announced that its new factory in Indonesia has produced its first batch of cars. It has begun work on its first European factory, in Hungary, and recently unveiled plans to invest $1 billion into a plant in Turkey, which will produce 150,000 electric and rechargeable hybrid cars a year.

CaveatsBYD’s biggest challenges remain low brand awareness outside China and regulatory scrutiny in the West, which is becoming increasingly hostile toward Chinese companies. The US recently raised its already hefty tariffs on Chinese EVs in a bid to discourage companies from importing them into the US. It is poised to do even more.

In a similar effort to protect the European motor industry from an influx of lower-cost Chinese-made EVs, the European Union has slapped the company and other Chinese automakers with tariffs in addition to an existing duty tax. To circumvent this, BYD’s Hungarian and Turkey production centers would allow it to export to the EU tariff-free.

These sorts of international economic tensions are likely to persist, if not worsen, as nations strive to dominate the clean industries that will define the coming century.

Next stepsThe affordability of BYD’s models is a key part of their appeal. The company’s cheapest car is the Seagull, which sells for less than $10,000 in China. BYD plans to start selling the Seagull in Europe starting next year. It also intends to open its Hungarian factory within three years.

Better known for its batteries than for AI, BYD has long lagged behind the likes of Tesla when it comes to software. Now, it’s working on narrowing the gap. It recently unveiled the Xuanji smart car system, which includes automated parking and AI-powered voice recognition. In addition, it’s collaborating with chipmaker Nvidia to bring the next generation of car-focused chips to its models starting next year.

BYD is also among the first automakers in China to obtain a license for testing cars equipped with Level 3 autonomous-driving capabilities, which means they can take over full control under certain conditions on designated highways. These self-driving capabilities will be put to the test in a partnership with Uber, in which future BYD driverless cars could be deployed to pick up customers—if they receive approval from governments across the world, that is.

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LanzaJet is making next-generation aviation fuel without fossil fuels. The company recently opened the world’s first commercial-scale production facility that turns alcohol into jet fuel and plans to produce up to 9 million gallons each year.

LanzaJet wants to cut aviation’s climate impact by rethinking where jet fuel comes from.

Today, hopping on a plane means burning huge amounts of fossil fuels—the aviation industry accounts for about 3% of global greenhouse-gas emissions.

LanzaJet’s technology makes jet fuel using alcohol, which can be sourced from a variety of materials, including corn and sugarcane. The company’s process starts with ethanol and then uses a series of steps that pull out water, string molecules together into longer chains, and add hydrogen. The result is a chemical mixture, which the company then processes further to separate out the components that can be burned as jet fuel.

The company is a leader in this alcohol-to-jet-fuel pathway. Currently, nearly all commercially available alternative jet fuels use waste fats, oils, and greases as their starting material, but as the industry scales, there’s a growing concern about their limited supply.

This new option for alternative fuels could drastically expand supply and help the industry scale more quickly, which will be crucial to meeting climate targets. LanzaJet opened the first commercial alcohol-to-jet-fuel factory in Georgia in January 2024 and has buyers secured for all the fuel produced at that facility through 2034. British Airways, one of LanzaJet’s investors, will be a customer.

Key indicators Industry: Aviation fuels * Founded: 2020 * Headquarters: Deerfield, Illinois, USA * Notable fact:* LanzaJet spun out of LanzaTech, a company whose main technology uses microbes to convert waste materials into chemicals and fuels.

Potential for impactAlternative fuels still produce carbon dioxide and other greenhouse gases when they’re burned in a plane’s engine. The difference is that they typically remove some carbon from the atmosphere first. In this case the corn or sugarcane used to make the ethanol soaks up carbon dioxide as it grows. The result is that at least some of the emissions from flying can be considered offset by the process of making the fuel.

LanzaJet’s fuels could cut the climate impacts from burning fuel roughly in half, though the exact amount will depend on the source of alcohol used. The company’s sugarcane-derived ethanol could cut emissions by between 54% and 66%, according to the US Environmental Protection Agency, which certifies low-emissions fuels under the country’s Renewable Fuel Standard program.

The company plans to test out its new Georgia factory using corn-based fuels, though it’s only certified to sell sugarcane-based fuels in the US so far. LanzaJet is also partnering with its former owner, LanzaTech, to take materials like municipal solid waste and industrial waste gas and transform them into ethanol, which LanzaJet will then make into jet fuel. This pathway could result in jet fuel that’s 85% less polluting than fossil fuels, the company claims.

Caveats Scaling could present a major challenge for LanzaJet, as it does for the industry as a whole. Alternative jet fuels made up just 0.17% of all global aviation fuel used in 2023. LanzaJet’s goal is to produce a billion gallons of alternative jet fuels annually by 2030, significantly more than the roughly 160 million gallons produced by the entire alternative fuels industry last year. To achieve that, the company will need to build many large facilities, and do it quickly.

Cost is another major challenge for new fuels—on average, alternative jet fuels cost 2.8 times more than their fossil-fuel counterparts in 2023. Prices could come down as facilities scale, but fuel is a significant cost for airlines, making this a crucial consideration for future customers.

Experts also caution that fuels from biological sources still have environmental impacts. Those effects depend largely on the specific agricultural practices used to produce them. Clearing natural ecosystems to plant massive fields of single crops, for example, can on balance release more greenhouse gases into the atmosphere than those crops will ever capture. In the worst-case scenarios, some crop-based biofuels produce more emissions than fossil fuels. LanzaJet and other fuel makers will need to choose their source materials carefully and be transparent with regulators and the public about the effects of producing their products.

Next steps LanzaJet is working to validate and ramp up its first commercial facility, which the company hopes to have operating at full capacity by the end of 2024. Next, the company will begin building even larger facilities, including a 27-million-gallon-per-year facility in the UK in partnership with British Airways that should be operating by 2027.

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Rondo Energy is supplying cheap, zero-emissions heat to factories to replace fossil-fuel-powered boilers, furnaces, and kilns. Its approach of using bricks and iron wire to provide a steady supply of hot air or steam stands out for its simplicity and potential to scale.

Finding a clean way to produce the large amounts of heat required for industrial processes is one of the biggest unsolved climate challenges. Widely discussed solutions, like carbon capture and green hydrogen, still struggle to compete economically with burning coal or gas.

Rondo offers an alternative approach: stacks of bricks, heated by electricity generated from the wind and sun. Inside Rondo’s heat batteries, cheap renewable electricity heats iron wires similar to those in a toaster oven, which warms hundreds of tons of bricks to temperatures of up to 1,500 °C. With four to six hours of charging a day, those bricks can turn intermittent renewable power into a 24-7 heat source for industrial facilities.

Among startups trying to commercialize zero-emissions heat batteries, Rondo stands out for its simple approach. Competitors’ heat batteries often involve some kind of new technique or engineered material that’s a few steps away from any current industrial technology. But the heat-resistant bricks inside a Rondo heat battery are similar to those that have been used in high-temperature steelmaking for over a century, meaning they are already produced cheaply and at industrial scales. That sales pitch is resonating with investors, who have poured $85 million into the startup over the past two years.

Key indicators Industry: Energy storage * Founded: 2020 * Headquarters: Alameda, California, USA * Notable fact:* The company’s name pays homage to the musical term for a type of composition with a recurring theme. Cofounders John O’Donnell and Pete von Behrens previously worked in concentrating solar thermal power; Rondo is their second venture into thermal storage.

Potential for impactIndustrial production of stuff, from clothing and food to cement and fertilizer, is responsible for about a third of global greenhouse-gas emissions. Most of those emissions come from burning fossil fuels to generate heat in factories. If Rondo’s heat batteries prove cost-competitive at scale, they could help eliminate billions of tons of carbon emissions that would otherwise enter the atmosphere each year.

Caveats While heat-resistant bricks are a proven industrial technology, using them as zero-emissions heat batteries will require building more wind and solar plants to generate huge amounts of cheap renewable energy. Electricity reforms would also be needed in many parts of the US to make heat batteries cost-competitive with other forms of industrial heat. These might include allowing heat battery users to purchase cheap wholesale power from the grid during times of the day when renewable energy is abundant—something that isn’t possible today in jurisdictions that only sell power to industries at a fixed daily rate.

Next steps Rondo has a 2-megawatt-hour battery operating commercially at an ethanol plant in California. Its scale-up plans are ambitious: In partnership with Siam Cement Group, the company is already producing enough heat-resistant brick to store 2.4 gigawatt-hours of energy a year, which could power more than 200 American homes. It plans to boost production to 90 gigawatt-hours a year in the future.Between 2025 and 2027, recently announced customers in the food and beverage and chemical industriesare expected to start using versions of Rondo’s commercial heat batteries in industrial facilities.

Experts are looking forward to seeing how Rondo’s batteries perform over time, both in bigger installations and in very high-temperature applications like steel and cement making, which are considered among the most difficult processes to decarbonize.

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First Solar is expanding production of its thin-film solar cells and opening new factories to meet a surge of demand. Meanwhile, it’s investing in perovskites—tiny crystalline materials that many view as a key solar technology of the future.

The world needs more electricity than ever, as the AI boom puts intense demand on data centers and more heat waves increase the use of air-conditioning. To reduce emissions and keep global warming in check, a larger share of that electricity must come from renewables.

Much of the growth in renewables comes from solar. And First Solar is one of the largest manufacturers of solar panels in the US, which is the world’s second-largest solar market after China. The company is benefiting from US tariffs on foreign-made solar panels and tax credits made available through the Inflation Reduction Act.

Today, Chinese firms produce the vast majority of the world’s solar panels. Most build cells that incorporate a layer of silicon to absorb the sun’s light and awaken electrons within, which then flow out as current. Instead of silicon, First Solar’s cells rely on a thin film made from two other elements: cadmium and tellurium. These cells can be produced more quickly than silicon cells, using less energy and water.

But there’s still room for improvement in the cells’ performance. Today’s best silicon solar panels convert roughly 25% of the sun’s energy into electricity, and cadmium telluride tends to lag behind that. To boost efficiency, First Solar is now looking to incorporate a new class of materials called perovskites into its cells. These tiny crystals absorb different wavelengths of light from those absorbed by silicon or cadmium telluride. Cells that add perovskites to the mix—known as perovskite tandem solar cells—could potentially convert even more of the sun’s energy into electricity.

First Solar is among a handful of companies exploring how to layer these crystals into commercial solar cells to improve performance. Last year it acquired a firm called Evolar, a leader in thin-film and perovskite research, to further this aim.

Key indicators Industry: Renewable energy * Founded: 1999 * Headquarters: Tempe, Arizona, USA * Notable fact:* First Solar’s backlog of orders totals 76 gigawatts and stretches out to 2030.

Potential for impactGlobally, solar energy accounted for more than three times as much new capacity for electricity generation as wind in 2023, according to the International Energy Agency. There are a few reasons why—the price of panels has dropped dramatically in the past 20 years as production ramped up, and they’re relatively easy to install and maintain.

Solar’s future looks just as bright—global solar capacity is expected to reach nearly 2,000 terawatt-hours this year, and the IEA says we could see it quadruple by the end of the decade. In the US, First Solar’s expanding production and its recent investments into perovskites will shape the solar market for years to come.

Caveats One of the biggest obstacles to bringing more utility-scale solar plants online in the US is hooking these projects up to the grid once they’re built. The federal agency that approves grid interconnections has a backlog of requests. Right now it takes about five years, on average, for a new solar plant to open. Recent reforms aim to make this process faster, but their impact is still unclear.

Compounding this problem is a shortage of transformers, which step the voltage of electricity up or down; these are crucial to managing the flow of clean energy across the grid. And there are siting challenges, since developers must obtain permits and some community groups oppose large installations. First Solar’s customers are overwhelmingly based in the US and include developers of new solar projects that face all these issues, which could limit the company’s growth.

The fate of the US solar industry is strongly influenced by domestic policy, and the US presidential election could affect First Solar’s expansion plans in a few ways (even if tax credits to US manufacturers have enjoyed broad bipartisan support). Though it seems unlikely that the IRA would be repealed, it’s possible that a new administration could amend parts of it.

The new president could impose higher tariffs and place more restrictions on imports. First Solar has publicly supported such tariffs—which critics blame for the high price of US panels. Or the president could lower tariffs and decrease import restrictions. Uncertainty on policy matters could make developers less willing to place new orders until a new administration is in place.

And there’s no guarantee that the company can make tandem cells work. Perovskites are notoriously unstable and break down in the sun—rather inconvenient for a solar material. First Solar will need to find new ways to produce and package them at scale, and prove to customers that these panels will work reliably for years once installed.

Finally, though First Solar’s panels avoid concerns about forced labor in the supply chain for silicon produced in China, such problems have also occurred in the company’s own supply chain.

Next stepsLater this year, First Solar will begin producing miniature versions of tandem solar panels at a factory in Ohio. If these panels perform well in tests, the company will manufacture full-size prototypes at its new R&D center nearby.

Meanwhile, First Solar is building new manufacturing facilities to expand production of its cadmium telluride panels. The company opened its first factory in India earlier this year and now manufactures in four countries—India, the US, Malaysia, and Vietnam.

In the US, First Solar just opened a new plant in Alabama, with another to follow in Louisiana in 2025. By 2027, the company expects to have more than 25 gigawatts of annual manufacturing capacity—more than the total capacity of new utility-scale US solar installed last year.

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Pano AI is helping communities spot fires faster, enabling firefighters to put out small blazes before they grow into infernos.

The four-year-old startup installs networks of rotating cameras in high vantage points throughout forests, grasslands, and other areas with high fire risk. Each station can capture ultra-high-definition video within a 10-mile radius, as well as infrared readings that can spot temperature fluctuations at night or through smoke.

Pano then uses its deep-learning systems to detect smoke or other signs of fire across these territories. Whenever they spot one, human analysts are available to review the images to confirm that a fire has broken out or reject false positives.

When blazes are confirmed, Pano alerts fire monitoring agencies, providing images and location data that help them respond quickly.

As firefighters battle the blaze, the company continues to provide up-to-date, highly zoomable images of the shifting conditions, along with satellite imagery, weather information, and additional data feeds assembled from other sources.

Key indicators Industry: Wildfire detection * Founded: 2020 * Headquarters: San Francisco, California, USA * Notable fact:* Pano is helping several agencies monitor and control flames that wildfire specialists intentionally set to clear out brush and reduce risks in forests and grasslands, standing ready to send the alert if the fire should break out beyond the designated boundaries.

Potential for impactThe risks of devastating wildfires are growing, in part because we continue to build communities on the edge of wildlands, many of which we’ve allowed to become overgrown. Meanwhile, climate change is also making many areas hotter and drier, turning trees, shrubs, and grasses into kindling.

As the economic and human toll of fire rises, it’s become increasingly critical to develop better ways to prevent or extinguish them before they turn into conflagrations.

Typically, emergency responders rely on people to spot smoke or fires and report them. But in the time it takes agencies to verify those reports, tiny fires can grow into massive blazes that become far more destructive and much harder to put out.

The promise of Pano is that it can dramatically shorten that response time by spotting, confirming, and pinpointing the location of fires that might not be visible to humans for hours, because they are in remote areas or below tree cover, or ignited at night. That should reduce the number of uncontrollable fires as well as the death and damage they cause.

The company says that the real-time information it provides also helps fire departments combat the flames in safer and more effective ways.

The company points to a number of case studies where its tools have helped to accelerate coordinated responses and contain wildfires. For instance, in the summer of 2023, Pano alerted Washington’s state fire division to the Jackson Road Fire, near Olympia. The response time was shortened by at least 20 minutes.

Firefighters still spent about a week battling the flames. But they restricted the blaze to 23 acres even as wind conditions worsened, and prevented any deaths and damage to structures.

CaveatsPano certainly didn’t invent the idea that cameras and computer software would be helpful in spotting and responding to fires. The ALERTCalifornia program has been leveraging similar technology for the same purpose for years. Other startups are also using sensors, satellites, cameras, and AI to improve wildfire detection, including Dryad and Robotics Cats.

It’s still hard to say just how effective these tools will be, given continually shifting climate conditions and the many other measures that governments, utilities, and additional wildfire tech startups are now taking to reduce risks.

Next stepsBut Pano has emerged as a clear leader in early fire detection. The startup has already deployed its cameras in nine states throughout the western US, including California, Oregon, Washington, and Colorado. It’s also set up stations in parts of Canada and Australia.

Pano AI’s customers include government agencies, power utilities, private forest owners, and ski resorts. It charges $50,000 per year as an all-in fee, covering its camera stations as well as software, maintenance, notifications, and services.

The company says its systems now monitor nearly 20 million acres around the world and have spotted almost 100,000 fires.

As heighted fire risk spreads to more regions and awareness of the danger grows, the company says, it’s also having more conversations with agencies from the Midwest, East Coast, and other areas where wildfire hasn’t traditionally been as much of a concern.

The company says that its effectiveness will only improve as its cameras monitor more areas around the world and its machine-learning systems get better at spotting the earliest signs of fire.

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Ceibo seeks to eliminate a major potential speed bump for the clean-energy transition: the looming global copper shortage. The firm’s low-impact extraction technology targets ores that aren’t economical to mine today but could help meet the copper demands of an electrified world.

Copper wires form the backbone of the clean-energy economy, connecting cars, buildings, and factories to the grid. Copper is also essential to solar panels, wind turbines, and EVs. Demand for the metal in these and other cleantech applications is expected to nearly triple by 2040. But much of the copper that remains in the ground is locked up in low-grade ores that aren’t economical to mine. The mining technology company Ceibo hopes to change that.

Today, about 20% of the world’s copper is produced from copper oxide ores. Copper is extracted by crushing the rock, placing it in a giant pile, and spraying it with dilute acid. As acid percolates through the rock, the copper dissolves and leaches out.

The remaining 80% of the world’s copper comes from copper sulfide ores, which don’t dissolve well in acid. To extract that copper, the industry uses a more energy- and water-intensive process that involves concentrating the metal in vats of chemicals before smelting it at high temperatures.

Ceibo is tweaking the lower-impact leaching process so that it works on copper sulfides. The company’s chemistry-based approach mimics the way naturally occurring microbial communities liberate copper from sulfide ores, but at an accelerated pace. By altering conditions within the rock pile, including pH and oxidation state, Ceibo’s tech makes it possible to recover more than 70% of the copper. Companies that are already mining copper oxides can plug the firm’s tech into their existing infrastructure without costly retrofits.

Ceibo is in the process of testing its technology in partnership with key players in the mining industry. The firm has also raised $36 million from clean-energy and mining financiers, part of a growing trend of investment in startups seeking to process copper sulfides with leaching. Among those startups, Ceibo stands out for being headquartered in Chile, the world’s largest copper producer. This could give the firm a home field advantage as it seeks to build partnerships with major industry players and rapidly scale its technology.

Key indicators Industry: Mining * Founded: 2021 * Headquarters: Santiago, Chile * Notable fact:* Ceibo got its start offering dust suppression services to copper miners under a different name, Aguamarina. Dust pollution is a major challenge for the copper industry.

Potential for impactWhile the cleantech sector’s appetite for copper is expected to surge, the mining sector isn’t keeping pace. With many of the best-quality ore deposits already exhausted, analysts predict a potential copper shortfall of more than 10 millions tons a year by 2040.

Liberating the potentially vast quantities of copper tied up in sulfide ores that aren’t economical to mine today may be key to closing the copper supply gap. Ceibo is aiming to produce a million tons of copper annually within the next 10 years, with further expansion in the future. At such scales, Ceibo’s relatively low-impact approach to copper processing could help clean up the industry.

CaveatsThe idea of using acid to leach sulfide ores isn’t new; researchers have been trying to develop a scalable, cost-effective way to do so for decades. The problem is so well known that industry insiders sometimes refer to it as the Holy Grail of copper mining.

Environmental variability is a key challenge. A company might develop a method that works well for one particular ore type but fails when applied elsewhere. Ceibo is developing a process that the company says is flexible by design, using a mix of proprietary chemical reagents and geochemical modeling to adjust to conditions on the ground. But the firm still has to demonstrate that its technology can help miners extract copper efficiently at commercial scales in a broad spectrum of geologic and environmental conditions.

Next stepsSo far, Ceibo has focused on proving its process in laboratory settings. To date, in partnership with mining companies, it has tested the performance of its technology on more than 20 ores. Later this year, Ceibo aims to begin running its first on-site pilot tests.

While much of Ceibo’s initial work has taken place in Chile, the firm recently opened a US office to gain a greater foothold in the North American market, which is being buoyed by the Biden administration’s efforts to expand domestic supply chains for critical minerals.

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Sun King is helping poor households across Asia and Africa access reliable, clean power and healthier ways of cooking.

Accessing clean sources of energy has always been a challenge for low-income communities worldwide, given the high up-front costs. At least hundreds of millions of people around the world have unreliable or no access to the electricity grid, forcing many of them to spend as much as 10% of their incomes on dirty fuels—like kerosene and diesel—that harm both their health and the environment.

One work-around for this challenge is to allow households to pay for clean energy in small, affordable amounts as they use it.

This is what Sun King has been able to deliver. By providing solar panels, handheld solar-powered lamps, batteries, and home systems that power lights and devices to communities in sub-Saharan Africa and Asia, it says, it offers reliable renewable electricity to some 40 million people. Its pay-as-you-go business model allows households to spend as little as $0.15 per day.

Now, having acquired PayGo Energy in 2023, Sun King is expanding its product portfolio into clean cooking.

PayGo’s stoves run on liquefied petroleum gas, which produces less of the health-damaging and climate-warming pollution generated by charcoal, biomass, and similar fuels used to heat basic stoves in many homes. The household costs for the stoves and fuel are subsidized by carbon credits that the company earns for reducing greenhouse-gas emissions, through a voluntary carbon offsets program.

Crucially, PayGo has earned high marks from academic experts for developing household cookstoves that reliably reduce indoor air pollution and climate emissions. Sun King says it’s also developing other cooking appliances, like pressure cookers, that could run on the renewable electricity it provides.

Key indicators Industry: Renewable energy * Founded: 2008 * Headquarters: Nairobi, Kenya * Notable fact:* Sun King supplies solar products to more than 40 million people in 10 African and two Asian countries.

Potential for impactSun King’s whole range of product lines helps cut the emissions driving climate change.

For instance, it has already sold 23 million solar products to previous users of kerosene lamps, each of which can pump out around a ton of carbon dioxide a decade.

And by reducing the need to collect biomass to produce household light, heat, or fuel for cooking, the company can help reduce deforestation as well as the emissions that occur from burning plant matter.

Cooking with wood and charcoal is a major contributor to global warming, responsible for approximately 2% of worldwide carbon emissions. The particulate pollution it releases also kills millions of people annually.

Voluntary carbon markets for clean cookstoves will only work if the programs are conducted in a transparent and credible manner; such programs have come under severe criticism for inflating the climate benefits of the appliances, in part by overestimating how much they’re actually used. But PayGo was among a few cookstove projects that researchers at the University of California, Berkeley, found did meet stringent quality criteria, in part by “metering” actual usage of cleaner replacement stoves. The stoves also use a fuel that meets World Health Organization health standards for indoor air pollution.

By operating in more rigorous ways, the company could help drive more investment in cookstove projects that actually make a difference for both public health and climate change.

Indeed, quality carbon credits—like those Sun King plans to release—have begun to fetch higher prices, in a market that has started to discriminate against inflated credits.

Caveats Even though Sun King and PayGo Energy adhere to very high standards in monitoring emissions, these approaches are not foolproof and may be flawed by inaccurate or overly generous assumptions.

And it may remain difficult to persuade many households to shift to cleaner stoves, depending on their specific needs, cultural practices, habits, and incomes.

Meanwhile, though providing off-grid solar power at a low up-front cost is a boon to low-income households in regions with spotty or overpriced electricity, these homes and communities will ideally be connected to large, clean, stable electricity grids in the future. That would ultimately provide the lower-cost, around-the-clock electricity needed to power businesses and create local jobs.

Next steps Sun King is now conducting a pilot initiative with a thousand households across Kenya, to introduce its next-generation clean cookstoves. The company also launched its first dedicated cookstove shop in the same country, known as EasyCook, in July.

Meanwhile, Sun King continues to improve its solar products and market reach. It has begun rolling out a new home system that delivers increased energy output at a lower retail price, and it launched operations in South Africa and Cameroon this year.

As the cost of solar panels and batteries continue to fall, Sun King’s products are becoming increasingly competitive with traditional grid electricity, offering consumers across growing parts of Africa and Asia cleaner, cheaper, and often more reliable energy.

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Pivot Bio is using genetically edited microbes to deliver just the right amount of nitrogen to crops, cutting climate emissions without reducing agricultural yields.

The development of synthetic fertilizer was one of the great achievements of the last century, providing an abundant source of nitrogen that boosted crop yields and helped feed a growing global population.

But the product is also a climate and environmental disaster. The production process releases huge amounts of carbon dioxide, and after it’s applied to fields it releases nitrous oxide, a far more powerful greenhouse gas. Synthetic fertilizer contributes about 5% of worldwide climate emissions and pollutes groundwater, lakes, and rivers.

Pivot Bio, a biotechnology company based in Berkeley, California, is harnessing microbes to deliver a usable form of nitrogen directly to the roots of crops, reducing the amount of synthetic fertilizer farmers need to use and the pollution that comes with it.

Nitrogen is an essential ingredient for photosynthesis, but most plants can’t directly absorb it from the air. Fertilizer manufacturers help them along by breaking down the strong triple bonds between nitrogen molecules and combining those molecules with hydrogen to form ammonia. After it’s applied in fields, much of the fertilizer turns into ammonium and nitrate, nitrogen-rich compounds that plants can take up and use to grow.

Certain bacteria and other microorganisms in soil pull off a similar trick naturally, if not as consistently. Pivot is putting a modern twist on this natural process, genetically engineering select microbes to increase the amount of nitrogen they deliver to the roots of plants over the growing season.

More and more farmers are putting it to use in their fields. The company’s products were applied to 5 million acres last year, up from 1 million two years earlier.

Key indicators Industry: Food and agriculture * Founded: 2011 * Headquarters: Berkeley, California, USA * Notable fact:* Pivot Bio says its products can replace 40 pounds of synthetic fertilizer per acre. US corn farmers generally apply about 150 to 220 pounds of fertilizer per acre every year, depending on the variety and hoped-for yield.

Potential for impactPivot sells the microbes as a seed coating or as a liquid that farmers can apply in furrows at the time of planting.

The company says the current version of its main product, designed for corn, can replace about 25% of the synthetic fertilizer normally used, without reducing crop output. The company has also developed nitrogen-delivering microbes tailored for wheat, sorghum, and other small grains, all selling for around or below the price of traditional fertilizer. Pivot adds that farmers have applied its products to more than 10 million acres (if you count repeated uses), nearly all in the US so far.

Pivot says that while generating a million tons of ammonia as fertilizer produces 2.6 million metric tons of carbon dioxide, manufacturing the microbes needed to deliver a million tons of nitrogen in the field produces only about 35,000 tons of emissions. The company estimates that its customers have cut emissions by the equivalent of more than 900,000 tons since the start of 2022. About 78% of that reduction occurred just last year, though the company says some of that increase was due to improved data collection.

A handful of academic studies have backed up the company’s claims that its products can reduce fertilizer use and emissions without lowering crop yields.

CaveatsSome farmers have reported mixed results in their fields, and Pivot’s products don’t necessarily increase yields over what’s possible with standard fertilizer use. That isn’t necessary for the company to make the case that it can help the climate—but it would make Pivot an easier sell to farmers.

Many are loath to cut down their use of synthetic fertilizer, a tried-and-true product, unless new policies require them to do so or pollution-cutting products promise to boost productivity as well.

The other obvious challenge with Pivot’s approach is that it’s not a complete solution to synthetic fertilizer pollution, since it can replace only a fraction of that fertilizer.

Next stepsBut it’s a big fraction in an industry that’s notoriously challenging to clean up, and one that’s set to grow.

Chris Abbott, the company’s CEO, stresses that Pivot can save farmers money, since its products are cost competitive with synthetic fertilizer but will more reliably deliver nitrogen that actually translates to plant growth.

The company expects that its next generation of microbes, scheduled to be ready for the 2026 US planting season, will be 25% more effective at generating nitrogen at the roots of crops. With future improvements, Abbott believes, the products will eventually be capable of replacing as much as half the synthetic fertilizer in fields, with crop yields the same or better.

If Pivot nears that goal and continues to win over farmers, it could begin to meaningfully reduce one of agriculture’s biggest sources of climate pollution.

Explore the 2024 list of 15 Climate Tech Companies to Watch.

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Large swaths of the global economy are nearly impossible to electrify but could run on low-emissions hydrogen, helping the world transition away from fossil fuels. Electric Hydrogen is working toward more efficient, affordable production of green hydrogen.

Electric Hydrogen is striving to develop production methods that make it easier and more affordable to generate huge amounts of green hydrogen.
Hydrogen has emerged as a promising alternative to fossil fuels for the transportation sector and as a feedstock in the production of steel, fertilizer, methanol, and other products.

But hydrogen production to date has been pretty dirty. The vast majority of hydrogen is produced from natural gas, emitting significant levels of planet-warming greenhouse gasses. It can also be generated by an electrolyzer, a device that uses electricity to split water molecules into hydrogen and oxygen. But most electrolyzers are small and expensive, and they consume lots of energy and water. Moreover, they typically rely on electrical grids that aren’t powered by predominantly clean energy.

Electric Hydrogen wants to address these issues by developing electrolyzers that have about 10 times the capacity of today’s standard devices while also being more affordable and efficient.

The company is already operating a pair of electrolyzer plants in California, including a one-megawatt facility in San Carlos and a 10-megawatt project in San Jose. In April, Electric Hydrogen opened an electrolyzer factory in Devens, Massachusetts, which will crank out its first line of 100-megawatt electrolyzers. The company also raised $380 million in funding in 2023 from backers including BP, United Airlines, and Microsoft, making it the first electrolyzer company to be valued at over $1 billion.

Key indicators Industry: Hydrogen * Founded: 2020 * Headquarters: Natick, Massachusetts, USA * Notable fact:* Two of the company’s three cofounders came from First Solar, a solar panel manufacturer that is also featured on this year’s list.

Potential for impactTo slow the pace of climate change, we need to drastically reduce our use of fossil fuels. Heavily polluting industries like fertilizer and chemical manufacturing are notoriously difficult to clean up. Fertilizer alone accounted for 2% of global emissions in 2022, according to a study published in Scientific Reports. It’s also tricky to eliminate emissions from certain types of transportation, including shipping and aviation, mainly because fuels can simply store more energy for a given weight than today’s batteries.

It’s these sectors where hydrogen shows the most promise, because it can be made into fuel that produces only water vapor as a by-product. But it’s hard to make clean hydrogen cost-competitive with fossil fuels.

CaveatsElectric Hydrogen will need to prove that its 100-megawatt electrolyzer systems can operate reliably at a low cost. To make low-emission hydrogen, the electrolyzers will need to use a lot of renewable energy, which may not always be available. In addition, Electric Hydrogen doesn’t share many details publicly about how its technology works, which makes it difficult to gauge the company’s claims and progress.

Next stepsThe good news is that the Inflation Reduction Act, signed into law by the Biden administration in 2022, provided generous subsidies aimed at accelerating US-based hydrogen production. Though the details of how exactly these tax credits will be awarded are still being worked out, Electric Hydrogen is poised to benefit greatly from them in the coming years, either directly or through cost reductions for its customers.

Meanwhile, Electric Hydrogen plans to send the Natick facility’s first electrolyzer systems to OCI, a clean methanol manufacturer in Beaumont, Texas, later this year. Full commercial operation of these systems is expected in 2025, and the methanol will likely be used for maritime shipping around Europe. The company is also hoping to build out its business in Europe and Australia within the next few years.

If these electrolyzers work as efficiently and affordably as hoped, it will mark a huge step toward the company’s goal of producing clean, affordable hydrogen.

Explore the 2024 list of 15 Climate Tech Companies to Watch.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The coolest thing about smart glasses is not the AR. It’s the AI.

In case you missed the memo, we are barreling toward the next big consumer device category: smart glasses. At its developer conference last week, Meta introduced a positively mind-blowing new set of augmented reality (AR) glasses dubbed Orion. Snap also unveiled its new Snap Spectacles last week. Back in June at Google IO, that company teased a pair, and Apple is rumored to be working on its own model as well.

After years of promise, AR specs are at last A Thing. But what’s really interesting about all this isn’t AR at all. It’s AI. Smart glasses enable you to seamlessly interact with AI as you go about your day. I think that’s going to be a lot more useful than viewing digital objects in physical spaces. Put more simply: it’s not about the visual effects, it’s about the brains.Read the full story.

—Mat Honan

This story is from the very first edition of The Debrief, MIT Technology Review’s new newsletter. It provides a weekly take on the tech news that really matters, and links to stories we love—as well as the occasional recommendation.

Sign up to receive it in your inbox every Friday, and to get ahead with the real story behind the biggest news in tech.

Why bigger is not always better in AI

In AI research, everyone seems to think that bigger is better. The idea is that more data, more computing power, and more parameters will lead to models that are more powerful.

But with scale come a slew of problems, such as invasive data-gathering practices and child sexual abuse material in data sets. To top it off, bigger models also have a far bigger carbon footprint, because they require more energy to run.

It doesn’t have to be like this. Researchers at the Allen Institute for Artificial Intelligence have built an open-source family of models which achieve impressive performance with a fraction of the resources used to build state-of-the-art models. Read more about why it’s a big achievement.

—Melissa Heikkilä

This story is from Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Robotaxi Cruise has been slapped with a $1.5 million fine
It failed to properly report one of its cars hitting and seriously injuring a pedestrian. (NYT $)
+ The company was also required to hand over data from other incidents (WP $)
+ What’s next for robotaxis. (MIT Technology Review)

2 Epic Games is suing Google and Samsung
The game maker claims the companies secretly colluded to squash competition. (WP $)
+ Epic has a checkered history in the courtroom. (WSJ $)

3 A chemical fire at a lab near Atlanta has unleashed chemicals into the airIn the wake of Hurricane Helene’s wreckage, too. (Vox)
+ The hurricane’s devastation has forced an essential chip mine to close. (Insider $)

4 Meta isn’t saying if it plans to train AI on smart glasses photos
It already trains its models on public US Instagram and Facebook posts. (TechCrunch)
+ Here’s what I made of Snap’s new augmented-reality Spectacles. (MIT Technology Review)

5 US public record systems are riddled with vulnerabilities
Attackers could infiltrate, meddle with or delete official documents. (Ars Technica)

6 Google wants to build AI data centers in AsiaAfter years of being overlooked, Southeast Asia is experiencing a data center boom. (Bloomberg $)
+ Malaysia is plotting new AI regulations, too. (Reuters)
+ Energy-hungry data centers are quietly moving into cities. (MIT Technology Review)

7 eBay is scrapping fees for UK sellersIn a bid to compete more directly with Vinted and Depop. (The Guardian)

8 The current state of AI artWhile a fraction is decent, the majority of it is garbage. (The Guardian)
+ Why artists are becoming less scared of AI. (MIT Technology Review)

9 Researchers are uncovering more about our shared history with animals
How did we get from one-celled microbes to multi-celled creatures? (Knowable Magazine)
+ Some fish can regrow injured tails. That could be good news for humans. (New Scientist $)

10 Robert Downey Jr’s new play is all about AIShould novelists be allowed to receive help from AI? Answers on a postcard. (The Atlantic $)
+ The play has been described as a ‘thought experiment.’ Hmm. (NYT $)

Quote of the day

“Without this, I would be speaking only to myself or into the air.”

—Zhang Xin, a software developer living in Wuhan, tells Rest of World why he has found solace in setting up virtual tombs for his late relatives.

The big story

Inside the messy ethics of making war with machines

August 2023In recent years, intelligent autonomous weapons have become a matter of serious concern. Giving an AI system the power to decide matters of life and death would radically change warfare forever.

And these systems have become sophisticated enough to raise novel questions—ones that are surprisingly tricky to answer. What does it mean when a decision is only part human and part machine? And when, if ever, is it ethical for that decision to be a decision to kill? Read the full story.

—Arthur Holland Michel

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Good news: the northern bald ibis is back from the brink of extinction!
• New York City’s Sushi-Con sounded absolutely bonkers—complete with the star of the show, a 400lb tuna. ($)
• The new Nosferatu trailer looks suitably intense.
• Kick back and relax—here’s how to host the perfect LAN party.

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In AI research, everyone seems to think that bigger is better. The idea is that more data, more computing power, and more parameters will lead to models that are more powerful. This thinking started with a landmark paper from 2017, in which Google researchers introduced the transformer architecture underpinning today’s language model boom and helped embed the “scale is all you need” mindset into the AI community. Today, big tech companies seem to be competing over scale above everything else.

“It’s like, how big is your model, bro?” says Sasha Luccioni, the AI and climate lead at the AI startup Hugging Face. Tech companies just add billions more parameters, which means an average person couldn’t download the models and tinker with them, even if they were open-source (which they mostly aren’t). The AI models of today are just “way too big,” she says.

With scale come a slew of problems, such as invasive data-gathering practices and child sexual abuse material in data sets, as Luccioni and coauthors detail in a new paper. To top it off, bigger models also have a far bigger carbon footprint, because they require more energy to run.

Another problem that scale brings is the extreme concentration of power, says Luccioni. Scaling up costs tons of money, and only elite researchers working in Big Tech have the resources to build and operate models like that.

“There’s this bottleneck that’s created by a very small number of rich and powerful companies who use AI as part of their core product,” she says.

It doesn’t have to be like this. I just published a story on a new multimodal large language model that is small but mighty. Researchers at the Allen Institute for Artificial Intelligence (Ai2) built an open-source family of models called Molmo, which achieve impressive performance with a fraction of the resources used to build state-of-the-art models.

The organization claims that its biggest Molmo model, which has 72 billion parameters, outperforms OpenAI’s GPT-4o, which is estimated to have over a trillion parameters, in tests that measure things like understanding images, charts, and documents.

Meanwhile, Ai2 says a smaller Molmo model, with 7 billion parameters, comes close to OpenAI’s state-of-the-art model in performance, an achievement it ascribes to vastly more efficient data collection and training methods. Read more about it from me here. Molmo shows we don’t need massive data sets and massive models that take tons of money and energy to train.

Breaking out of the “scale is all you need” mindset was one of the biggest challenges for the researchers who built Molmo, says Ani Kembhavi, a senior director of research at Ai2.

“When we started this project, we were like, we have to think completely out of the box, because there has to be a better way to train models,” he says. The team wanted to prove that open models can be as powerful as closed, proprietary ones, and that required them to build models that were accessible and didn’t cost millions of dollars to train.

Molmo shows that “less is more, small is big, open [is as good as] closed,” Kembhavi says.

There’s another good case for scaling down. Bigger models tend to be able to do a wider range of things than end users actually need, says Luccioni.

“Most of the time, you don’t need a model that does everything. You need a model that does a specific task that you want it to do. And for that, bigger models are not necessarily better,” she says.

Instead, we need to change the ways we measure AI performance to focus on things that actually matter, says Luccioni. For example, in a cancer detection algorithm, instead of using a model that can do all sorts of things and is trained on the internet, perhaps we should be prioritizing factors such as accuracy, privacy, or whether the model is trained on data that you can trust, she says.

But that would require a higher level of transparency than is currently the norm in AI. Researchers don’t really know how or why their models do what they do, and don’t even really have a grasp of what goes into their data sets. Scaling is a popular technique because researchers have found that throwing more stuff at models seems to make them perform better. The research community and companies need to shift the incentives so that tech companies will be required to be more mindful and transparent about what goes into their models, and help us do more with less.

“You don’t need to assume [AI models] are a magic box and going to solve all your issues,” she says.

Now read the rest of The AlgorithmDeeper LearningAn AI script editor could help decide what films get made in Hollywood

Every day across Hollywood, scores of people read through scripts on behalf of studios, trying to find the diamonds in the rough among the many thousands sent in every year. Each script runs up to 150 pages, and it can take half a day to read one and write up a summary. With only about 50 of these scripts selling in a given year, readers are trained to be ruthless.

Lights, camera, AI: Now the tech company Cinelytic, which works with major studios like Warner Bros. and Sony Pictures, aims to offer script feedback with generative AI. It launched a new tool called Callaia that analyzes scripts. Using AI, it takes Callaia less than a minute to write its own “coverage,” which includes a synopsis, a list of comparable films, grades for areas like dialogue and originality, and actor recommendations. Read more from James O’Donnell here.

Bits and BytesCalifornia’s governor has vetoed the state’s sweeping AI legislation
Governor Gavin Newsom vetoed SB 1047, a bill that required pre-deployment safety testing of large AI systems, and gave the state’s attorney general the right to sue AI companies for serious harm. He said he thought the bill focused too much on the largest models without considering broader harms and risks. Critics of AI’s rapid growth have expressed dismay at the decision. (The New York Times)

Sorry, AI won’t “fix” climate change
OpenAI’s CEO Sam Altman claims AI will deliver an “Intelligence Age,” unleashing “unimaginable” prosperity and “astounding triumphs” like “fixing the climate.” But tech breakthroughs alone can’t solve global warming. In fact, as it stands, AI is making the problem much worse. (MIT Technology Review)

How turning OpenAI into a real business is tearing it apart
In yet another organizational shakeup, the startup lost its CTO Mira Murati and other senior leaders. OpenAI is riddled with chaos that stems from its CEO’s push to transform it from a nonprofit research lab into a for-profit organization. Insiders say this shift has “corrupted” the company’s culture. (The Wall Street Journal)

Why Microsoft made a deal to help restart Three Mile Island
A once-shuttered nuclear plant could soon be used to power Microsoft’s massive investment in AI development. (MIT Technology Review)

OpenAI released its advanced voice mode to more people. Here’s how to get it.
The company says the updated version responds to your emotions and tone of voice, and allows you to interrupt it midsentence. (MIT Technology Review)

The FTC is cracking down on AI scams
The agency launched “Operation AI Comply” and says it will investigate AI-infused frauds and other types of deception, such as chatbots giving “legal advice,” AI tools that let people create fake online reviews, and false claims of huge earnings from AI-powered business opportunities.
(The FTC)

Want AI that flags hateful content? Build it.
A new competition promises $10,000 in prizes to anyone who can track hateful images online. (MIT Technology Review)

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This article is from The Debrief with Mat Honan, MIT Technology Review’s weekly newsletter from its editor in chief. To receive it every Friday, sign up here.

In case you missed the memo, we are barreling toward the next big consumer device category: smart glasses. At its developer conference this week, Meta (née Facebook) introduced a positively mind-blowing new set of augmented reality (AR) glasses dubbed Orion. Snap unveiled its new Snap Spectacles last week. Back in June at Google IO, that company teased a pair. Apple is rumored to be working on its own model as well. Phew.

Both Meta and Snap have now put their glasses in the hands of (or maybe on the faces of) reporters. And both have proved that after years of promise, AR specs are at last A Thing. But what’s really interesting about all this to me isn’t AR at all. It’s AI.

Take Meta’s new glasses. They are still just a prototype, as the cost to build them—reportedly $10,000—is so high. But the company showed them off anyway this week, awing basically everyone who got to try them out. The holographic functions look very cool. The gesture controls also appear to function really well. And possibly best of all, they look more or less like normal, if chunky, glasses. (Caveat that I may have a different definition of normal-looking glasses than most people. ) If you want to learn more about their features, Alex Heath has a great hands-on writeup in The Verge.

But what’s so intriguing to me about all this is the way smart glasses enable you to seamlessly interact with AI as you go about your day. I think that’s going to be a lot more useful than viewing digital objects in physical spaces. Put more simply: it’s not about the visual effects, it’s about the brains.

Today if you want to ask a question of ChatGPT or Google’s Gemini or what have you, you pretty much have to use your phone or laptop to do it. Sure, you can use your voice, but it still needs that device as an anchor. That’s especially true if you have a question about something you see—you’re going to need the smartphone camera for that. Meta has already pulled ahead here by letting people interact with its AI via its Ray-Ban Meta smart glasses. It’s liberating to be freed from the tether of the screen. Frankly, staring at a screen kinda sucks.

That’s why when I tried Snap’s new Spectacles a couple of weeks ago, I was less taken by the ability to simulate a golf green in the living room than I was with the way I could look out on the horizon, ask Snap’s AI agent about the tall ship I saw in the distance, and have it not only identify it but give me a brief description of it. Similarly, in The Verge Heath notes that the most impressive part of Meta’s Orion demo was when he looked at a set of ingredients and the glasses told him what they were and how to make a smoothie out of them.

The killer feature of Orion or other glasses won’t be AR ping-pong games—batting an invisible ball around with the palm of your hand is just goofy. But the ability to use multimodal AI to better understand, interact with, and just get more out of the world around you without getting sucked into a screen? That’s amazing.

And really, that’s always been the appeal. At least to me. Back in 2013, when I was writing about Google Glass, what was most revolutionary about that extremely nascent face computer was its ability to offer up relevant, contextual information using Google Now (at the time the company’s answer to Apple’s Siri) in a way that bypassed my phone.

While I had mixed feelings about Glass overall, I argued, “You are so going to love Google Now for your face.” I still think that’s true.

Assistants that help you accomplish things in the world, without having to be given complicated instructions or interfacing with a screen at all, are going to usher in a new wave of computing. While Google’s Project Astra demo, a still unreleased AI agent that it showed off this summer, was wild on a phone, it was not until Astra ran on a pair of smart glasses that things really fired up.

Years ago, I had a spox from Magic Leap, an early company working on AR headsets, try to convince me that leaving virtual objects, like a digital bouquet of flowers, around in physical spaces for others to find would be cool. Okay… sure. And yeah, Pokemon Go was hugely popular. But it has taken generative AI, not AR gimmicks, to really make smart glasses make sense.

Multimodal AI that can understand speech, video, images, and text, combined with glasses that let it see what you see and hear what you hear, will redefine the way we interact with the world every bit as much as the smartphone did.

Finally, a weird aside: Orion was the great huntsman of Greek mythology. (And of course, is the constellation you see up in the sky.) There are lots of versions of his story, but a common one is that the king of Chios blinded him after Orion drunkenly raped the king’s daughter. He eventually regained his vision by looking into the rising sun.

It’s a dramatic story, but maybe not the best product name for a pair of glasses.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The UK is done with coal. How’s the rest of the world doing?

The UK is shutting down its final coal-fired power plant today, marking the end of an era for the country’s energy system. Once the backbone of the grid, coal has been steadily replaced with other, less polluting energy sources.

It’s a major milestone for the notoriously polluting fossil fuel. But coal is still booming in other parts of the world, especially in some larger countries where electricity demand is growing quickly. Read the full story.

—Casey Crownhart

Sorry, AI won’t “fix” climate change

In an essay last week, Sam Altman, the CEO of OpenAI, argued that the accelerating capabilities of AI will usher in an idyllic “Intelligence Age,” unleashing “unimaginable” prosperity and “astounding triumphs” like “fixing the climate.”

It’s a promise that no one is in a position to make—and one that, when it comes to the topic of climate change, fundamentally misunderstands the nature of the problem.

To be sure, AI may help the world address the rising dangers of climate change. But technological advances are just the start—necessary but far from sufficient to eliminate the world’s climate emissions.Read the full story.

—James Temple

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 California’s governor has vetoed its landmark AI safety bill
Gavin Newsom felt the bill could give the public a false sense of security. (LA Times $)
+ The legislation was the most ambitious attempt at curtailing AI’s growth to date. (NYT $)
+ It’s a win for Big Tech and a big step backwards for AI safety champions. (WP $)

2 The International Space Station has sprung a leak
And no one’s sure why. (Ars Technica)
+ SpaceX has launched a rescue mission to retrieve astronauts stranded onboard. (AP News)

3 US defense tech startups are on the hunt for new suppliers
They’ve long relied on China for materials. Now they’re being forced to go elsewhere. (WSJ $)
+ Meanwhile, China is warning native companies not to buy Nvidia’s chips. (Bloomberg $)
+ Here’s the defense tech at the center of US aid to Israel, Ukraine, and Taiwan. (MIT Technology Review)
4 Apple must turn over 1.3 million documents todayThe judge in its lawsuit vs Epic Games denied Apple’s request for extra time. (The Verge)+ Spare a thought for the Apple staffers working over the weekend to meet the deadline. (TechCrunch)

5 How Big Tech gatekeeps access to its anti-terror guidance
The organization blocked TikTok and PornHub’s parent company’s applications to join. (Wired $)

6 AI tools are making code cheaper
What this means in the long term for developers is still unclear. (Economist $)
+ How AI assistants are already changing the way code gets made. (MIT Technology Review) 7 Things aren’t looking great for 23andMeHow useful is collecting all that personal information, really? (The Atlantic $)

8 Beware the anti-woke tech bro
Their narrow world views are cries for help. (Vox)

9 Parents in Silicon Valley really love this YouTuber
Former NASA engineer Mark Rober’s videos are smart enough to justify the screen time. (The Information $)

10 Why dating apps are pivoting to friendship
Make it last forever, friendship never ends. (FT $)

Quote of the day

“If you are not throwing soup at Midjourney paintings or gluing yourself to strawberries, you are not doing it right.”

—AI researcher Joscha Bach jokes about the California AI safety bill in a post on X.

The big story

I took an international trip with my frozen eggs to learn about the fertility industry

September 2022 —Anna Louie Sussman

Like me, my eggs were flying economy class. They were ensconced in a cryogenic storage flask packed into a metal suitcase next to Paolo, the courier overseeing their passage from a fertility clinic in Bologna, Italy, to the clinic in Madrid, Spain, where I would be undergoing in vitro fertilization.

The shipping of gametes and embryos around the world is a growing part of a booming global fertility sector. As people have children later in life, the need for fertility treatment increases each year.

After paying for storage costs for six and four years, respectively, at 40 I was ready to try to get pregnant. Transporting the Bolognese batch served to literally put all my eggs in one basket. Read the full story.

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Rest in power the great Kris Kristofferson, a true giant of country music.
• Do you vibe? It’s yet more evidence that TikTok fitness trends are pretty spurious.
• New York in the Seventies looked pretty wild, I must say.
• Just in time for October—why do we see ghosts? Well, there’s a few explanations…

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The UK is shutting down its final coal-fired power plant today, marking the end of an era for the country’s energy system. Once the backbone of the grid, coal has been steadily replaced with other, less polluting energy sources.

Coal is the most emissions-intensive fuel powering the grid today, and moving away from it, even to other fossil fuels, can help reduce climate pollution. Some countries have started to replace the fuel in earnest—members of the G7, a group of wealthy economies, have all agreed to phase out coal-fired power plants that don’t use carbon capture by 2035. But coal is still booming in other parts of the world, especially in some larger countries where electricity demand is growing quickly.

A tale of two coal declinesThe power plant, scheduled to shut down at midnight on September 30, is called Ratcliffe-on-Soar, and it’s the last bastion of coal in the UK, where the fuel has a rich history. The country relied on coal for over 100 years, and until 1990, it made up the lion’s share of electricity generated there.

Since then, the UK has seen two major waves of cutting down coal. The first came in the 1990s, when coal went from around 65% of electricity supply to roughly 35%, and there was a series of mine closures across the country. Coal was largely replaced by natural gas, which was becoming more widely available and beat out coal on economics, says Joel Jaeger, a senior research associate at the World Resources Institute.

Then, roughly a decade ago, came a second wave of coal retirements. This time it was driven in part by policy: The European Union (which the UK belonged to at the time) had set a price on carbon, and the UK implemented an even higher one in 2013. That made coal even less economical an option, Jaeger says. In the 2010s, renewables (mostly wind and bioenergy) were quickly ramped up to replace most of the remaining coal infrastructure.

Of the countries that have phased out coal the fastest, the UK has made the most impressive transformation, Jaeger says, since the country has totally wiped it from the grid. Others with speedy transformations include Portugal, which reached zero coal in late 2021, and Greece, where coal went from supplying over half the electricity in 2014 to less than 10% as of 2023. Denmark has also quickly ramped down the fuel and, unlike other countries with quick transitions, replaced it almost entirely with renewables rather than natural gas.

A natural transitionThe US is the largest nation among those that have moved away from coal the fastest, Jaeger says. It’s been more of a steady change than what happened in the UK—coal has dropped from contributing over 50% of electricity to 20% over the last four decades.

Much of the shift was a response to the growing availability of natural gas in the US—the fracking boom beginning in the mid-2000s made it more domestically accessible and less expensive, Jaeger says. In more recent years, pollution standards for coal plants have slowly tightened and the fleet has aged, he adds, making the plants more expensive to run and causing more of them to be retired.

More recently, the US has seen renewables like wind and solar coming onto the grid, and tax credits have helped make them cheaper, pushing more older coal plants to shut down. The US is one of the G7 countries that have agreed to reach zero unabated coal power by 2035.

Germany has also roughly halved its coal use in the past decade, and it’s replaced the fuel mostly with renewables rather than natural gas. The country has simultaneously been shutting down nuclear power plants, sunsetting the last one in the country in April 2023. Some critics argue that this has slowed the move away from coal.

Where coal is still kingEven as many nations, especially in Europe and North America, move away from coal, the fuel is still booming in other parts of the world. Energy demand globally is increasing, and coal has consistently been the world’s biggest power source, meeting about 35% of demand.

Nowhere shows this trend better than China. While most of the countries mentioned so far (the UK, Germany, the US, Greece, Denmark) have had either steady or decreasing electricity demand since 2005, China’s grid has expanded dramatically.

Total Chinese electricity demand was roughly 400 terawatt-hours in 1985. In 2005, it reached 2,500 TWh. As of 2023, it’s 9,500 TWh. The country is basically sprinting to build more power plants to keep up with demand, and much of it is being filled with coal-fired power plants.

Roughly two-thirds of new coal power plants that came online around the world this year were in China. However, the country is also seeing very quick rises in renewables, including wind and solar power. So even while the use of coal has skyrocketed in the country, the proportion of coal on its grid has ticked down slightly over the past few years.

India is also seeing quick growth of electricity demand, and coal accounted for roughly 75% of that country’s grid as of 2023.

The good news is the coal boom could have been a lot worse, Jaeger says. As of 2015 (the year that major nations signed the Paris Agreement, setting a goal to limit warming to roughly 1.5 °C over preindustrial levels), nearly 1,500 gigawatts’ worth of coal capacity was in development around the world. As of 2023, about half of those planned plants had been canceled or suspended. Roughly 30% went into operation, while the rest are still in development.

Shutting down coal plants is a great way to quickly reduce emissions from the power grid. The problem is, for many of the countries where coal is still growing, moving away from it is going to be harder than it’s been in countries like the UK.

The fleet of coal-fired power plants in both China and India is relatively new, so it would be more of a financial loss to phase them out now. Both nations also have booming domestic coal industries, so shifting away could have economic impacts for people there.

While both countries have high and growing emissions today, they’re not the biggest historical contributors to climate change. Europe and the US together have emitted roughly 40% of all greenhouse gases in the atmosphere since 1850, meaning those countries have contributed the most to the climate crisis.

Richer nations that have been able to move away from coal, like the UK, Germany, and the US, may need to support other countries that need to do the same, whether that’s through financial assistance, technology sharing, or other strategies, Jaeger says.

If there’s one takeaway from the shutdown of the UK’s final coal plant, he adds, it’s that “rapid speeds of transition away from fossil fuels are possible.”

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In an essay last week, Sam Altman, the CEO of OpenAI, argued that the accelerating capabilities of AI will usher in an idyllic “Intelligence Age,” unleashing “unimaginable” prosperity and “astounding triumphs” like “fixing the climate.”

It’s a promise that no one is in a position to make—and one that, when it comes to the topic of climate change, fundamentally misunderstands the nature of the problem.

More maddening, the argument suggests that the technology’s massive consumption of electricity today doesn’t much matter, since it will allow us to generate abundant clean power in the future. That casually waves away growing concerns about a technology that’s already accelerating proposals for natural-gas plants and diverting major tech companies from their corporate climate targets.

By all accounts, AI’s energy demands will only continue to increase, even as the world scrambles to build larger, cleaner power systems to meet the increasing needs of EV charging, green hydrogen production, heat pumps, and other low-carbon technologies. Altman himself reportedly just met with White House officials to make the case for building absolutely massive AI data centers, which could require the equivalent of five dedicated nuclear reactors to run.

It’s a bedrock perspective of MIT Technology Review that technological advances can deliver real benefits and accelerate societal progress in meaningful ways. But for decades researchers and companies have oversold the potential of AI to deliver blockbuster medicines, achieve super intelligence, and free humanity from the need to work. To be fair, there have been significant advances, but nothing on the order of what’s been hyped.

Given that track record, I’d argue you need to develop a tool that does more than plagiarize journalism and help students cheat on homework before you can credibly assert that it will solve humanity’s thorniest problems, whether the target is rampant poverty or global warming.

To be sure, AI may help the world address the rising dangers of climate change. We have begun to see research groups and startups harness the technology to try to manage power grids more effectively, put out wildfires faster, and discover materials that could create cheaper, better batteries or solar panels.

All those advances are still relatively incremental. But let’s say AI does bring about an energy miracle. Perhaps its pattern-recognition prowess will deliver the key insight that finally cracks fusion—a technology that Altman is betting on heavily as an investor.

That would be fantastic. But technological advances are just the start—necessary but far from sufficient to eliminate the world’s climate emissions.

How do I know?

Because between nuclear fission plants, solar farms, wind turbines, and batteries, we already have every technology we need to clean up the power sector. This should be the low-hanging fruit of the energy transition. Yet in the largest economy on Earth, fossil fuels still generate 60% of the electricity. The fact that so much of our power still comes from coal, petroleum, and natural gas is a regulatory failure as much as a technological one.

“As long as we effectively subsidize fossil fuels by allowing them to use the atmosphere as a waste dump, we are not allowing clean energy to compete on a level playing field,” Zeke Hausfather, a climate scientist at the independent research organization Berkeley Earth, wrote on X in a response to Altman’s post. “We need policy changes, not just tech breakthroughs, to meet our climate goals.”

That’s not to say there aren’t big technical problems we still need to solve. Just look at the continuing struggles to develop clean, cost-competitive ways of fertilizing crops or flying planes. But the fundamental challenges of climate change are sunk costs, development obstacles, and inertia.

We’ve built and paid for a global economy that spews out planet-warming gases, investing trillions of dollars in power plants, steel mills, factories, jets, boilers, water heaters, stoves, and SUVs that run on fossil fuels. And few people or companies will happily write off those investments so long as those products and plants still work. AI can’t remedy all that just by generating better ideas.

To raze and replace the machinery of every industry around the world at the speed now required, we will need increasingly aggressive climate policies that incentivize or force everyone to switch to cleaner plants, products, and practices.

But with every proposal for a stricter law or some big new wind or solar farm, forces will push back, because the plan will hit someone’s wallet, block someone’s views, or threaten the areas or traditions someone cherishes. Climate change is an infrastructure problem, and building infrastructure is a messy human endeavor.

Tech advances can ease some of these issues. Cheaper, better alternatives to legacy industries make hard choices more politically palatable. But there are no improvements to AI algorithms or underlying data sets that solve the challenge of NIMBYism, the conflict between human interests, or the desire to breathe the fresh air in an unsullied wilderness.

To assert that a single technology—that just happens to be the one your company develops—can miraculously untangle these intractable conflicts of human society is at best self-serving, if not a little naïve. And it’s a troubling idea to proclaim at a point when the growth of that very technology is threatening to undermine the meager progress the world has begun to make on climate change.

As it is, the one thing we can state confidently about generative AI is that it’s making the hardest problem we’ve ever had to solve that much harder to solve.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Space travel is dangerous. Could genetic testing and gene editing make it safer?

Long-distance space travel can wreak havoc on human health. There’s radiation and microgravity to contend with, as well as the psychological toll of isolation and confinement. Research on identical twin astronauts has also revealed a slew of genetic changes that happen when a person spends a year in space.

That’s why some bioethicists are exploring the idea of radical treatments for future astronauts. Once we’ve figured out all the health impacts of space travel, they argue, we should edit the genomes of astronauts ahead of launch to offer them the best protection. Some have even suggested this might result in the creation of an all-new species: Homo spatialis.

If this is starting to sound a bit like sci-fi, that’s because it mostly is, for now. But there are biotechnologies we can use to help space travelers now, too.

—Jessica Hamzelou

This story is from The Checkup, our weekly health and biotech newsletter. Sign up to receive it in your inbox every Thursday.

MIT Technology Review Narrated: How generative AI could reinvent what it means to play

Open-world video games are inhabited by vast crowds of computer-controlled characters. They make virtual worlds feel lived in and full.

After a while, however, the repetitive chitchat (or threats) of a passing stranger forces you to bump up against the truth: This is just a game.

It may not always be like that. Generative AI is opening the door to entirely new kinds of in-game interactions that are open-ended, creative, and unexpected. Future AI-powered NPCs that don’t rely on a script could make games and virtual worlds deeply immersive.

This is our latest story to be turned into a MIT Technology Review Narrated podcast. In partnership with News Over Audio, we’ll be making a selection of our stories available, each one read by a professional voice actor. You’ll be able to listen to them on the go or download them to listen to offline.

We’re publishing a new story each week on Spotify and Apple Podcasts, including some taken from our most recent print magazine. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Sam Altman has denied he’s due a giant equity stake in OpenAI
Although the startup’s board has discussed him being compensated. (CNBC)
+ Questions have been raised over the legality of OpenAI abandoning its non-profit roots. (Vox)
+ Fellow AI firm Anthropic is also a public benefit company. (Bloomberg $)

2 Hurricane Helene is battering the US
The colossal category 4 storm is a potential threat to life. (The Guardian)
+ Florida residents are being warned to evacuate as soon as possible. (Vox)
+ How climate change can supercharge hurricanes. (MIT Technology Review)

3 A ‘predatory’ US telehealth startup prescribes Adderall to patients from China
In a bid to avoid a crackdown from federal authorities. (WSJ $)

4 High schools aren’t equipped to deal with sexually explicit deepfakes
And neither are tech companies. (The Atlantic $)
+ A high school’s deepfake porn scandal is pushing US lawmakers into action. (MIT Technology Review)

5 Close to a third of Elon Musk’s X posts last week were incorrectThey were either false, misleading, or missing crucial context. (NYT $)
+ Musk’s inflammatory posting led to him not being invited to a UK tech summit. (The Guardian)

6 Science editors are questioning Meta’s claims it’s not polarizing
Respected journal Science is revisiting a paper it published last year. (WSJ $)

7 An exoskeleton manufacturer is refusing to fix a broken suit
Leaving its paralyzed wearer unable to move. (404 Media)
+ A brain implant changed her life. Then it was removed against her will. (MIT Technology Review)

8 China’s premier nuclear submarine sank in its own shipyardIt’s a major step backwards in its plans to catch up with US seapower. (FT $)

9 For sale: a brand new NASA moon rover
It’s technically a bargain. (Economist $)
+ What’s next for the moon. (MIT Technology Review)

10 How this family of finches became an internet sensation
Nesting season is providing essential viewing. (NY Mag $)

Quote of the day

“Elon Musk treats the platform like his own misinformation megaphone.”

—Imran Ahmed, chief executive of the watchdog Center for Countering Digital Hate, tells AFP he fears Musk will continue fueling political tensions ahead of the US Presidential election.

The big story

The race to fix space-weather forecasting before next big solar storm hits

April 2024As the number of satellites in space grows, and as we rely on them for increasing numbers of vital tasks on Earth, the need to better predict stormy space weather is becoming more and more urgent.

Scientists have long known that solar activity can change the density of the upper atmosphere. But it’s incredibly difficult to precisely predict the sorts of density changes that a given amount of solar activity would produce.

Now, experts are working on a model of the upper atmosphere to help scientists to improve their models of how solar activity affects the environment in low Earth orbit. If they succeed, they’ll be able to keep satellites safe even amid turbulent space weather, reducing the risk of potentially catastrophic orbital collisions. Read the full story.

—Tereza Pultarova

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Form an orderly line to sample some of the world’s oldest cheese!
• We love the New York City Gay Man’s Chorus.
• The Earth is due to gain a second moon for a couple of months, which is pretty cool.
• York University in the UK has honored a fallen campus celebrity—a particularly tall duck called Long Boi.

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This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Recently, global news has been pretty bleak. So this week, I’ve decided to focus my thoughts beyond Earth’s stratosphere and well into space. A couple of weeks ago, SpaceX launched four private astronauts into orbit, where they performed the first ever spacewalk undertaken by private citizens (as opposed to astronauts trained by national agencies).

The company has more ambitious plans for space travel, and it’s not alone. Elon Musk, the founder of SpaceX, claimed on Sunday that he would launch uncrewed missions to Mars within two years, and crewed missions four years after that if the uncrewed missions were successful. (Other SpaceX timelines for reaching the Red Planet haven’t panned out.) NASA refers to Mars as its “​​horizon goal for human exploration.” China previously announced plans for a human mission as early as 2033 and recently moved up its timeline for an uncrewed sample return mission by two years. And the UAE has a 100-year plan to construct a habitable community on Mars by 2117.

None of this will be straightforward. Long-distance space travel can wreak havoc on human health. There’s radiation and microgravity to contend with, as well as the psychological toll of isolation and confinement. Research on identical twin astronauts has also revealed a slew of genetic changes that happen when a person spends a year in space.

That’s why some bioethicists are exploring the idea of radical treatments for future astronauts. Once we’ve figured out all the health impacts of space travel, they argue, we should edit the genomes of astronauts ahead of launch to offer them the best protection. Some have even suggested this might result in the creation of an all-new species: Homo spatialis. If this is starting to sound a bit like sci-fi, that’s because—for now, at least—it is. But there are biotechnologies we can use to help space travelers now, too.

Space travel is risky. When it comes down to it, a space launch essentially involves strapping humans into a capsule and exploding a bomb beneath them, says Paul Root Wolpe, who served as NASA’s senior bioethicist for 15 years.

Once you’re in space, you’re subject to far higher levels of radiation than you’d encounter on Earth. Too much radiation can increase a person’s risk of cancer and neurological disorders. It can also harm body tissues, resulting in cataracts or digestive diseases, for example. That’s why agencies like the US Department of Labor’s Occupational Safety and Health Administration set limits on radiation exposure. (NASA also sets limits on the amount of radiation astronauts can be exposed to.)

Then there’s microgravity. Our bodies have adapted to Earth’s gravity. Without that gravitational pull, strange things can happen. For one thing, internal fluids can start to pool at the top of the body. Muscles don’t need to work as hard when there’s no gravity, and astronauts tend to experience loss of muscle mass as well as bone.

Five years ago, scientists working with NASA published the results of a groundbreaking study comparing two identical twins—one of whom spent a year in space while the other remained on Earth. The twins, Mark and Scott Kelly, were both trained astronauts. And because they have the same set of genes, researchers were able to compare them to assess the impact of long-term space travel on how genes work.

The researchers found that both twins experienced some changes to the way their genes worked over that period, but they changed in different ways. Some of the effects in the space-faring brother lasted for more than six months. These changes are thought to be a response to the stress of space travel and perhaps a reaction to the DNA damage caused by space radiation.

Space travel comes with other risks, including weight loss, permanent eye damage caused by what is known as “spaceflight-associated neuro-ocular syndrome,” and psychological distress as a result of being far from friends and loved ones.

And if all that weren’t enough, injuries are also common on space missions, says Wolpe, who is now founding director of the Center for Peace Building and Conflict Transformation at Emory University. Tools and equipment can float around, knocking into people. Bungee cords snap. “Astronauts are supposed to wear safety goggles at all times, but they didn’t,” says Wolpe. “The injury list is lengthy … it’s really surprising how many injuries were [sustained] by astronauts on the space station.”

Commercial space travel brings a new set of dangers. Until very recently, the only people who traveled to space went through rigorous health tests and training programs overseen by national agencies. That isn’t the case for private space travel, where the rules are determined by the individual company, says Wolpe.

Astronauts are screened for common conditions like high blood pressure and diabetes. Space tourists might not be. We’re still learning the basics when it comes to the impact of space travel on health. We have no idea how it might affect a person who has various disorders and takes multiple medications.

Could gene editing protect astronauts from these potential problems? People who have adapted to high altitudes on Earth have genetic factors that allow them to thrive in low-oxygen environments—what if we could confer these factors to astronauts? And while we’re at it, why not throw in some more genetic changes—ones that might protect them from bone or muscle loss, for example?

Here’s where we get into Homo spatialis territory—the idea of a new species better suited to a life in space, or on a planet other than Earth. For the time being, this approach is not an option—there are currently no gene therapies that have been designed for people undertaking space travel. But one day “it might be in the best interests of the astronauts to undergo some genetic intervention, like gene editing, to safeguard them,” says Rosario Isasi, a bioethicist at the University of Miami. “It might be more than a duty, but a condition for an astronaut going on these missions.”

Wolpe is not keen on the idea. “There is some integrity to being human, and to the human body, that should not be breached,” he says. “These kinds of modifications are going to … end up with a number of disasters.” Isasi also hopes that advances in precision medicine, which will make possible bespoke treatments for individuals, might sidestep the need for genetic modifications.

In the meantime, genetic testing could be helpful for both astronauts and space tourists, says Wolpe. Some body tissues are more vulnerable to radiation damage, including the thyroid gland. Genetic tests that reveal a person’s risk of thyroid cancer might be useful for those considering space travel, he says.

Whether people are going into space as tourists, employees, scientists, or research subjects, figuring out how to send them safely is vitally important. After all, space tourism is nothing like regular tourism. “You’re putting [people] in a situation the human body was never designed to be in,” says Wolpe.

Now read the rest of The CheckupRead more from MIT Technology Review’s archiveScientists can test-drive space missions in extreme and remote environments here on Earth. “Analogue astronaut facilities,” which have been set up in deserts and in the Antarctic, simulate the isolating experience of real space travel, Sarah Scoles reports.

Astronaut meals could be set for a slightly weird overhaul. The prepackaged food currently used has a shelf life of around a year and a half. Making food from astronauts’ breath could one day be an alternative solution for longer space missions, writes Jonathan O’Callaghan.

Only 11 people can fit on the International Space Station at once. Perhaps a self-assembling space habitat—complete with a sea-anemone-inspired sofa—could provide alternative living quarters, writes Sarah Ward.

More than a dozen robotic vehicles are scheduled to land on the moon in the 2020s, and there are plans in the works for “lunar economies” and “permanent settlements,” reports Jonathan O’Callaghan in this piece that explores what’s next for the moon.

The International Space Station is getting old, and there are plans to destroy it by 2030. Now NASA is partnering with private companies to develop new commercial space stations for research, manufacturing, and tourism, reports David W. Brown.

From around the webThe team that earned the Nobel Prize for developing CRISPR is asking to cancel two of their own seminal patents. My colleague Antonio Regalado has the scoop. (MIT Technology Review)

In an attempt to protect young children from allergic reactions, did pediatricians inadvertently create an epidemic of peanut allergies? (Wall Street Journal)

Only 6% of the plastic produced in the US in 2021 ended up getting recycled, according to a Greenpeace report. It’s one of the reasons why microplastics are so ubiquitous. (National Geographic)

Axolotls age slowly, and no one really knows what they die. It now appears they pause at least one aspect of the aging process partway through their lives. (New Scientist)

“Mpox” has become the established name for a viral disease that has been responsible for over 200 deaths in the last couple of years—but only in the English language. Multiple names are still used in Spanish, French, and Portuguese, some of which have racist connotations. (The Lancet)

Being a living kidney donor today is less risky than it was a couple of decades ago. Data collected between 1994 and 2009 estimated 3.1 deaths within 90 days per 10,000 donations. This figure declined in the years between 2013 and 2022, to less than 1 death per 10,000 donations. (JAMA Network)

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Two Nobel Prize winners want to cancel their own CRISPR patents in Europe

In the decade-long fight to control CRISPR, the super-tool for modifying DNA, it’s been common for lawyers to try to overturn patents held by competitors. But now, in a surprise twist, the team that earned the Nobel Prize in chemistry for developing CRISPR is asking to cancel two of their own seminal patents, MIT Technology Review has learned.

­­The request to withdraw the pair of European patents, by lawyers for Emmanuelle Charpentier and Jennifer Doudna, comes after a damaging August opinion from a European technical appeals board, which ruled that the duo’s earliest patent filing didn’t explain CRISPR well enough for other scientists to use it and doesn’t count as a proper invention.

The decision could have major ramifications regarding who gets to collect the lucrative licensing fees on using the technology.Read the full story.

— Antonio Regalado

A tiny new open-source AI model performs as well as powerful big ones

What’s new: The Allen Institute for Artificial Intelligence (Ai2), a research nonprofit, is releasing a family of open-source multimodal language models, called Molmo, that it says perform as well as top proprietary models from OpenAI, Google, and Anthropic.

What it does: The organization claims that its biggest Molmo model outperforms OpenAI’s GPT-4o in tests that measure things like understanding images, charts, and documents. Meanwhile, Ai2 says a smaller Molmo model comes close to OpenAI’s state-of-the-art model in performance, an achievement it ascribes to vastly more efficient data collection and training methods.

Why it matters: These techniques could prove really useful if we want to meaningfully govern the data that we use for AI development, and suggest that training models on less, but higher-quality, data can lower computing costs. Read the full story.

—Melissa Heikkilä

Want AI that flags hateful content? Build it.

Humane Intelligence, an organization focused on evaluating AI systems, is launching a competition that challenges developers to create a computer vision model that can track hateful image-based propaganda online.

This is the second of a planned series of 10 “algorithmic bias bounty” programs from the nonprofit, with the twin goal of both teaching people how to do algorithmic assessments and actually solving a pressing problem in the field. Read the full story.

—Scott J Mulligan

Why Microsoft made a deal to help restart Three Mile Island

Nuclear power is coming back to Three Mile Island. Its nuclear power plant is often associated with a very specific event: one of its reactors suffered a partial meltdown in 1979 in what remains the most significant nuclear accident in US history. It has been shuttered ever since.

The site’s owner announced last week that it has plans to reopen the plant and has signed a deal with Microsoft. The company will purchase the plant’s entire electric generating capacity over the next 20 years. Casey Crownhart, our senior climate reporter, has dug into what this says about the future of the nuclear industry and Big Tech’s power demand. Read the full story.

This story is from The Spark, our weekly newsletter giving you the inside track on all things happening in climate innovation. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI is set to become a for-profit business
And CEO Sam Altman could be on course to receive $150 billion in equity. (Reuters)
+ The startup’s costs far outpace its current revenue. (NYT $)
+ CTO Mira Murati and two other executives have announced their departure. (Insider $)

2 Meta’s new smart glasses actually look pretty good
Called Project Orion, they’re a confident step forward for facial computing. (NYT $)
+ Wearers can receive real-time translation for other languages. (The Information $)
+ Meta also announced deals with household names to voice its AI assistant. (WSJ $)
+ Expect an influx of even more AI-generated images on your social feeds. (The Verge)
3 Google is fighting Microsoft in European courts
It’s filed a formal complaint in the EU accusing its rival of abusing its cloud power. (WSJ $)

4 Uber is getting into the product-delivery business
It’s part of a strategy to challenge Amazon and other retail delivery services. (FT $)
+ Uber’s facial recognition is locking Indian drivers out of their accounts. (MIT Technology Review)

5 Operators are racing to overhaul their power gridsStringing new sets of wires on existing lines is one solution. (IEEE Spectrum)
+ Why one developer won’t quit fighting to connect the US’s grids. (MIT Technology Review)

6 The Titan Submersible had multiple problems with its hull
The US Coast Guard is currently holding a hearing into the fatal implosion last year. (Wired $)

7 How one lab ingredient ruined science experiments across the worldScientists aren’t entirely sure why the seaweed-derived agar went bad. (The Atlantic $)

8 Reddit is using AI to translate its commentsIf it works, it could unlock entire communities written in other languages. (Insider $)

9 Just 5,000 people are using the Rabbit R1 daily
That’s just 5% of the people who bought it five months ago. (The Verge)

10 The world’s first 3D-printed hotel is under construction in Texas
Once completed, 43 new units will be available to stay in. (Reuters)
+ Meet the designers printing houses out of salt and clay. (MIT Technology Review)

Quote of the day

“What would you like to talk about, love?”

—Meta’s AI chatbot, which has been trained to imitate English actor Judi Dench, attempts a casual conversation with the Washington Post.

The big story

Inside the decades-long fight over Yahoo’s misdeeds in China

December 2023

When you think of Big Tech these days, Yahoo is probably not top of mind. But for Chinese dissident Xu Wanping, the company still looms large—and has for nearly two decades.

In 2005, Xu was arrested for signing online petitions relating to anti-Japanese protests. He didn’t use his real name, but he did use his Yahoo email address, which was among many Yahoo China handed over to Chinese law enforcement. This in turn allowed the government to identify and arrest some users.

Xu served nine years in prison as a result. Now, he and five other Chinese former political prisoners are suing Yahoo and a slate of co-defendants. Read the full story.

—Eileen Guo

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• The bird photographer of the year competition is guaranteed to showcase amazing talent.
• If you’re lucky enough to grow your own apples, here’s some of the best recipes you should consider.
• Why what you consider a normal sleeping pattern may not be so normal after all.
• How Fred Flintstone became a cultural icon.

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This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Nuclear power is coming back to Three Mile Island.

That nuclear power plant is typically associated with a very specific event. One of its reactors, Unit 2, suffered a partial meltdown in 1979 in what remains the most significant nuclear accident in US history. It has been shuttered ever since.

But the site, in Pennsylvania, is also home to another reactor—Unit 1, which consistently and safely generated electricity for decades until it was shut down in 2019. The site’s owner announced last week that it has plans to reopen the plant and signed a deal with Microsoft. The company will purchase the plant’s entire electric generating capacity over the next 20 years.

This news is fascinating for so many reasons. Obviously this site holds a certain significance in the history of nuclear power in the US. There’s a possibility this would be one of the first reactors in the country to reopen after shutting down. And Microsoft will be buying all the electricity from the reactor. Let’s dig into what this says about the future of the nuclear industry and Big Tech’s power demand.

Unit 2 at Three Mile Island operated for just a few months before the accident, in March 1979. At the time, Unit 1 was down for refueling. That reactor started back up, to some controversy, in the mid-1980s and produced enough electricity for hundreds of thousands of homes in the area for more than 30 years.

Eventually, though, the plant faced economic struggles. Even though it was operating at relatively high efficiency and with low costs, it was driven out of business by record low prices for natural gas and the introduction of relatively cheap, subsidized renewable energy to the grid, says Patrick White, research director of the Nuclear Innovation Alliance, a nonprofit think tank.

That situation has shifted in just the past few years, White says. There’s more money available now for nuclear, including new technology-agnostic tax credits in the Inflation Reduction Act. And there’s also rising concern about the increased energy demand on the power grid, in part from tech giants looking to power data centers like those needed to run AI.

In announcing its deal with Microsoft, Constellation Energy, the owner of Three Mile Island Unit 1, also shared that the plant is getting a rebrand—the site will be renamed the Crane Clean Energy Center. (Not sure if that one’s going to stick.)

The confluence of the particular location of this reactor and the fact that the electricity will go to power data centers (and other infrastructure) makes this whole announcement instantly attention-grabbing. As one headline put it, “Microsoft AI Needs So Much Power It’s Tapping Site of US Nuclear Meltdown.”

For some people in climate circles, this deal makes a lot of sense. Nuclear power remains one of the most expensive forms of electricity today. But experts say it could play a crucial role on the grid, since the plants typically put out a consistent amount of electricity—it’s often referred to as “firm power,” in contrast with renewables like wind and solar that are intermittently available.

Without guaranteed money there’s a chance this reactor would simply have been decommissioned as planned. Reopening plants that shuttered recently could provide an opportunity to get the benefits of nuclear power without having to build an entirely new project.

In March, the Palisades Nuclear Plant in Michigan got a loan guarantee from the US Department of Energy’s Loan Programs Office to the tune of over $1.5 billion to help restart. Palisades shut down in 2022, and the site’s owner says it hopes to get it back online by late 2025. It will be the first shuttered reactor in the US to come back online, if everything goes as planned. (For more details, check out my story from earlier this year.)

Three Mile Island may not be far behind—Constellation says the reactor could be running again by 2028. (Interestingly, the facility will need to separately undergo a relicensing process in just a few years, as it’s currently only licensed to run through 2034. A standard 20-year extension could have it running until 2054.)

If Three Mile Island comes back online, Microsoft will be the one benefiting, as its long-term power purchase agreement would secure it enough energy to power roughly 800,000 homes every year. Except in this case, it’ll be used to help run the company’s data center infrastructure in the region.

This isn’t the first recent sign Big Tech is jumping in on nuclear power: Earlier this year, Amazon purchased a data center site right next to the Susquehanna nuclear power plant, also in Pennsylvania.

While Amazon will use only part of the output of the Susquehanna plant, Microsoft will buy all the power that Three Mile Island produces. That raises the question of who’s paying for what in this whole arrangement. Ratepayers won’t be expected to shoulder any of the costs to restart the facility, Constellation CEO Joe Dominguez told the Washington Post. The company also won’t seek any special subsidies from the state, he added.

However, Dominguez also told the Post that federal money is key in allowing this project to go forward. Specifically, there are tax credits in the Inflation Reduction Act set aside for existing nuclear plants.

The company declined to give the Post a value for the potential tax credits and didn’t respond to my request for comment, but I busted out a calculator and did my own math. Assuming an 835-megawatt plant running at 96.3% capacity (the figure Constellation gave for the plant’s final year of operation) and a $15-per-megawatt-hour tax credit, that could add up to about $100 million each year, assuming requirements for wages and price are met.

It’ll be interesting to see how much further this trend of restarting plants might go. The Duane Arnold nuclear plant in Iowa is one potential candidate—it shuttered in 2020 after 45 years, and the site’s owner has made public comments about the potential of reopening.

Restarting any or all of these three sites could be the latest sign of an approaching nuclear resurgence. Big tech companies need lots of energy, and bringing old nuclear plants onto the grid—or, better yet, keeping aging ones open—seems to me like a great way to meet demand.

But given the relative rarity of opportunities to snag power from recently closed or closing plants, I think the biggest question for the industry is whether this wave of interest will translate into building new reactors as well.

Now read the rest of The SparkRelated readingRead my story from earlier this year for all the details on what it takes to reopen a shuttered nuclear power plant and what we might see at Palisades.

In the latest in our virtual events series, my colleagues James Temple, Melissa Heikkilä, and David Rotman are having a discussion about AI’s climate impacts. Subscribers can join them for the discussion live at 12:30 p.m. Eastern today, September 25, or check out the recording later.

AI is an energy hog, but the effects of the technology on emissions are a bit complicated, as I covered in this newsletter.

Three more thingsIt’s been a busy week for the climate team here at MIT Technology Review, so let’s do a rapid-fire round:

1. Countries including Germany, Sweden, and New Zealand are ending EV subsidies. I wrote about why some experts are worried that the move is coming too soon for some of them.
2. A proposal to connect two of the US’s largest grids could be crucial to cleaning up our electricity system. The project just got a major boost in the form of hundreds of billions of dollars, and it could represent a long-awaited success for energy entrepreneur Michael Skelly, as my colleague James Temple covered in a new story.
3. Finally, there’s just one week until we drop our 2024 list of 15 Climate Tech Companies to Watch. Check out this preview story about the list, and keep your eyes peeled next week for the reveal.

Keeping up with climate The US Department of Energy just announced $3 billion in funding to boost the battery and EV supply chain. (E&E News)

→ A single Minnesota mine could unlock billions of tax credits in the US. (MIT Technology Review)

Cheap solar panels are making that energy source abundantly available in Pakistan. But the boom also threatens making power pulled from the grid unaffordable. (Financial Times)

Individual action alone won’t solve the climate crisis, but there are some things people can do. Check out this package on how to decarbonize your life through choices about everything from food to transportation. (Heatmap News)

A group of major steel buyers wants a million tons of low-emissions steel in North America by 2028. These kinds of commitments from customers could help clean up heavy industry. (Canary Media)

This startup wants to use ground-up rocks and the ocean to soak up carbon dioxide. The result could transform the oceans. (New York Times)

North America’s largest food companies are struggling to cut emissions. The biggest culprit is their supply chains—the ingredients they use and the transportation needed to move them around. (Inside Climate News)
California is suing ExxonMobil, claiming the company misled consumers by perpetuating the myth that recycling could solve the plastic waste crisis. Only a small fraction of plastic waste is ever recycled. (The Verge)

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Humane Intelligence, an organization focused on evaluating AI systems, is launching a competition that challenges developers to create a computer vision model that can track hateful image-based propaganda online. Organized in partnership with the Nordic counterterrorism group Revontulet, the bounty program opens September 26. It is open to anyone, 18 or older, who wants to compete and promises $10,000 in prizes for the winners.

This is the second of a planned series of 10 “algorithmic bias bounty” programs from Humane Intelligence, a nonprofit that investigates the societal impact of AI and was launched by the prominent AI researcher Rumman Chowdhury in 2022. The series is supported by Google.org, Google’s philanthropic arm.

“The goal of our bounty programs is to, number one, teach people how to do algorithmic assessments,” says Chowdhury, “but also, number two, to actually solve a pressing problem in the field.”

Its first challenge asked participants to evaluate gaps in sample data sets that may be used to train models—gaps that may specifically produce output that is factually inaccurate, biased, or misleading.

The second challenge deals with tracking hateful imagery online—an incredibly complex problem. Generative AI has enabled an explosion in this type of content, and AI is also deployed to manipulate content so that it won’t be removed from social media. For example, extremist groups may use AI to slightly alter an image that a platform has already banned, quickly creating hundreds of different copies that can’t easily be flagged by automated detection systems. Extremist networks can also use AI to embed a pattern into an image that is undetectable to the human eye but will confuse and evade detection systems. It has essentially created a cat-and-mouse game between extremist groups and online platforms.

The challenge asks for two different models. The first, a task for those with intermediate skills, is one that identifies hateful images; the second, considered an advanced challenge, is a model that attempts to fool the first one. “That actually mimics how it works in the real world,” says Chowdhury. “The do-gooders make one approach, and then the bad guys make an approach.” The goal is to engage machine-learning researchers on the topic of mitigating extremism, which may lead to the creation of new models that can effectively screen for hateful images.

A core challenge of the project is that hate-based propaganda can be very dependent on its context. And someone who doesn’t have a deep understanding of certain symbols or signifiers may not be able to tell what even qualifies as propaganda for a white nationalist group.

“If [the model] never sees an example of a hateful image from a part of the world, then it’s not going to be any good at detecting it,” says Jimmy Lin, a professor of computer science at the University of Waterloo, who is not associated with the bounty program.

This effect is amplified around the world, since many models don’t have a vast knowledge of cultural contexts. That’s why Humane Intelligence decided to partner with a non-US organization for this particular challenge. “Most of these models are often fine-tuned to US examples, which is why it’s important that we’re working with a Nordic counterterrorism group,” says Chowdhury.

Lin, though, warns that solving these problems may require more than algorithmic changes. “We have models that generate fake content. Well, can we develop other models that can detect fake generated content? Yes, that is certainly one approach to it,” he says. “But I think overall, in the long run, training, literacy, and education efforts are actually going to be more beneficial and have a longer-lasting impact. Because you’re not going to be subjected to this cat-and-mouse game.”

The challenge will run till November 7, 2024. Two winners will be selected, one for the intermediate challenge and another for the advanced; they will receive $4,000 and $6,000, respectively. Participants will also have their models reviewed by Revontulet, which may decide to add them to its current suite of tools to combat extremism.

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Recorded on September 25, 2024

Putting AI’s Climate Impact Into Perspective

Speakers: David Rotman, Editor-at-large, Melissa Heikkilä, Senior AI Reporter, and James Temple, Sr Editor for Energy

The rise of AI comes with a growing carbon footprint and an increased demand for electricity. Analysts project that AI could drive up data centers’ energy consumption by 160% this decade. So how worried should we be about AI’s electricity demands and carbon emissions? How can this technology be used responsibly in the face of the climate crisis? Hear from editor-at-large David Rotman, senior AI reporter Melissa Heikkilä, and senior editor for energy James Temple for a conversation exploring the energy trade-offs involved in AI.

Related Coverage

• AI is an energy hog. This is what it means for climate change.
• Making an image with generative AI uses as much energy as charging your phone
• AI’s carbon footprint is bigger than you think

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In the decade-long fight to control CRISPR, the super-tool for modifying DNA, it’s been common for lawyers to try to overturn patents held by competitors by pointing out errors or inconsistencies.

But now, in a surprise twist, the team that earned the Nobel Prize in chemistry for developing CRISPR is asking to cancel two of their own seminal patents, MIT Technology Review has learned. The decision could affect who gets to collect the lucrative licensing fees on using the technology.

­­The request to withdraw the pair of European patents, by lawyers for Nobelists Emmanuelle Charpentier and Jennifer Doudna, comes after a damaging August opinion from a European technical appeals board, which ruled that the duo’s earliest patent filing didn’t explain CRISPR well enough for other scientists to use it and doesn’t count as a proper invention.

The Nobel laureates’ lawyers say the decision is so wrong and unfair that they have no choice but to preemptively cancel their patents, a scorched-earth tactic whose aim is to prevent the unfavorable legal finding from being recorded as the reason.

“They are trying to avoid the decision by running away from it,” says Christoph Then, founder of Testbiotech, a German nonprofit that is among those opposing the patents, who provided a copy of the technical opinion and response letter to MIT Technology Review. “We think these are some of the earliest patents and the basis of their licenses.”

Discovery of the centuryCRISPR has been called the biggest biotech discovery of the century, and the battle to control its commercial applications—such as gene-altered plants, modified mice, and new medical treatments—has raged for a decade.

The dispute primarily pits Charpentier and Doudna, who were honored with the Nobel Prize in 2020 for developing the method of genome editing, against Feng Zhang, a researcher at the Broad Institute of MIT and Harvard, who claimed to have invented the tool first on his own.

Back in 2014, the Broad Institute carried out a coup de main when it managed to win, and later defend, the controlling US patent on CRISPR’s main uses. But the Nobel paircould, and often did, point to their European patents as bright points in their fight. In 2017, the University of California, Berkeley, where Doudna works, touted its first European patent as exciting, “broad,” and “precedent” setting.

After all, a region representing more than 30 countries had not only recognized the pair’s pioneering discovery; it had set a standard for other patent offices around the world. It also made the US Patent Office look like an outlier whose decisions favoring the Broad Institute might not hold up long term. A further appeal challenging the US decisions is pending in federal court.

Long-running sagaBut now the European Patent Office is also saying—for different reasons—that Doudna and Charpentier can’t claim their basic invention. And that’s a finding their attorneys think is so damaging, and reached in such an unjust way, that they have no choice but to sacrifice their own patents. “The Patentees cannot be expected to expose the Nobel-prize winning invention … to the repercussions of a decision handed down under such circumstances,” says the 76–page letter sent by German attorneys on their behalf on September 20.

The chief intellectual-property attorney at the University of California, Randi Jenkins, confirmed the plan to revoke the two patents but downplayed their importance.

“These two European patents are just another chapter in this long-running saga involving CRISPR-Cas9,” Jenkins said. “We will continue pursuing claims in Europe, and we expect those ongoing claims to have meaningful breadth and depth of coverage.”

The patents being voluntarily disavowed are EP2800811, granted in 2017, and EP3401400, granted in 2019. Jenkins added the Nobelists still share one issued CRISPR patent in Europe, EP3597749, and one that is pending. That tally doesn’t include a thicket of patent claims covering more recent research from Doudna’s Berkeley lab that were filed separately.

Freedom to operateThe cancellation of the European patents will affect a broad network of biotech companies that have bought and sold rights as they seek to achieve either commercial exclusivity to new medical treatments or what’s called “freedom to operate”—the right to pursue gene-slicing research unmolested by doubts over who really owns the technique.

These companies include Editas Medicine, allied with the Broad Institute; Caribou Biosciences and Intellia Therapeutics in the US, both cofounded by Doudna; and Charpentier’s companies, CRISPR Therapeutics and ERS Genomics.

ERS Genomics, which is based in Dublin and calls itself “the CRISPR licensing company,” was set up in Europe specifically to collect fees from others using CRISPR. It claims to have sold nonexclusive access to its “foundational patents” to more than 150 companies, universities, and organizations who use CRISPR in their labs, manufacturing, or research products.

For example, earlier this year Laura Koivusalo, founder of a small Finnish biotech company, StemSight, agreed to a “standard fee” because her company is researching an eye treatment using stem cells that were previously edited using CRISPR.

Although not every biotech company thinks it’s necessary to pay for patent rights long before it even has a product to sell, Koivusalo decided it would be the right thing to do. “The reason we got the license was the Nordic mentality of being super honest. We asked them if we needed a license to do research, and they said yes, we did,” she says.

A slide deck from ERS available online lists the fee for small startups like hers at $15,000 a year. Koivusalo says she agreed to buy a license to the same two patents that are now being canceled. She adds: “I was not aware they were revoked. I would have expected them to give a heads-up.”

A spokesperson for ERS Genomics said its customers still have coverage in Europe based on the Nobelists’ remaining CRISPR patent and pending application.

In the US, the Broad Institute has also been selling licenses to use CRISPR. And the fees can get big if there’s an actual product involved. That was the case last year, when Vertex Pharmaceuticals won approval to sell the first CRISPR-based treatment, for sickle-cell disease. To acquire rights under the Broad Institute’s CRISPR patents, Vertex agreed to pay $50 million on the barrelhead—and millions more in the future.

PAM problemThere’s no doubt that Charpentier and Doudna were first to publish, in a 2012 paper, how CRISPR can function as a “programmable” means of editing DNA. And their patents in Europe withstood an initial round of formal oppositions filed by lawyers.

But this August, in a separate analysis, a technical body decided that Berkeley had omitted a key detail from its earliest patent application, making it so that “the skilled person could not carry out the claimed method,” according to the finding. That is, it said, the invention wasn’t fully described or enabled.

The omission relates to a feature of DNA molecules called “protospacer adjacent motifs,” or PAMs. These features, a bit like runway landing lights, determine at what general locations in a genome the CRISPR gene scissors are able to land and make cuts, and where they can’t.

In the 76-page reply letter sent by lawyers for the Nobelists, they argue there wasn’t really any need to mention these sites, which they say were so obvious that “even undergraduate students” would have known they were needed.

The lengthy letter leaves no doubt the Nobel team feels they’ve been wronged. In addition to disavowing the patents, the text runs on because it seeks to “make of public record the reasons for which we strongly disagree with [the] assessment on all points” and to “clearly show the incorrectness” of the decision, which, they say, “fails to recognize the nature and origin of the invention, misinterprets the common general knowledge, and additionally applies incorrect legal standards.”

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The Allen Institute for Artificial Intelligence (Ai2), a research nonprofit, is releasing a family of open-source multimodal language models, called Molmo, that it says perform as well as top proprietary models from OpenAI, Google, and Anthropic.

The organization claims that its biggest Molmo model, which has 72 billion parameters, outperforms OpenAI’s GPT-4o, which is estimated to have over a trillion parameters, in tests that measure things like understanding images, charts, and documents.

Meanwhile, Ai2 says a smaller Molmo model, with 7 billion parameters, comes close to OpenAI’s state-of-the-art model in performance, an achievement it ascribes to vastly more efficient data collection and training methods.

What Molmo shows is that open-source AI development is now on par with closed, proprietary models, says Ali Farhadi, the CEO of Ai2. And open-source models have a significant advantage, as their open nature means other people can build applications on top of them. The Molmo demo is available here, and it will be available for developers to tinker with on the Hugging Face website. (Certain elements of the most powerful Molmo model are still shielded from view.)

Other large multimodal language models are trained on vast data sets containing billions of images and text samples that have been hoovered from the internet, and they can include several trillion parameters. This process introduces a lot of noise to the training data and, with it, hallucinations, says Ani Kembhavi, a senior director of research at Ai2. In contrast, Ai2’s Molmo models have been trained on a significantly smaller and more curated data set containing only 600,000 images, and they have between 1 billion and 72 billion parameters. This focus on high-quality data, versus indiscriminately scraped data, has led to good performance with far fewer resources, Kembhavi says.

Ai2 achieved this by getting human annotators to describe the images in the model’s training data set in excruciating detail over multiple pages of text. They asked the annotators to talk about what they saw instead of typing it. Then they used AI techniques to convert their speech into data, which made the training process much quicker while reducing the computing power required.

These techniques could prove really useful if we want to meaningfully govern the data that we use for AI development, says Yacine Jernite, who is the machine learning and society lead at Hugging Face, and was not involved in the research.

“It makes sense that in general, training on higher-quality data can lower the compute costs,” says Percy Liang, the director of the Stanford Center for Research on Foundation Models, who also did not participate in the research.

Another impressive capability is that the model can “point” at things, meaning it can analyze elements of an image by identifying the pixels that answer queries.

In a demo shared with MIT Technology Review, Ai2 researchers took a photo outside their office of the local Seattle marina and asked the model to identify various elements of the image, such as deck chairs. The model successfully described what the image contained, counted the deck chairs, and accurately pinpointed to other things in the image as the researchers asked. It was not perfect, however. It could not locate a specific parking lot, for example.

Other advanced AI models are good at describing scenes and images, says Farhadi. But that’s not enough when you want to build more sophisticated web agents that can interact with the world and can, for example, book a flight. Pointing allows people to interact with user interfaces, he says.

Jernite says Ai2 is operating with a greater degree of openness than we’ve seen from other AI companies. And while Molmo is a good start, he says, its real significance will lie in the applications developers build on top of it, and the ways people improve it.

Farhadi agrees. AI companies have drawn massive, multitrillion-dollar investments over the past few years. But in the past few months, investors have expressed skepticism about whether that investment will bring returns. Big, expensive proprietary models won’t do that, he argues, but open-source ones can. He says the work shows that open-source AI can also be built in a way that makes efficient use of money and time.

“We’re excited about enabling others and seeing what others would build with this,” Farhadi says.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Why one developer won’t quit fighting to connect the US’s grids

Michael Skelly hasn’t learned to take no for an answer. For much of the last 15 years, the energy entrepreneur has worked to develop long-haul transmission lines to carry wind power across the Great Plains, Midwest, and Southwest. But so far, he has little to show for the effort.

Skelly has long argued that building such lines and linking together the nation’s grids would accelerate the shift from coal- and natural-gas-fueled power plants to the renewables needed to cut the pollution driving climate change. But his previous business shut down in 2019, after halting two of its projects and selling off interests in three more.

Skelly contends he was early, not wrong, and that the market and policymakers are increasingly coming around to his perspective. After all, the US Department of Energy just blessed his latest company’s proposed line with hundreds of millions in grants. Read the full story.

—James Temple

OpenAI released its advanced voice mode to more people. Here’s how to get it.

OpenAI is broadening access to Advanced Voice Mode, a feature of ChatGPT that allows you to speak more naturally with the AI model. It allows you to interrupt its responses midsentence, and it can sense and interpret your emotions from your tone of voice and adjust its responses accordingly.

Users who’ve been able to try it have largely described the model as an impressively fast, dynamic, and realistic voice assistant—which has made its limited availability particularly frustrating to some other OpenAI users. This is the first time the company has promised to bring the new voice mode to a wide range of users. Here’s what you need to know.

—James O’Donnell

An AI script editor could help decide what films get made in Hollywood

Every day across Hollywood, scores of film school graduates and production assistants work as script readers. Their job is to find the diamonds in the rough from the 50,000 or so screenplays pitched each year and flag any worth pursuing further.

Now the film-focused tech company Cinelytic, which works with major studios like Warner Bros. and Sony Pictures to analyze film budgets and box office potential, aims to offer script feedback with generative AI.

It takes its new tool Callaia less than a minute to compile a synopsis, a list of comparable films, grades for areas like dialogue and originality, and actor recommendations. Cool idea, but is it any good? Read the full story.

—James O’Donnell

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The star witness in the FTX case has been sentenced to two years in prison
Caroline Ellison got off lightly in exchange for her extensive cooperation. (CNBC)
+ In contrast, Sam Bankman-Fried was sentenced to 25 years earlier this year. (FT $)
+ Her help has been credited with helping to recover customer assets. (The Verge)

2 A Chinese-funded US VC fund is under scrutiny from the FBI
There are fears it may have passed trade secrets to Beijing. (FT $)
+ Hone Capital has invested in heavy-hitters including Stripe. (TechCrunch)

3 CrowdStrike’s CEO apologized to US Congress over the catastrophic outage
The crash highlighted the dangers of relying on single vendors. (WP $)
+ It’s facing legal action from its disgruntled shareholders. (Bloomberg $)+ The system failure affected millions of PCs across the world. (MIT Technology Review)

4 A bold plan to refreeze the Arctic may just work
Trials pumping seawater over existing ice appear have proved successful. (New Scientist $)
+ Europe is running rings around the US in terms of heat pump adoption. (The Atlantic $)

5 Huge data centers are springing up across Latin AmericaAnd local communities are paying the price. (The Guardian)
+ Energy-hungry data centers are quietly moving into cities. (MIT Technology Review)

6 Why Mark Zuckerberg washed his hands of politicsHe regrets some of the political posturing he dabbled in during his 20s. (NYT $)
+ Meta isn’t giving up on giving its chatbots famous voices. (Insider $)

7 Be wary of Google Images of risky mushroom species
They could be AI-generated and look nothing like the real thing. (404 Media)
+ Director and AI-embracer James Cameron has joined Stability AI’s board. (The Verge)

8 You probably don’t need an iPhone 16
How much better can a camera get, really? (New Yorker $)

9 Resist the temptation to vent about work online
Anything you share on company devices could come back to bite you. (WSJ $)

10 How to save the Earth from a colossal asteroid
Blast it into oblivion using a massive X-ray beam, obviously. (Vice)
+ Earth is probably safe from a killer asteroid for 1,000 years. (MIT Technology Review)

Quote of the day

“Not a day goes by that I don’t think about all of the people I hurt. I participated in a criminal conspiracy that ultimately stole billions of dollars from people who entrusted their money with us.”

—Caroline Ellison, a former executive at FTX, apologizes to New York federal court during her sentencing, Bloomberg reports.

The big story

How tracking animal movement may save the planet

February 2024Animals have long been able to offer unique insights about the natural world around us, acting as organic sensors picking up phenomena invisible to humans. Canaries warned of looming catastrophe in coal mines until the 1980s, for example.

These days, we have more insight into animal behavior than ever before thanks to technologies like sensor tags. But the data we gather from these animals still adds up to only a relatively narrow slice of the whole picture.

This is beginning to change. Researchers are asking: What will we find if we follow even the smallest animals? What could we learn from a system of animal movement, continuously monitoring how creatures big and small adapt to the world around us? It may be, some researchers believe, a vital tool in the effort to save our increasingly crisis-plagued planet. Read the full story.

—Matthew Ponsford

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• This night parrot looks like it’s hard some seriously late nights.
• The Nazgûl morning routine sounds like a great way to start the day.
• Time for a hypnotic pencil-sharpening video.
• Spooky season’s starting early this year: we’ve just discovered a new species of ghost shark!

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Michael Skelly hasn’t learned to take no for an answer.

For much of the last 15 years, the Houston-based energy entrepreneur has worked to develop long-haul transmission lines to carry wind power across the Great Plains, Midwest, and Southwest, delivering clean electricity to cities like Albuquerque, Chicago, and Memphis. But so far, he has little to show for the effort.

Skelly has long argued that building such lines and linking together the nation’s grids would accelerate the shift from coal- and natural-gas-fueled power plants to the renewables needed to cut the pollution driving climate change. But his previous business, Clean Line Energy Partners, shut down in 2019, after halting two of its projects and selling off interests in three more.

Skelly contends he was early, not wrong, about the need for such lines, and that the market and policymakers are increasingly coming around to his perspective. Indeed, the US Department of Energy just blessed his latest company’s proposed line with hundreds of millions in grants.

The North Plains Connector would stretch about 420 miles from southeast Montana to the heart of North Dakota and create the first major connection between the US’s two largest grids, enabling system operators to draw on electricity generated by hydro, solar, wind, and other resources across much of the country. This could help keep regional power systems online during extreme weather events and boost the overall share of electricity generated by those clean sources.

Skelly says he’s already secured the support of nine utilities around the region for the project, as well as more than 90% of the landowners along the route.

Michael Skelly founded Clean Line Energy Partners in 2009.GRID UNITEDHe says that more and more local energy companies have come to recognize that rising electricity demands, the growing threat storms and fires pose to power systems, and the increasing reliance on renewables have hastened the need for more transmission lines to stitch together and reinforce the country’s fraying, fractured grids.

“There’s a real understanding, really, across the country of the need to invest more in the grid,” says Skelly, now chief executive of Grid United, the Houston-based transmission development firm he founded in 2021. “We need more wires in the air.”

Still, proposals to build long transmission lines frequently stir up controversy in the communities they would cross. It remains to be seen whether this growing understanding will be enough for Skelly’s project to succeed, or to get the US building anywhere near the number of transmission lines it now desperately needs.

Linking gridsTransmission lines are the unappreciated linchpin of the clean-energy transition, arguably as essential as solar panels in cutting emissions and as important as seawalls in keeping people safe.

These long, high, thick wires are often described as the highways of our power systems. They connect the big wind farms, hydroelectric plants, solar facilities, and other power plants to the edges of cities, where substations step down the voltage before delivering electricity into homes and businesses along distribution lines that are more akin to city streets.

There are three major grid systems in the US: the Western Interconnection, the Eastern Interconnection, and the Texas Interconnected System. Regional grid operators such as the California Independent System Operator, the Midcontinent Independent System Operator, and the New York Independent System Operator oversee smaller local grids that are connected, to a greater or lesser extent, within those larger networks.

Transmission lines that could add significant capacity for sharing electricity back and forth across the nation’s major grid systems are especially valuable for cutting emissions and improving the stability of the power system. That’s because they allow those independent system operators to draw on a far larger pool of electricity sources. So if solar power is fading in one part of the country, they could still access wind or hydropower somewhere else. The ability to balance out fluctuations in renewables across regions and seasons, in turn, reduces the need to rely on the steady output of fossil-fuel plants.

“There’s typically excess wind or hydro or other resources somewhere,” says James Hewett, manager of the US policy lobbying group at Breakthrough Energy, the Bill Gates–backed organization focusing on clean energy and climate issues. “But today, the limiting constraint is the ability to move resources from the place where they’re excessive to where they’re needed.”

(Breakthrough Energy Ventures, the investment arm of the firm, doesn’t hold any investments in the North Plains Connector project or Grid United.)

It also means that even if regional wildfires, floods, hurricanes, or heat waves knock out power lines and plants in one area, operators may still be able to tap into adjacent systems to keep the lights on and air-conditioning running. That can be a matter of life and death in the event of such emergencies, as we’ve witnessed in the aftermath of heat waves and hurricanes in recent years.

Studies have shown that weaving together the nation’s grids can boost the share of electricity that renewables reliably provide, significantly cut power-sector emissions, and lower system costs. A recent study by the Lawrence Berkeley National Lab found that the lines interconnecting the US’s major grids and the regions within them offer the greatest economic value among transmission projects, potentially providing more than $100 million in cost savings per year for every additional gigawatt of added capacity. (The study presupposes that the lines are operated efficiently and to their full capacity, among other simplifying assumptions.)

Experts say that grid interconnections can more than pay for themselves over time because, among other improved efficiencies, they allow grid operators to find cheaper sources of electricity at any given time and enable regions to get by with fewer power plants by relying on the redundancy provided by their neighbors.

But as it stands, the meager links between the Eastern Interconnection and Western Interconnection amount to “tiny little soda straws connecting two Olympic swimming pools,” says Rob Gramlich, president of Grid Strategies, a consultancy in Washington, DC.

“A win-win-win”Grid United’s North Plains Connector, in contrast, would be a fat pipe.

The $3.2 billion, three-gigawatt project would more than double the amount of electricity that could zip back and forth between those grid systems, and it would tightly interlink a trio of grid operators that oversee regional parts of those larger systems: the Western Electricity Coordinating Council, the Midcontinent Independent System Operator, and the Southwest Power Pool. If the line is developed, each could then more easily tap into the richest, cheapest sources at any given time across a huge expanse of the nation, be it hydropower generated in the Northwest, wind turbines cranking across the Midwest, or solar power produced anywhere.

The North Plains Connector transmission line would stretch from from southeast Montana to the heart of North Dakota, connecting the nation’s two biggest grids.COURTESY: ALLETEThis would ensure that utilities could get greater economic value out of those energy plants, which are expensive to build but relatively cheap to operate, and it would improve the reliability of the system during extreme weather, Skelly says.

“If you’ve got a heat dome in the Northwest, you can send power west,” he says. “If you have a winter storm in the Midwest, you can send power to the east.”

Grid United is developing the project as a joint venture with Allete, an energy company in Duluth, Minnesota, that operates several utilities in the region.

The Department of Energy granted $700 million to a larger regional effort, known as the North Plains Connector Interregional Innovation project, which encompasses two smaller proposals in addition to Grid United’s. The grants will be issued through a more than $10 billion program established under the Bipartisan Infrastructure Law, enacted by President Joe Biden in 2021.

That funding will likely be distributed to regional utilities and other parties as partial matching grants, designed to incentivize investments in the project among those likely to benefit from it. That design may also help address a chicken-and-egg problem that plagues independent transmission developers like Grid United, Breakthrough’s Hewett says.

Regional utilities can pass along the costs of projects to their electricity customers. Companies like Grid United, however, generally can’t sign up the power producers that will pay to use their lines until they’ve got project approval, but they also often can’t secure traditional financing until they’ve lined up customers.

The DOE funding could ease that issue by providing an assurance of capital that would help get the project through the lengthy permitting process, Hewett says.

“The states are benefiting, local utilities are benefiting, and the developer will benefit,” he says. “It’s a win-win-win.”

Transmission hurdlesOver the years, developers have floated various proposals to more tightly interlink the nation’s major grid systems. But it’s proved notoriously difficult to build any new transmission lines in the US—a problem that has only worsened in recent years.

The nation is developing only 20% of the transmission capacity per year in the 2020s that it did in the early 2010s. On average, interstate transmission lines take eight to 10 years to develop “if they succeed at all,” according to a report from the Niskanen Center.

The biggest challenge in adding connections between grids, says Gramlich of Grid Strategies, is that there’s no clear processes for authorizing lines that cross multiple jurisdictions and no dedicated regional or federal agencies overseeing such proposals. The fact that numerous areas may benefit from such lines also sparks interregional squabbling over how the costs should be allocated.

In addition, communities often balk at the sight of wires and towers, particularly if the benefits of the lines mostly accrue around the end points, not necessarily in all the areas the wires cross. Any city, county, or state, or even one landowner, can hold up a project for years, if not kill it.

But energy companies themselves share much of the blame as well. Regional energy agencies, grid operators, and utilities have actively fought proposals from independent developers to erect wires passing through their territories. They often simply don’t want to forfeit control of their systems, invite added competition, or deal with the regulatory complexity of such projects.

The long delays in building new grid capacity have become a growing impediment to building new energy projects.

As of last year, there were 2,600 gigawatts’ worth of proposed energy generation or storage projects waiting in the wings for transmission capacity that would carry their electricity to customers, according to a recent analysis by Lawrence Berkeley National Lab. That’s roughly the electricity output of 2,600 nuclear reactors, or more than double the nation’s entire power system.

The capacity of projects in the queue has risen almost eightfold from a decade ago, and about 95% of them are solar, wind, or battery proposals.

“Grid interconnection remains a persistent bottleneck,” Joseph Rand, an energy policy researcher at the lab and the lead author of the study, said in a statement.

The legacy of Clean Line EnergySkelly spent the aughts as the chief development officer of Horizon Wind Energy, a large US wind developer that the Portuguese energy giant EDP snapped up in 2007 for more than $2 billion. Skelly then made a spirited though ill-fated run for Congress in 2008, as the Democratic nominee for the 7th Congressional District of Texas. He ran on a pro-renewables, pro-education campaign but lost by a sizable margin in a district that was solidly Republican.

The following year, he founded Clean Line Energy Partners. The company raised tens of millions of dollars and spent a decade striving to develop five long-range transmission projects that could connect the sorts of wind projects Skelly had worked to build before.

The company did successfully earn some of the permits required for several lines. But it was forced to shut down or offload its projects amid pushback from landowner groups and politicians opposed to renewables, as well as from regional utilities and public utility commissions.

“He was going to play in other people’s sandboxes and they weren’t exactly keen on having him in there,” says Russell Gold, author of Superpower: One Man’s Quest to Transform American Energy, which recounted Skelly’s and Clean Line Energy’s efforts and failures.

Ultimately, those obstacles dragged out the projects beyond the patience of the company’s investors, who declined to continue throwing more money at them, he says.

The company was forced to halt the Centennial West line through New Mexico and the Rock Island project across the Midwest. In addition, it sold off its stake in the Grain Belt Express, which would stretch from Kansas to Indiana, to Invenergy; the Oklahoma portion of the Plains and Eastern line to NextEra Energy; and the Western Spirit line through New Mexico, along with an associated wind farm project, to Pattern Development.

Clean Line Energy itself wound down in 2019.

The Western Spirit transmission line was electrified in late 2021, but the other two projects are still slogging through planning and permitting.

“These things take a long time,” Skelly says.

For all the challenges the company faced, Gold still credits it with raising awareness about the importance and necessity of long-distance interregional transmission. He says it helped spark conversations that led the Federal Energy Regulatory Commission to eventually enact rules to support regional transmission planning and encouraged other big players to focus more on building transmission lines.

“I do believe that there is a broader social, political, and commercial awareness now that the United States needs to interconnect its grids,” Gold says.

Lessons learnedSkelly spent a few years as a senior advisor at Lazard, consulting with companies on renewable energy. But he was soon ready to take another shot at developing long-haul transmission lines and started Grid United in 2021.

The new company has proposed four transmission projects in addition to the North Plains Connector—one between Arizona and New Mexico, one between Colorado and Oklahoma, and one each within Texas and Wyoming.

Asked what he thinks the legacy of Clean Line Energy is, Skelly says it’s mixed. But he soon adds that the history of US infrastructure building is replete with projects that didn’t move ahead. The important thing, he says, is to draw the right lessons from those failures.

“When we’re smart about it, we look at the past to see what we can learn,” he says. “We certainly do that today in our business.”

Skelly says one of the biggest takeaways was that it’s important to do the expensive upfront work of meeting with landowners well in advance of applying for permitting, and to use their feedback to guide the line of the route.

Anne Hedges, director of policy and legislative affairs at the Montana Environmental Information Center, confirms that this is the approach Grid United has taken in the region so far.

“A lot of developers seem to be more focused on drawing a straight line on a map rather than working with communities to figure out the best placement for the transmission system,” she says. “Grid United didn’t do that. They got out on the ground and talked to people and planned a route that wasn’t linear.”

The other change that may make Grid United’s project there more likely to move forward has more to do with what the industry’s learned than what Skelly has.

Gramlich says regional grid operators and utilities have become more receptive to collaborating with developers on transmission lines—and for self-interested reasons. They’ll need greater capacity, and soon, to stay online and meet the growing energy demands of data centers, manufacturing facilities, electric vehicles, and buildings, and address the risks to power systems from extreme weather events.

Industry observers are also hopeful that an energy permitting reform bill pending in Congress, along with the added federal funding and new rules requiring transmission providers to do more advance planning, will also help accelerate development. The bipartisan bill promises to shorten the approval process for projects that are determined to be in the national interest. It would also require neighboring areas to work together on interregional transmission planning.

Hundreds of environmental groups have sharply criticized the proposal, which would also streamline approvals for certain oil and gas operations.

“This legislation guts bedrock environmental protections, endangers public health, opens up tens of millions of acres of public lands and hundreds of millions of acres of offshore waters to further oil and gas leasing, gives public lands to mining companies, and would defacto rubberstamp gas export projects that harm frontline communities and perpetuate the climate crisis,” argued a letter signed by 350.org, Earthjustice, the Center for Biological Diversity, the Union of Concerned Scientists, and hundreds of other groups.

But a recent analysis by Third Way, a center-left think tank in Washington, DC, found that the emissions benefits from accelerating transmission permitting could significantly outweigh the added climate pollution from the fossil-fuel provisions in the bill. It projects that the bill would, on balance, reduce global emissions by 400 million to 16.6 billion tons of carbon dioxide through 2050.

“Guardedly optimistic” Grid United expects to begin applying for county and state permits in the next few months and for federal permits toward the end of the year. It hopes to begin construction within the next four years and switch the line on in 2032.

Since the applications haven’t been made, it’s not clear what individuals or groups are or will be opposed to it—though, given the history of such projects, some will surely object.

Hedges says the Montana Environmental Information Center is reserving judgment until it sees the actual application. She says the organization will be particularly focused on any potential impact on water and wildlife across the region, “making sure that they’re not harming what are already struggling resources in this area.”

So if Skelly was too early with his last company, the obvious question is: Are the market, regulatory, and societal conditions now ripe for interregional transmission lines?

“We’re gonna find out if they are, right?” he says. “We don’t know yet.”

Skelly adds that he doesn’t think the US is going to build as much transmission as it needs to. But he does believe we’ll start to see more projects moving forward—including, he hopes, the North Plains Connector.

“You just can’t count on anything, and you’ve just got to keep going and push, push, push,” he says. “But we’re making good progress. There’s a lot of utility interest. We have a big grant from the DOE, which will help bring down the cost of the project. So knock on wood, we’re guardedly optimistic.”

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OpenAI is broadening access to Advanced Voice Mode, a feature of ChatGPT that allows you to speak more naturally with the AI model. It allows you to interrupt its responses midsentence, and it can sense and interpret your emotions from your tone of voice and adjust its responses accordingly.

These features were teased back in May when OpenAI unveiled GPT-4o, but they were not released until July—and then just to an invite-only group. (At least initially, there seem to have been some safety issues with the model; OpenAI gave several Wired reporters access to the voice mode back in May, but the magazine reported that the company “pulled it the next morning, citing safety concerns.”)

Users who’ve been able to try it have largely described the model as an impressively fast, dynamic, and realistic voice assistant—which has made its limited availability particularly frustrating to some other OpenAI users.

Today is the first time OpenAI has promised to bring the new voice mode to a wide range of users. Here’s what you need to know.

What can it do? Though ChatGPT currently offers a standard voice mode to paid users, its interactions can be clunky. In the mobile app, for example, you can’t interrupt the model’s often long-winded responses with your voice, only with a tap on the screen. The new version fixes that, and also promises to modify its responses on the basis of the emotion it’s sensing from your voice. As with other versions of ChatGPT, users can personalize the voice mode by asking the model to remember facts about themselves. The new mode also has improved its pronunciation of words in non-English languages.

AI investor Allie Miller posted a demo of the tool in August, which highlighted a lot of the same strengths of OpenAI’s own release videos: The model is fast and adept at changing its accent, tone, and content to match your needs.

I’m testing the new @OpenAI Advanced Voice Mode and I just snorted with laughter.

In a good way.

Watch the whole thing pic.twitter.com/vSOMzXdwZo

— Allie K. Miller (@alliekmiller) August 2, 2024

The update also adds new voices. Shortly after the launch of GPT-4o, OpenAI was criticized for the similarity between the female voice in its demo videos, named Sky, and that of Scarlett Johansson, who played an AI love interest in the movie Her. OpenAI then removed the voice.

Now it has launched five new voices, named Arbor, Maple, Sol, Spruce, and Vale, which will be available in both the standard and advanced voice modes. MIT Technology Review has not heard them yet, but OpenAI says they were made using professional voice actors from around the world. “We interviewed dozens of actors to find those with the qualities of voices we feel people will enjoy talking to for hours—warm, approachable, inquisitive, with some rich texture and tone,” a company spokesperson says.

Who can access it and when?For now, OpenAI is rolling out access to Advanced Voice Mode to Plus users, who pay $20 per month for a premium version, and Team users, who pay $30 per month and have higher message limits. The next group to receive access will be those in the Enterprise and Edu tiers. The exact timing, though, is vague; an OpenAI spokesperson says the company will “gradually roll out access to all Plus and Team users and will roll out to Enterprise and Edu tiers starting next week.” The company hasn’t committed to a firm deadline for when all users in these categories will have access. A message in the ChatGPT app indicates that all Plus users will have access by “the end of fall.”

There are geographic limitations. The new feature is not yet available in the EU, the UK, Switzerland, Iceland, Norway, or Liechtenstein.

There is no immediate plan to release Advanced Voice Mode to free users. (The standard mode remains available to all paid users.)

What steps have been taken to make sure it’s safe?As the company noted upon the initial release in July and again emphasized this week, Advanced Voice Mode has been safety-tested by external experts “who collectively speak a total of 45 different languages, and represent 29 different geographies.” The GPT-4o system card details how the underlying model handles issues like generating violent or erotic speech, imitating voices without their consent, or generating copyrighted content.

Still, OpenAI’s models are not open-source. Compared with such models, which are more transparent about their training data and the “model weights” that govern how the AI produces responses, OpenAI’s closed-source models are harder for independent researchers to evaluate from the perspective of safety, bias, and harm.

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Every day across Hollywood, scores of film school graduates and production assistants work as script readers. Their job is to find the diamonds in the rough from the 50,000 or so screenplays pitched each year and flag any worth pursuing further. Each script runs anywhere from 100 to 150 pages, and it can take half a day to read one and write up a “coverage,” or summary of the strengths and weaknesses. With only about 50 of these scripts selling in a given year, readers are trained to be ruthless.

Now the film-focused tech company Cinelytic, which works with major studios like Warner Bros. and Sony Pictures to analyze film budgets and box office potential, aims to offer script feedback with generative AI.

Today it launched a new tool called Callaia, which amateur writers and professional script readers alike can use to analyze scripts at $79 each. Using AI, it takes Callaia less than a minute to write its own coverage, which includes a synopsis, a list of comparable films, grades for areas like dialogue and originality, and actor recommendations. It also makes a recommendation on whether or not the film should be financed, giving it a rating of “pass,” “consider,” “recommend,” or “strongly recommend.” Though the foundation of the tool is built with ChatGPT’s API, the team had to coach the model on script-specific tasks like evaluating genres and writing a movie’s logline, which summarize the story in a sentence.

“It helps people understand the script very quickly,” says Tobias Queisser, Cinelytic’s cofounder and CEO, who also had a career as a film producer. “You can look at more stories and more scripts, and not eliminate them based on factors that are detrimental to the business of finding great content.”

The idea is that Callaia will give studios a more analytical way to predict how a script may perform on the screen before spending on marketing or production. But, the company says, it’s also meant to ease the bottleneck that script readers create in the filmmaking process. With such a deluge to sort through, many scripts can make it to decision-makers only if they have a recognizable name attached. An AI-driven tool would democratize the script selection process and allow better scripts and writers to be discovered, Queisser says.

The tool’s introduction may further fuel the ongoing Hollywood debate about whether AI will help or harm its creatives. Since the public launch of ChatGPT in late 2022, the technology has drawn concern everywhere from writers’ rooms to special effects departments, where people worry that it will cheapen, augment, or replace human talent.

In this case, Callaia’s success will depend on whether it can provide critical feedback as well as a human script reader can.

That’s a challenge because of what GPT and other AI models are built to do, according to Tuhin Chakrabarty, a researcher who studied how well AI can analyze creative works during his PhD in computer science at Columbia University. In one of his studies, Chakrabarty and his coauthors had various AI models and a group of human experts—including professors of creative writing and a screenwriter—analyze the quality of 48 stories, 12 that appeared in the New Yorker and the rest of which were AI-generated. His team found that the two groups virtually never agreed on the quality of the works.

“Whenever you ask an AI model about the creativity of your work, it is never going to say bad things,” Chakrabarty says. “It is always going to say good things, because it’s trained to be a helpful, polite assistant.”

Cinelytic CTO Dev Sen says this trait did present a hurdle in the design of Callaia, and that the initial output of the model was overly positive. That improved with time and tweaking. “We don’t necessarily want to be overly critical, but aim for a more balanced analysis that points out both strengths and weaknesses in the script,” he says.

Vir Srinivas, an independent filmmaker whose film Orders from Above won Best Historical Film at Cannes in 2021, agreed to look at an example of Callaia’s output to see how well the AI model can analyze a script. I showed him what the model made of a 100-page script about a jazz trumpeter on a journey of self-discovery in San Francisco, which Cinelytic provided. Srinivas says that the coverage generated by the model didn’t go deep enough to present genuinely helpful feedback to a screenwriter.

“It’s approaching the script in too literal a sense and not a metaphorical one—something which human audiences do intuitively and unconsciously,” he says. “It’s as if it’s being forced to be diplomatic and not make any waves.”

There were other flaws, too. For example, Callaia predicted that the film would need a budget of just $5 to $10 million but also suggested that expensive A-listers like Paul Rudd would have been well suited for the lead role.

Cinelytic says it’s currently at work improving the actor recommendation component, and though the company did not provide data on how well its model analyzes a given script, Sen says feedback from 100 script readers who beta-tested the model was overwhelmingly positive. “Most of them were pretty much blown away, because they said that the coverages were on the order of, if not better than, the coverages they’re used to,” he says.

Overall, Cinelytic is pitching Callaia as a tool meant to quickly provide feedback on lots of scripts, not to replace human script readers, who will still read and adjust the tool’s findings. Queisser, who is cognizant that whether AI can effectively write or edit creatively is hotly contested in Hollywood, is hopeful the tool will allow script readers to more quickly identify standout scripts while also providing an efficient source of feedback for writers.

“Writers that embrace our tool will have something that can help them refine their scripts and find more opportunities,” he says. “It’s positive for both sides.”

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The UK’s new moonshot research agency just launched an £81 million ($106 million) program to develop early warning systems to sound the alarm if Earth gets perilously close to crossing climate tipping points.

A climate tipping point is a threshold beyond which certain ecosystems or planetary processes begin to shift from one stable state to another, triggering dramatic and often self-reinforcing changes in the climate system.

The Advanced Research and Invention Agency (ARIA) will announce today that it’s seeking proposals to work on systems for two related climate tipping points. One is the accelerating melting of the Greenland Ice Sheet, which could raise sea levels dramatically. The other is the weakening of the North Atlantic Subpolar Gyre, a huge current rotating counterclockwise south of Greenland that may have played a role in triggering the Little Ice Age around the 14th century.

The goal of the five-year program will be to reduce scientific uncertainty about when these events could occur, how they would affect the planet and the species on it, and over what period those effects might develop and persist. In the end, ARIA hopes to deliver a proof of concept demonstrating that early warning systems can be “affordable, sustainable, and justified.” No such dedicated system exists today, though there’s considerable research being done to better understand the likelihood and consequences of surpassing these and other climate tipping points.

Sarah Bohndiek, a program director for the tipping points research program, says we underappreciate the possibility that crossing these points could significantly accelerate the effects of climate change and increase the dangers, possibly within the next few decades.

By developing an early warning system, “we might be able to change the way that we think about climate change and think about our preparedness for it,” says Bohndiek, a professor of biomedical physics at the University of Cambridge.

ARIA intends to support teams that will work toward three goals: developing low-cost sensors that can withstand harsh environments and provide more precise and needed data about the conditions of these systems; deploying those and other sensing technologies to create “an observational network to monitor these tipping systems”; and building computer models that harness the laws of physics and artificial intelligence to pick up “subtle early warning signs of tipping” in the data.

But observers stress that designing precise early warning systems for either system would be no simple feat and might not be possible anytime soon. Not only do scientists have limited understanding of these systems, but the data on how they’ve behaved in the past is patchy and noisy, and setting up extensive monitoring tools in these environments is expensive and cumbersome.

Still, there’s wide agreement that we need to better understand these systems and the risks that the world may face.

Unlocking breakthroughsIt is clear that the tipping of either of these systems could have huge effects on Earth and its inhabitants.

As the world warmed in recent decades, trillions of tons of ice melted off the Greenland Ice Sheet, pouring fresh water into the North Atlantic, pushing up ocean levels, and reducing the amount of heat that the snow and ice reflected back into space.

Melting rates are increasing as Arctic warming speeds ahead of the global average and hotter ocean waters chip away at ice shelves that buttress land-based glaciers. Scientists fear that as those shelves collapse, the ice sheet will become increasingly unstable.

The complete loss of the ice sheet would raise global sea levels by more than 20 feet (six meters), submerging coastlines and kick-starting mass climate migration around the globe.

But at any point along the way, the influx of water into the North Atlantic could also substantially slow down the convection systems that help to drive the Subpolar Gyre, because fresher water isn’t as dense and prone to sinking. (Saltier, cooler water readily sinks.)

The weakening of the Subpolar Gyre could cool parts of northwest Europe and eastern Canada, shift the jet stream northward, create more erratic weather patterns across Europe, and undermine the productivity of agriculture and fisheries, according to one study last year.

The Subpolar Gyre may also influence the strength of the Atlantic Meridional Overturning Circulation (AMOC), a network of ocean currents that moves massive amounts of heat, salt, and carbon dioxide around the globe. The specifics of how a weakened Subpolar Gyre would affect the AMOC are still the subject of ongoing research, but a dramatic slowdown or shutdown of that system is considered one of the most dangerous climate tipping points. It could substantially cool Northern Europe, among other wide-ranging effects.

The tipping of the AMOC itself, however, is not the focus of the ARIA research program.

The agency, established last year to “unlock scientific and technological breakthroughs,” is a UK answer to the US’s DARPA and ARPA-E research programs. Other projects it’s funding include efforts to develop precision neurotechnologies, improve robot dexterity, and build safer and more energy-efficient AI systems. ARIA is also setting up programs for developing synthetic plants and exploring climate interventions that could cool the planet, including solar geoengineering.

Bohndiek and the other program director of the tipping points program—Gemma Bale, an assistant professor at the University of Cambridge—are both medical physicists who previously focused on developing medical devices. At ARIA, they initially expected to work on efforts to decentralize health care.

But Bohndiek says they soon realized that “a lot of these things that need to change at the individual health level will be irrelevant if climate change truly is going to cross these big thresholds.” She adds, “If we’re going to end up in a society where the world is so much warmer … does the problem of decentralizing health care matter anymore?”

Bohndiek and Bale stress that they hope the program will draw applications from researchers who haven’t traditionally worked on climate change. They add that any research teams proposing to work in or around Greenland must take appropriate steps to engage with local communities, governments, and other research groups.

Tipping dangersEfforts are already underway to develop greater understanding of the Subpolar Gyre and the Greenland Ice Sheet, including the likelihood, timing, and consequences of their tipping into different states.

There are, for instance, regular field expeditions to measure and refine modeling of ice loss in Greenland. A variety of research groups have set up sensor networks that cross various points of the Atlantic to more closely monitor the shifting conditions of current systems. And several studies have already highlighted the appearance of some “early warning signals” of a potential collapse of the AMOC in the coming decades.

But the goal of the ARIA program is to accelerate such research efforts and sharpen the field’s focus on improving our ability to predict tipping events.

William Johns, an oceanographer focused on observation of the AMOC at the University of Miami, says the field is a long way from being able to state confidently that systems like the Subpolar Gyre or AMOC will weaken beyond the bounds of normal natural fluctuations, much less say with any precision when they would do so.

He stresses that there’s still wide disagreement between models on these sorts of questions and limited evidence of what took place before they tipped in the ancient past, all of which makes it difficult to even know what signals we should be monitoring for most closely.

Jaime Palter, an associate professor of oceanography at the University of Rhode Island, adds that she found it a “puzzling” choice to fund a research program focused on the tipping of the Subpolar Gyre. She notes that researchers believe the wind drives the system more than convection, that its connection to the AMOC isn’t well understood, and that the slowdown of the latter system is the one that more of the field is focused on—and more of the world is worried about.

But she and Johns both said that providing funds to monitor these systems more closely is critical to improve scientific understanding of how they work and the odds that they will tip.

Radical interventionsSo what could the world do if ARIA or anyone else does manage to develop systems that can predict, with high confidence, that one of these systems will shift into a new state in, say, the next decade?

Bohndiek stresses that the effects of reaching a tipping point wouldn’t be immediate, and that the world would still have years or even decades to take actions that might prevent the breakdown of such systems, or begin adapting to the changes they’ll bring. In the case of runaway melting of the ice sheet, that could mean building higher seawalls or relocating cities. In the case of the Subpolar Gyre weakening, big parts of Europe might have to look to other areas of the world for their food supplies.

More reliable predictions might also alter people’s thinking about more dramatic interventions, such as massive and hugely expensive engineering projects to prop up ice shelves or to freeze glaciers more stably onto the bedrock they’re sliding upon.

Similarly, they might shift how some people weigh the trade-offs between the dangers of climate change and the risks of interventions like solar geoengineering, which would involve releasing particles in the atmosphere that could reflect more heat back into space.

But some observers note that if enough fresh water is pouring into the Atlantic to weaken the gyre and substantially slow the broader Atlantic current system, there’s very little the world can do to stop it.

“I’m afraid I don’t really see an action you could take,” Johns says. “You can’t go vacuum up all the fresh water—it’s not going to be feasible—and you can’t stop it from melting on the scale we’d have to.”

Bale readily acknowledges that they’ve selected a very hard problem to solve, but she stresses that the point of ARIA research programs is to work at the “edge of the possible.”

“We genuinely don’t know if an early warning system for these systems is possible,” she says. “But I think if it is possible, we know that it would be valuable and important for society, and that’s part of our mission.”

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To tackle complex global problems such as preventing disease and mitigating climate change, we’re going to need new ideas from our brightest minds. Every year, MIT Technology Review identifies a new class of Innovators Under 35 taking on these and other challenges.

On September 10, we will honor the 2024 class of Innovators Under 35. These 35 researchers and entrepreneurs are rising stars in their fields pursuing ambitious projects: One is unraveling the mysteries of how our immune system works, while another is engineering microbes to someday replace chemical pesticides.

Each is doing groundbreaking work to advance one of five areas: materials science, biotechnology, robotics, artificial intelligence, or climate and energy. Some have found clever ways to integrate these disciplines. One innovator, for example, enlists tiny robots to reduce the amount of antibiotics required to treat infections.

MIT Technology Review has published its Innovators Under 35 list since 1999. The first edition was created for our 100th anniversary and was meant to give readers a glimpse into the future, by highlighting what some of the world’s most talented young scientists are working on today.

This year, we’re celebrating our 125th anniversary and honoring this 25th class of innovators with the same goal in mind. (Note: The 2024 list will be made available exclusively to subscribers. If you’re not a subscriber, you can sign up here.)

Keep an eye on The Download newsletter next week for our announcement of the new class. You can also meet some of them at EmTech MIT, which will take place on September 30 and October 1 on MIT’s campus in Cambridge, Massachusetts.

If you can’t wait until then, we’ll reveal our Innovator of the Year during a live broadcast on LinkedIn on Monday, September 9. This person stood out for using their ingenuity to address a power imbalance in the tech sector (and that’s the only hint you get). They’ll join me on screen to talk about their work and share what’s next for their research.

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The Olympic Games in Paris just finished last month and the Paralympics are still underway, so the 2028 Summer Olympics in Los Angeles feel like a lifetime from now. But the prospect of watching the games in his home city has Josh Kahn, a filmmaker in the sports entertainment world who has worked in content creation for both LeBron James and the Chicago Bulls, thinking even further into the future: What might an LA Olympics in the year 3028 look like?

It’s the perfect type of creative exercise for AI video generation, which came into the mainstream with the debut of OpenAI’s Sora earlier this year. By typing prompts into generators like Runway or Synthesia, users can generate fairly high-definition video in minutes. It’s fast and cheap, and it presents few technical obstacles compared with traditional creation techniques like CGI or animation. Even if every frame isn’t perfect—distortions like hands with six fingers or objects that disappear are common—there are, at least in theory, a host of commercial applications. Ad agencies, companies, and content creators could use the technology to create videos quickly and cheaply.

Kahn, who has been toying with AI video tools for some time, used the latest version of Runway to dream up what the Olympics of the future could look like, entering a new prompt in the model for each shot. The video is just over one minute long and features sweeping aerial views of a futuristic version of LA where sea levels have risen sharply, leaving the city crammed right up to the coastline. A football stadium sits perched on top of a skyscraper, while a dome in the middle of the harbor contains courts for beach volleyball.

The video, which was shared exclusively with MIT Technology Review, is meant less as a road map for the city and more as a demonstration of what’s possible now with AI.

“We were watching the Olympics and the amount of care that goes into the cultural storytelling of the host city,” Kahn says. “There’s a culture of imagination and storytelling in Los Angeles that has kind of set the tone for the rest of the world. Wouldn’t it be cool if we could showcase what the Olympics would look like if they returned to LA 1,000 years from now?”

More than anything, the video shows what a boon the generative technology may be for creators. However, it also indicates what’s holding it back. Though Kahn declined to share his prompts for the shots or specify how many prompts it took to get each take right, he did caution that anyone wishing to create good content with AI must be comfortable with trial and error. Particularly challenging in his futuristic project was getting the AI model to think outside the box in terms of architecture. A stadium hovering above water, for example, is not something most AI models have seen many examples of in their training data.

With each shot requiring a new set of prompts, it’s also hard to instill a sense of continuity throughout a video. The color, angle of the sun, and shapes of buildings are difficult for a video generation model to keep consistent. The video also lacks any close-ups of people, which Kahn says AI models still tend to struggle with.

“These technologies are always better on large-scale things right now as opposed to really nuanced human interaction,” he says. For this reason, Kahn imagines that early filmmaking applications of generative video might be for wide shots of landscapes or crowds.

Alex Mashrabov, an AI video expert who left his role as director of generative AI at Snap last year to found a new AI video company called Higgsfield AI, agrees on the current failures and flaws of AI video. He also points out that good dialogue-heavy content is hard to produce with AI, as it tends to hinge upon subtle facial expressions and body language.

Some content creators may be reluctant to adopt generative video simply because of the amount of time required to prompt the models again and again to get the end result right.

“Typically, the success rate is one out of 20,” Mashrabov says, but it’s not uncommon to need 50 or 100 attempts.

For many purposes, though, that’s good enough. Mashrabov says he’s seen an uptick in AI-generated video advertisements from massive suppliers like Temu. In goods-producing countries like China, video generators are in high demand to quickly make in-your-face video ads for particular products. Even if an AI model might require lots of prompts to yield a usable ad, filming it with real people, cameras, and equipment might be 100 times more expensive. Applications like this might be the first use of generative video at scale as the technology slowly improves, he says.

“Although I think this is a very long path, I’m very confident there are low-hanging fruits,” Mashrabov says. “We’re figuring out the genres where generative AI is already good today.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

AI’s impact on elections is being overblown

—Felix M. Simon is a research fellow in AI and News at the Reuters Institute for the Study of Journalism; Keegan McBride is an assistant professor in AI, government, and policy at the Oxford Internet Institute; Sacha Altay is a research fellow in the department of political science at the University of Zurich.

This year, close to half the world’s population has the opportunity to participate in an election. And according to a steady stream of pundits, institutions, academics, and news organizations, there’s a major new threat to the integrity of those elections: artificial intelligence.

The internet is full of doom-laden stories proclaiming that AI-generated deepfakes will mislead and influence voters, as well as enabling new forms of personalized and targeted political advertising.

Though such claims are concerning, it is critical to look at the evidence. With a substantial number of this year’s elections concluded, it is a good time to ask how accurate these assessments have been so far. The preliminary answer seems to be not very. Read the full story.

Here’s how ed-tech companies are pitching AI to teachers

This back-to-school season marks the third year in which AI models like ChatGPT will be used by thousands of students around the globe. A top concern among educators remains that when students use such models to write essays or come up with ideas for projects, they miss out on the hard and focused thinking that builds creative reasoning skills.

But this year, educational technology companies are pitching schools on a different use of AI. Rather than scrambling to tamp down the use of it in the classroom, these companies are coaching teachers how to use AI tools to cut down on time they spend on tasks like grading, providing feedback to students, or planning lessons.

They’re positioning AI as a teacher’s ultimate time saver. But will teachers buy it? And should they? Read the full story.

—James O’Donnell

This story is from The Algorithm, our weekly newsletter covering all the latest developments in AI. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Nvidia is getting into data center design
Not content with dominating chipmaking, it wants to control how they’re used, too. (WSJ $)
+ China’s attempts to build an Nvidia rival aren’t going to plan. (FT $)
+ Things are going from bad to worse for US chipmaker Intel. (Reuters)
+ What’s next in chips. (MIT Technology Review)

2 Brazil’s Supreme Court has voted to uphold the ban on XResidents who seek to bypass the ban using a VPN will face fines. (Bloomberg $)
+ The country’s president says other countries should look to it as an example. (WSJ $)

3 Left-leaning conspiracy theories are on the rise
And they’re likely to increase in the run up to the US Presidential election. (NYT $)+ Hackers linked to the Chinese state impersonated US voters online. (Reuters)

4 Water supplies are at risk of contamination from wildfires
Ash and charred soil can alter watersheds’ conditions—sometimes permanently. (Wired $)
+ Canada’s 2023 wildfires produced more emissions than fossil fuels in most countries. (MIT Technology Review)

5 India is becoming increasingly reliant on China’s imports
Which has US policymakers concerned. (WP $)
+ Taiwan has accused China’s chipmakers of illegally poaching its top talent. (Bloomberg $)

6 Cryptobiotic soil is essential to dryland ecosystemsBut climate change—and overzealous hikers—aren’t helping. (The Atlantic $)

7 Tech firms are paying attention to this reclusive mathematician’s ideasAlexander Grothendieck’s concepts could give AI greater semantic understanding of the world—if they work, that is. (The Guardian)
+ What is AI? (MIT Technology Review)

8 Cutting-edge fishing equipment does away with ropes
But the fishing community isn’t convinced by the fiddly new tech. (Undark Magazine)
+ How fish-safe hydropower technology could keep more renewables on the grid. (MIT Technology Review)

9 ‘Founder mode’ is the latest Silicon Valley buzz-term
Are you a founder or a manager? (Insider $)

10 Everyone hates dynamic pricing models
Everyone except the businesses that profit off them, that is. (Bloomberg $)
+ The British government is investigating their use to sell event tickets. (Wired $)
+ How pricing algorithms learn to collude. (MIT Technology Review)

Quote of the day

“You don’t have to pay pensions to robots.”

—Brian Jones, a foreman at the Port of Philadelphia, tells the New York Times why he fears the inevitable creep of automation into his industry.

The big story

Inside the cozy but creepy world of VR sleep rooms

March 2023

People are gathering in virtual spaces to relax, and even sleep, with their headsets on. VR sleep rooms are becoming popular among people who suffer from insomnia or loneliness, offering cozy enclaves where strangers can safely find relaxation and company—most of the time.

These rooms are created to induce calm. Some imitate beaches and campsites with bonfires, while others mimic hotel rooms or cabins.

The opportunity to sleep in groups can be particularly appealing to isolated or lonely people who want to feel less alone, and safe enough to fall asleep. The trouble is, what if the experience doesn’t make you feel that way? Read the full story.

—Tanya Basu

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)+ Why Hollywood studio tours are raking in the big bucks.
+ The UK’s new Prime Minister is welcoming a new furry friend to Downing Street
+ Here’s how scientists traced back the origin of life on Earth to the last universal common ancestor, known as Luca.
+ There’s no avoiding pop-up restaurants these days.

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This story is from The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here.

This back-to-school season marks the third year in which AI models like ChatGPT will be used by thousands of students around the globe (among them my nephews, who tell me with glee each time they ace an assignment using AI). A top concern among educators remains that when students use such models to write essays or come up with ideas for projects, they miss out on the hard and focused thinking that builds creative reasoning skills.

But this year, more and more educational technology companies are pitching schools on a different use of AI. Rather than scrambling to tamp down the use of it in the classroom, these companies are coaching teachers how to use AI tools to cut down on time they spend on tasks like grading, providing feedback to students, or planning lessons. They’re positioning AI as a teacher’s ultimate time saver.

One company, called Magic School, says its AI tools like quiz generators and text summarizers are used by 2.5 million educators. Khan Academy offers a digital tutor called Khanmigo, which it bills to teachers as “your free, AI-powered teaching assistant.” Teachers can use it to assist students in subjects ranging from coding to humanities. Writing coaches like Pressto help teachers provide feedback on student essays.

The pitches from ed-tech companies often cite a 2020 report from McKinsey and Microsoft, which found teachers work an average of 50 hours per week. Many of those hours, according to the report, consist of “late nights marking papers, preparing lesson plans, or filling out endless paperwork.” The authors suggested that embracing AI tools could save teachers 13 hours per week.

Companies aren’t the only ones making this pitch. Educators and policymakers have also spent the last year pushing for AI in the classroom. Education departments in South Korea, Japan, Singapore, and US states like North Carolina and Colorado have issued guidance for how teachers can positively and safely incorporate AI.

But when it comes to how willing teachers are to turn over some of their responsibilities to an AI model, the answer really depends on the task, according to Leon Furze, an educator and PhD candidate at Deakin University who studies the impact of generative AI on writing instruction and education.

“We know from plenty of research that teacher workload actually comes from data collection and analysis, reporting, and communications,” he says. “Those are all areas where AI can help.”

Then there are a host of not-so-menial tasks that teachers are more skeptical AI can excel at. They often come down to two core teaching responsibilities: lesson planning and grading. A host of companies offer large language models that they say can generate lesson plans to conform to different curriculum standards. Some teachers, including in some California districts, have also used AI models to grade and provide feedback for essays. For these applications of AI, Furze says, many of the teachers he works with are less confident in its reliability.

When companies promise time savings for planning and grading, it is “a huge red flag,” he says, because “those are core parts of the profession.” He adds, “Lesson planning is—or should be—thoughtful, creative, even fun.” Automated feedback on creative skills like writing is controversial too: “Students want feedback from humans, and assessment is a way for teachers to get to know students. Some feedback can be automated, but not all.”

So how eager are teachers to adopt AI to save time? Earlier this year, in May, a Pew research poll found that only 6% of teachers think AI can provide more benefits than harm in education. But with AI changing faster than ever, this school year might be when ed-tech companies start to win them over.

Now read the rest of The Algorithm

Deeper learningHow machine learning is helping us probe the secret names of animals

Until now, only humans, dolphins, elephants, and probably parrots had been known to use specific sounds to call out to other individuals. But now, researchers armed with audio recorders and pattern-recognition software are making unexpected discoveries about the secrets of animal names—at least with small monkeys called marmosets. They’ve found that the animals will adjust the sounds they make in a way that’s specific to whoever they’re “conversing” with at the time.

Why this matters: In years past, it’s been argued that human language is unique and that animals lack both the brains and vocal apparatus to converse. But there’s growing evidence that isn’t the case, especially now that the use of names has been found in at least four distantly related species. Read more from Antonio Regalado.

Bits and bytesHow will AI change the future of sex?

Porn and real-life sex affect each other in a loop. If people become accustomed to getting exactly what they want from erotic media, this could further affect their expectations of relationships. (MIT Technology Review)

There’s a new way to build neural networks that could make AI more understandable

The new method, studied in detail by a group led by researchers at MIT, could make it easier to understand why neural networks produce certain outputs, help verify their decisions, and even probe for bias. (MIT Technology Review)

Researchers built an “AI scientist.” What can it do?

The large language model does everything from reading the literature to writing and reviewing its own papers, but it has a limited range of applications so far. (Nature)

OpenAI is weighing changes to its corporate structure as it seeks more funding

These discussions come as Apple, Nvidia, and Microsoft are considering a funding round that would value OpenAI at more than $100 billion. (Financial Times)

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This year, close to half the world’s population has the opportunity to participate in an election. And according to a steady stream of pundits, institutions, academics, and news organizations, there’s a major new threat to the integrity of those elections: artificial intelligence.

The earliest predictions warned that a new AI-powered world was, apparently, propelling us toward a “tech-enabled Armageddon” where “elections get screwed up”, and that “anybody who’s not worried [was] not paying attention.” The internet is full of doom-laden stories proclaiming that AI-generated deepfakes will mislead and influence voters, as well as enabling new forms of personalized and targeted political advertising. Though such claims are concerning, it is critical to look at the evidence. With a substantial number of this year’s elections concluded, it is a good time to ask how accurate these assessments have been so far. The preliminary answer seems to be not very; early alarmist claims about AI and elections appear to have been blown out of proportion.

While there will be more elections this year where AI could have an effect, the United States being one likely to attract particular attention, the trend observed thus far is unlikely to change. AI is being used to try to influence electoral processes, but these efforts have not been fruitful.Commenting on the upcoming US election, Meta’s latest Adversarial Threat Report acknowledged that AI was being used to meddle—for example, by Russia-based operations—but that “GenAI-powered tactics provide only incremental productivity and content-generation gains” to such “threat actors.” This echoes comments from the company’s president of global affairs, Nick Clegg, who earlier this year stated that “it is striking how little these tools have been used on a systematic basis to really try to subvert and disrupt the elections.”

Far from being dominated by AI-enabled catastrophes, this election “super year” at that point was pretty much like every other election year.

While Meta has a vested interest in minimizing AI’s alleged impact on elections, it is not alone. Similar findings were also reported by the UK’s respected Alan Turing Institute in May. Researchers there studied more than 100 national elections held since 2023 and found “just 19 were identified to show AI interference.” Furthermore, the evidence did not demonstrate any “clear signs of significant changes in election results compared to the expected performance of political candidates from polling data.”

This all raises a question: Why were these initial speculations about AI-enabled electoral interference so off, and what does it tell us about the future of our democracies? The short answer: Because they ignored decades of research on the limited influence of mass persuasion campaigns, the complex determinants of voting behaviors, and the indirect and human-mediated causal role of technology.

First, mass persuasion is notoriously challenging. AI tools may facilitate persuasion, but other factors are critical. When presented with new information, people generally update their beliefs accordingly; yet even in the best conditions, such updating is often minimal and rarely translates into behavioral change. Though political parties and other groups invest colossal sums to influence voters, evidence suggests that most forms of political persuasion have very small effects at best. And in most high-stakes events, such as national elections, a multitude of factors are at play, diminishing the effect of any single persuasion attempt.

Second, for a piece of content to be influential, it must first reach its intended audience. But today, a tsunami of information is published daily by individuals, political campaigns, news organizations, and others. Consequently, AI-generated material, like any other content, faces significant challenges in cutting through the noise and reaching its target audience. Some political strategists in the United States have also argued that the overuse of AI-generated content might make people simply tune out, further reducing the reach of manipulative AI content. Even if a piece of such content does reach a significant number of potential voters, it will probably not succeed in influencing enough of them to alter election results.

Third, emerging research challenges the idea that using AI to microtarget people and sway their voting behavior works as well as initially feared. Voters seem to not only recognize excessively tailored messages but actively dislike them. According to some recent studies, the persuasive effects of AI are also, at least for now, vastly overstated. This is likely to remain the case, as ever-larger AI-based systems do not automatically translate to better persuasion. Political campaigns seem to have recognized this too. If you speak to campaign professionals, they will readily admit that they are using AI, but mainly to optimize “mundane” tasks such as fundraising, get-out-the-vote efforts, and overall campaign operations rather than generating new AI-generated, highly tailored content.

Fourth, voting behavior is shaped by a complex nexus of factors. These include gender, age, class, values, identities, and socialization. Information, regardless of its veracity or origin—whether made by an AI or a human—often plays a secondary role in this process. This is because the consumption and acceptance of information are contingent on preexisting factors, like whether it chimes with the person’s political leanings or values, rather than whether that piece of content happens to be generated by AI.

Concerns about AI and democracy, and particularly elections, are warranted. The use of AI can perpetuate and amplify existing social inequalities or reduce the diversity of perspectives individuals are exposed to. The harassment and abuse of female politicians with the help of AI is deplorable. And the perception, partially co-created by media coverage, that AI has significant effects could itself be enough to diminish trust in democratic processes and sources of reliable information, and weaken the acceptance of election results. None of this is good for democracy and elections.

However, these points should not make us lose sight of threats to democracy and elections that have nothing to do with technology: mass voter disenfranchisement; intimidation of election officials, candidates, and voters; attacks on journalists and politicians; the hollowing out of checks and balances; politicians peddling falsehoods; and various forms of state oppression (including restrictions on freedom of speech, press freedom and the right to protest).

Of at least 73 countries holding elections this year, only 47 are classified as full (or at least flawed) democracies, according to Our World in Data/Economist Democracy Index, with the rest being hybrid or authoritarian regimes. In countries where elections are not even free or fair, and where political choice that leads to real change is an illusion, people have arguably bigger fish to fry.

And still, technology—including AI—often becomes a convenient scapegoat, singled out by politicians and public intellectuals as one of the major ills befalling democratic life. Earlier this year, Swiss president Viola Amherd warned at the World Economic Forum in Davos, Switzerland, that “advances in artificial intelligence allow … false information to seem ever more credible” and present a threat to trust. Pope Francis, too, warned that fake news could be legitimized through AI. US Deputy Attorney General Lisa Monaco said that AI could supercharge mis- and disinformation and incite violence at elections. This August, the mayor of London, Sadiq Kahn, called for a review of the UK’s Online Safety Act after far-right riots across the country, arguing that “the way the algorithms work, the way that misinformation can spread very quickly and disinformation … that’s a cause to be concerned. We’ve seen a direct consequence of this.”

The motivations to blame technology are plenty and not necessarily irrational. For some politicians, it can be easier to point fingers at AI than to face scrutiny or commit to improving democratic institutions that could hold them accountable. For others, attempting to “fix the technology” can seem more appealing than addressing some of the fundamental issues that threaten democratic life. Wanting to speak to the zeitgeist might play a role, too.

Yet we should remember that there’s a cost to overreaction based on ill-founded assumptions, especially when other critical issues go unaddressed. Overly alarmist narratives about AI’s presumed effects on democracy risk fueling distrust and sowing confusion among the public—potentially further eroding already low levels of trust in reliable news and institutions in many countries. One point often raised in the context of these discussions is the need for facts. People argue that we cannot have democracy without facts and a shared reality. That is true. But we cannot bang on about needing a discussion rooted in facts when evidence against the narrative of AI turbocharging democratic and electoral doom is all too easily dismissed. Democracy is under threat, but our obsession with AI’s supposed impact is unlikely to make things better—and could even make them worse when it leads us to focus solely on the shiny new thing while distracting us from the more lasting problems that imperil democracies around the world.

Felix M. Simon is a research fellow in AI and News at the Reuters Institute for the Study of Journalism; Keegan McBride is an assistant professor in AI, government, and policy at the Oxford Internet Institute; Sacha Altay is a research fellow in the department of political science at the University of Zurich.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How “personhood credentials” could help prove you’re a human online

As AI models become better at mimicking human behavior, it’s becoming increasingly difficult to distinguish between real human internet users and sophisticated systems imitating them.

That’s a real problem when those systems are deployed for nefarious ends like spreading misinformation or conducting fraud, and it makes it a lot harder to trust what you encounter online.

A group of researchers have developed a potential solution— a verification concept called ‘personhood credentials’ that proves its holder is a real person, without revealing any further information about their identity. Read the full story to learn how it works.

—Rhiannon Williams

The race to replace the powerful greenhouse gas that underpins the power grid

The power grid is underpinned by a single gas that is used to insulate a range of high-voltage equipment. The problem is, it’s also a super powerful greenhouse gas: a nightmare for climate change.

Sulfur hexafluoride (or SF6) is far from the most common gas that warms the planet, contributing around 1% of warming to date—carbon dioxide and methane are much more well-known and abundant. But emissions of the gas are steadily ticking up every year.

Now, companies are looking to do away with equipment that relies on the gas and searching for replacements that can match its performance. Read the full story.

—Casey Crownhart

Unveiling the 2024 Innovator of the Year

Every year, MIT Technology Review recognizes 35 Innovators Under 35. These young entrepreneurs, researchers, and humanitarians are inventing materials and building systems to help tackle the world’s most pressing problems in biotechnology, computing, and climate science.

On Monday, September 9, we’ll introduce our 2024 Innovator of the Year live on LinkedIn. Join us at 12.30pm ET to find out who it is, and learn about their work and the impact they’re having in this special broadcast ahead of the list’s publication. Register here to be among the first to know!

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 X is a lot quieter without its Brazilian users
The extremely online nation ran many of X’s most popular fan accounts. (NYT $)
+ Brazil’s Supreme Court is under fire from some quarters for banning access to the platform. (FT $)+ The investors who helped Elon Musk buy X are seriously out of pocket. (WP $)

2 China’s online surveillance net is widening
Influencers’ followers are increasingly becoming targets for police interrogation. (The Guardian)
+ How 2023 marked the death of anonymity online in China. (MIT Technology Review) 3 Intel has a plan to revive its fortunes
The once-mighty chipmaker plans to shed as many unnecessary assets as possible. (Reuters)
+ Its sales are shrinking, and rival Nvidia is flourishing. (Bloomberg $)

4 We need much more grid storageEVs haven’t fully taken off, so battery makers are looking to the grid instead. (Economist $)
+ New iron batteries could help. (MIT Technology Review)

5 Dating apps are developing AI wingmen to help you flirtTinder, Hinge, Bumble and Grindr’s new bots will suggest smooth chat-up lines. (FT $)
6 US sanctions are pushing China and Russia to build new payment systemsTo help them skirt the US-dollar-dominated global financial order. (Insider $)
+ Is the digital dollar dead? (MIT Technology Review)

7 These scientists want to store biological samples on the moonSeeds, plant, animal and microbial samples could be safer there than on Earth. (Wired $)
+ Boeing’s Starliner spacecraft is making weird noises. (Ars Technica)
+ Future space food could be made from astronaut breath. (MIT Technology Review)

8 Making video calls from prison is seriously expensiveBut US regulators are finally capping how much private companies can charge. (WSJ $)

9 Hobby apps are exploding in popularity
Social media fatigue is real, and Strava and Letterboxd are reaping the benefits. (Bloomberg $)
+ Want to see what your friends are up to? Check your Venmo. (The Atlantic $)
+ How to fix the internet. (MIT Technology Review)

10 Why AI is such a compelling movie villain
From 2001: A Space Odyssey to the Terminator to the Matrix. (WP $)

Quote of the day

“Pls turn off history.”

—A Google employee tells others to turn off their chat history while discussing sensitive subjects, which the US Federal Government claims is evidence that workers knew to avoid creating a legal paper trail, 404 Media reports.

The big story

The race to produce rare earth materials

January 2024

Abandoning fossil fuels and adopting lower-­carbon technologies are our best options for warding off the accelerating threat of climate change. And access to rare earth elements, key ingredients in many of these technologies, will partly determine which countries will meet their goals for lowering emissions.

Some nations, including the US, are increasingly worried about whether the supply of those elements will remain stable. As a result, scientists and companies alike are intent on increasing access and improving sustainability by exploring secondary or unconventional sources. Read the full story.

—Mureji Fatunde

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Now fall is officially on its way, it’s time to update your autumnal reading list ($)
• I love this image of a neuroscientist and her baby captured by an MRI machine.
• My favorite Olympic sport? Snail racing! You can read more about how the snails power their little cars here (thanks Claire!)
• Marginal gains really do work.

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As AI models become better at mimicking human behavior, it’s becoming increasingly difficult to distinguish between real human internet users and sophisticated systems imitating them.

That’s a real problem when those systems are deployed for nefarious ends like spreading misinformation or conducting fraud, and it makes it a lot harder to trust what you encounter online.

A group of 32 researchers from institutions including OpenAI, Microsoft, MIT and Harvard have developed a potential solution— a verification concept called ‘personhood credentials’ that proves its holder is a real person, without revealing any further information about their identity. The team explored the idea in a non peer-reviewed paper posted to the Arxiv preprint server earlier this month.

Personhood credentials work by doing two things AI systems still cannot do: bypassing state-of-the-art cryptographic systems, and passing as a person in the offline, real world.

To request credentials, a human would have to physically go to one of a number of issuers, which could be a government or other kind of trusted organization, where they would be asked to provide evidence that they’re a real human, such as a passport, or volunteer biometric data. Once they’ve been approved, they’d receive a single credential to store on their devices like users are currently able to store credit and debit cards in smartphones’ Wallet apps.

To use these credentials online, a user could present it to a third party digital service provider who could then verify them using zero-knowledge proofs, a cryptographic protocol that would confirm the holder was in possession of a personhood credential without disclosing any further unnecessary information.

The ability to filter out any non-verified humans on a platform could allow people to choose not to see anything that hasn’t definitely been posted by a human on social media, or filter out Tinder matches that don’t come with personhood credentials, for example.

The authors want to encourage governments, companies and standards bodies to consider adopting it in the future to prevent AI deception ballooning out of our control.

“AI is everywhere. There will be many issues, many problems, and many solutions,” says Tobin South, a PhD student at MIT who worked on the project. “Our goal is not to prescribe this to the world, but to open the conversation about why we need this and how it could be done.”

Possible technical options already exist. For example, a network called Idena claims to be the first blockchain proof-of-person system. It works by getting humans to solve puzzles that would prove difficult for bots within a short time frame. The controversial Worldcoin program, which collects users’ biometric data, bills itself as the world’s largest privacy-preserving human identity and financial network. It recently partnered with the Malaysian government to provide proof of humanness online by scanning users’ irises, which creates a code. Like the personhood credentials concept, each code is protected using cryptography.

However, the project has been criticized for deceptive marketing practices, collecting more personal data than acknowledged, and failing to obtain meaningful consent from users. Regulators in Hong Kong and Spain banned Worldcoin from operating earlier this year, while its operations have been suspended in countries including Brazil, Kenya, and India.

So there remains a need for fresh solutions. The rapid rise of accessible AI tools has ushered in a dangerous period when internet users are hyper-suspicious about what is and isn’t true online, says Henry Ajder, an expert on AI and deepfakes and adviser to Meta and the UK government. And while ideas for verifying personhood have been around for some time, these credentials feel like one of the most substantive visions of how to push back against encroaching skepticism, he says.

But the biggest challenge the credentials will face is getting enough adoption from platforms, digital services and governments, who may feel uncomfortable conforming to a standard they don’t control. “For this to work effectively, it would have to be something which is universally adopted,” he says. “In principle the technology is quite compelling, but in practice and the messy world of humans and institutions, I think there would be quite a lot of resistance.”

Martin Tschammer, head of security at startup Synthesia, which creates AI-generated hyperrealistic deepfakes, says he agrees with the principle driving personhood credentials: the need to verify humans online. However, he is unsure whether it’s the right solution or how practical it would be to implement. He also expressed skepticism over who would run such a scheme.

“We may end up in a world in which we centralize even more power and concentrate decision-making over our digital lives, giving large internet platforms even more ownership over who can exist online and for what purpose,” he says. “And, given the lackluster performance of some governments in adopting digital services and autocratic tendencies that are on the rise, is it practical or realistic to expect this type of technology to be adopted en masse and in a responsible way by the end of this decade?”

Rather than waiting for collaboration across industry, Synthesia is currently evaluating how to integrate other personhood-proving mechanisms into its products. He says it already has several measures in place: For example, it requires businesses to prove that they are legitimate registered companies, and will ban and refuse to refund customers found to have broken its rules.

One thing is clear: we are in urgent need of methods to differentiate humans from bots, and encouraging discussions between tech and policy stakeholders is a step in the right direction, says Emilio Ferrara, a professor of computer science at the University of Southern California, who was also not involved in the project.

“We’re not far from a future where, if things remain unchecked, we’re going to be essentially unable to tell apart interactions that we have online with other humans or some kind of bots. Something has to be done,” he says. “We can’t be naive as previous generations were with technologies.”

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The power grid is underpinned by a single gas that is used to insulate a range of high-voltage equipment. The problem is, it’s also a super powerful greenhouse gas, a nightmare for climate change.

Sulfur hexafluoride (or SF6) is far from the most common gas that warms the planet, contributing around 1% of warming to date—carbon dioxide and methane are much more well-known and abundant. However, like many other fluorinated gases, SF6 is especially potent: It traps about 20,000 times more energy than carbon dioxide does over the course of a century, and it can last in the atmosphere for 1,000 years or more.

Despite their relatively small contributions so far, emissions of the gas are ticking up, and the growth rate has been climbing every year. SF6 emissions in China nearly doubled between 2011 and 2021, accounting for more than half the world’s emissions of the gas.

Now, companies are looking to do away with equipment that relies on the gas and searching for replacements that can match its performance. Last week, Hitachi Energy announced it’s producing new equipment that replaces SF6 with other materials. And there’s momentum building to ban SF6 in the power industry, including a recently passed plan in the European Union that will phase out the gas’s use in high-voltage equipment by 2032.

As equipment manufacturers work to produce alternatives, some researchers say that we should go even further and are trying to find solutions that avoid fluorine-containing materials entirely.

High voltage, high stakesYou probably have a circuit-breaker box in your home—if a circuit gets overloaded, the breaker flips, stopping the flow of electricity. The power grid has something similar, called switchgear.

The difference is, it often needs to handle something like a million times more energy than your home’s equipment does, says Markus Heimbach, executive vice president and managing director of the high-voltage products business unit at Hitachi Energy. That’s because parts of the power grid operate at high voltages, allowing them to move energy around while losing as little as possible. Those high voltages require careful insulation at all times and safety measures in case something goes wrong.

Some switchgear uses the same materials as your home circuit-breaker boxes—there’s air around it to insulate it. But when it’s scaled up to handle high voltage, it ends up being gigantic and requiring a large land footprint, making it inconvenient for larger, denser cities.

The solution today is SF6, “a super gas, from a technology point of view,” Heimbach says. It’s able to insulate equipment during normal operation and help interrupt current when needed. And the whole thing has a much smaller footprint than air-insulated equipment.

The problem is, small amounts of SF6 leak out of equipment during normal operation, and more can be released during a failure or when old equipment isn’t handled properly. When the gas escapes, its strong ability to trap heat and the fact that it has such a long lifetime makes it a menace in the atmosphere.

Some governments will soon ban the gas for the power industry, which makes up the vast majority of the emissions. The European Union agreed to ban SF6-containing medium-voltage switchgear by 2030, and high-voltage switchgear that uses the gas by 2032. Several states in the US have proposed or adopted limits and phaseouts.

Making changes Hitachi Energy recently announced it’s producing high-voltage switchgear that can handle up to 550 kilovolts (kV). The model follows products rated for 420 kV the company began installing in 2023—there are more than 250 booked by customers today, Heimbach says.

Hitachi Energy’s new switchgear substitutes SF6 with a gas mixture that contains mostly carbon dioxide and oxygen. It works as well as SF6 and is as safe and reliable but with a much lower global warming potential, trapping 99% less energy in the atmosphere, Heimbach says.

However, for some of its new equipment, Hitachi Energy still uses some C4-fluoronitriles, which helps with insulation, Heimbach says. This gas is present at a low fraction, less than 5% of the mixture, and it’s less potent than SF6, Heimbach says. But C4-fluoronitriles are still powerful greenhouse gases, up to a few thousand times more potent than carbon dioxide. These and other fluorinated substances could soon be in trouble too—chemical giant 3M announced in late 2022 that the company would stop manufacturing all fluoropolymers, fluorinated fluids, and PFAS-additive products by 2025.

In order to eliminate the need for fluorine-containing gases, some researchers are looking into the grid’s past for alternatives. “We know that there’s no one-for-one replacement gas that has the properties of SF6,” says Lukas Graber, an associate professor in electrical engineering at Georgia Institute of Technology.

SF6 is both extremely stable and extremely electronegative, meaning it tends to grab onto free electrons, and nothing else can quite match it, Graber says. So he’s working on a research project that aims to replace SF6 gas with supercritical carbon dioxide. (Supercritical fluids are those at temperatures and pressures so high that distinct liquid and gas phases don’t quite exist.) The inspiration came from equipment that used to use oil-based materials—instead of trying to grab electrons like SF6, supercritical carbon dioxide can basically slow them down.

Graber and his research team received project funding from the US Department of Energy’s Advanced Research Projects Agency for Energy. The first small-scale prototype is nearly finished, he adds, and the plan is to test out a full-scale prototype in 2025.

Utilities are known for being conservative, since the safety and reliability of the electrical grid have high stakes, Hitachi Energy’s Heimbach says. But with more SF6 bans coming, they’ll need to find and adopt solutions that don’t rely on the gas.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How machine learning is helping us probe the secret names of animals

The news: Do animals have names? It seems so, after new research appears to have discovered that small monkeys called marmosets “vocally label” their monkey friends with specific sounds.

How they did it: The team used audio recorders and pattern-recognition software to analyze the animals’ high-pitched chirps and twitters. To prove they’d cracked the monkey code—and learned the secret names—the team played recordings at the marmosets through a speaker and found they responded more often when their label, or name, was in the recording.

Why it matters: Until now, only humans, dolphins, elephants, and probably parrots had been known to use specific sounds to call out to other individuals. This sort of research could provide clues to the origins of human language, arguably the most powerful innovation in our species’ evolution. Read the full story.

—Antonio Regalado

A new smart mask analyzes your breath to monitor your health

Your breath can give away a lot about you. Each exhalation contains all sorts of compounds, including possible biomarkers for disease or lung conditions, that could give doctors a valuable insight into your health.

Now a new smart mask could help doctors check your breath for these signals continuously and in a noninvasive way. A patient could wear the mask at home, measure their own levels, and then go to the doctor if a flare-up is likely. Read the full story.

—Scott J Mulligan

A new way to build neural networks could make AI more understandable

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost unchanged for decades. While these networks give modern artificial intelligence its power, they are also inscrutable.

Existing artificial neurons, used in large language models like GPT4, work by taking in a large number of inputs, adding them together, and converting the sum into an output using another mathematical operation inside the neuron. Combinations of such neurons make up neural networks, and their combined workings can be difficult to decode.

But the new way to combine neurons works a little differently—and should be easier to make sense of. Read the full story.

—Anil Ananthaswamy

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The arrest of Telegram’s founder is unsettling Silicon Valley
It’s opened encryption up to new levels of scrutiny. (NYT $)+ Defenders of encryption fear the case will embolden authorities to attack it. (WP $)

2 Publishers are opting out of Apple’s AI scrapingApple gave major sites the choice to hand over their data, so they’ve said no. (Wired $)
+ It is also reported to be investing in AI giant OpenAI. (WSJ $)
+ Apple is poised to release new AI features in its next iOS update. (MIT Technology Review)

3 Brazil is going after Elon Musk
A judge has vowed to shut down X and has blocked Starlink’s bank accounts. (Bloomberg $)+ Musk is waging an ongoing battle with Brazil’s Supreme Court justice. (FT $)

4 Schools are still grappling with AITeachers are split over whether using the tools constitutes cheating or not. (New Yorker $)
+ ChatGPT is going to change education, not destroy it. (MIT Technology Review)

5 US regulators are rethinking cancer drug dosing in clinical trials
The FDA wants drugmakers to reexamine their dosing. Startups are worried. (WSJ $)
+ Cancer vaccines are having a renaissance. (MIT Technology Review)

6 We’re learning more about the proteins that regulate our genesIt looks as though they’ve been secretly managing our cells, too. (Knowable Magazine)

7 This company teaches gas-fueled car owners how to convert them into EVsBut retrofitting vehicles comes with some pretty major risks. (Rest of World)
+ Why EV charging needs more than Tesla. (MIT Technology Review)

8 Meta’s AI assistant is steadily growing more popularIt’s got around 400 million monthly users. (The Information $)
+ But arch rival OpenAI has around 200 million weeklyusers. (Axios)

9 LA’s new arena is fully digitized
Facial recognition cameras are everywhere, and good luck buying anything without its official app. (The Atlantic $)

10 Algorithm-driven music recommendations are hit and miss
Here’s some different ways to find new tunes. (WP $)
+ How to break free of Spotify’s algorithm. (MIT Technology Review)

Quote of the day

“You’re going to be left with crypto scams and rapid weight-loss adverts.”

—An insider tells the Financial Times how Telegram’s recent legal troubles are likely to deter advertisers from wanting to work with the platform.

The big story

What’s next for the world’s fastest supercomputers

September 2023

When the Frontier supercomputer came online last year, it marked the dawn of so-called exascale computing, with machines that can execute an exaflop—or a quintillion (1018) floating point operations a second.

Since then, scientists have geared up to make more of these blazingly fast computers: several exascale machines are due to come online in the US and Europe in 2024.

But speed itself isn’t the endgame. Researchers hope to pursue previously unanswerable questions about nature—and to design new technologies in areas from transportation to medicine. Read the full story.

—Sophia Chen

We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• If you’ve ever struggled to erect a tent, just know the world record for putting one up is one minute and seven seconds.
• Are we all beer girls now?
• Traveling art exhibitions are big business these days.
• Brace yourself: fall is coming.

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A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost unchanged for decades. While these networks give modern artificial intelligence its power, they are also inscrutable.

Existing artificial neurons, used in large language models like GPT4, work by taking in a large number of inputs, adding them together, and converting the sum into an output using another mathematical operation inside the neuron. Combinations of such neurons make up neural networks, and their combined workings can be difficult to decode.

But the new way to combine neurons works a little differently. Some of the complexity of the existing neurons is both simplified and moved outside the neurons. Inside, the new neurons simply sum up their inputs and produce an output, without the need for the extra hidden operation. Networks of such neurons are called Kolmogorov-Arnold Networks (KANs), after the Russian mathematicians who inspired them.

The simplification, studied in detail by a group led by researchers at MIT, could make it easier to understand why neural networks produce certain outputs, help verify their decisions, and even probe for bias. Preliminary evidence also suggests that as KANs are made bigger, their accuracy increases faster than networks built of traditional neurons.

“It’s interesting work,” says Andrew Wilson, who studies the foundations of machine learning at New York University. “It’s nice that people are trying to fundamentally rethink the design of these [networks].”

The basic elements of KANs were actually proposed in the 1990s, and researchers kept building simple versions of such networks. But the MIT-led team has taken the idea further, showing how to build and train bigger KANs, performing empirical tests on them, and analyzing some KANs to demonstrate how their problem-solving ability could be interpreted by humans. “We revitalized this idea,” said team member Ziming Liu, a PhD student in Max Tegmark’s lab at MIT. “And, hopefully, with the interpretability… we [may] no longer [have to] think neural networks are black boxes.”

While it’s still early days, the team’s work on KANs is attracting attention. GitHub pages have sprung up that show how to use KANs for myriad applications, such as image recognition and solving fluid dynamics problems.

Finding the formulaThe current advance came when Liu and colleagues at MIT, Caltech, and other institutes were trying to understand the inner workings of standard artificial neural networks.

Today, almost all types of AI, including those used to build large language models and image recognition systems, include sub-networks known as a multilayer perceptron (MLP). In an MLP, artificial neurons are arranged in dense, interconnected “layers.” Each neuron has within it something called an “activation function”—a mathematical operation that takes in a bunch of inputs and transforms them in some pre-specified manner into an output.

In an MLP, each artificial neuron receives inputs from all the neurons in the previous layer and multiplies each input with a corresponding “weight” (a number signifying the importance of that input). These weighted inputs are added together and fed to the activation function inside the neuron to generate an output, which is then passed on to neurons in the next layer. An MLP learns to distinguish between images of cats and dogs, for example, by choosing the correct values for the weights of the inputs for all the neurons. Crucially, the activation function is fixed and doesn’t change during training.

Once trained, all the neurons of an MLP and their connections taken together essentially act as another function that takes an input (say, tens of thousands of pixels in an image) and produces the desired output (say, 0 for cat and 1 for dog). Understanding what that function looks like, meaning its mathematical form, is an important part of being able to understand why it produces some output. For example, why does it tag someone as creditworthy given inputs about their financial status? But MLPs are black boxes. Reverse-engineering the network is nearly impossible for complex tasks such as image recognition.

And even when Liu and colleagues tried to reverse-engineer an MLP for simpler tasks that involved bespoke “synthetic” data, they struggled.

“If we cannot even interpret these synthetic datasets from neural networks, then it’s hopeless to deal with real-world data sets,” says Liu. “We found it really hard to try to understand these neural networks. We wanted to change the architecture.”

Mapping the mathThe main change was to remove the fixed activation function and introduce a much simpler learnable function to transform each incoming input before it enters the neuron.

Unlike the activation function in an MLP neuron, which takes in numerous inputs, each simple function outside the KAN neuron takes in one number and spits out another number. Now, during training, instead of learning the individual weights, as happens in an MLP, the KAN just learns how to represent each simple function. In a paper posted this year on the preprint server ArXiv, Liu and colleagues showed that these simple functions outside the neurons are much easier to interpret, making it possible to reconstruct the mathematical form of the function being learned by the entire KAN.

The team, however, has only tested the interpretability of KANs on simple, synthetic data sets, not on real-world problems, such as image recognition, which are more complicated. “[We are] slowly pushing the boundary,” says Liu. “Interpretability can be a very challenging task.”

Liu and colleagues have also shown that KANs get more accurate at their tasks with increasing size faster than MLPs do. The team proved the result theoretically and showed it empirically for science-related tasks (such as learning to approximate functions relevant to physics). “It’s still unclear whether this observation will extend to standard machine learning tasks, but at least for science-related tasks, it seems promising,” Liu says.

Liu acknowledges that KANs come with one important downside: it takes more time and compute power to train a KAN, compared to an MLP.

“This limits the application efficiency of KANs on large-scale data sets and complex tasks,” says Di Zhang, of Xi’an Jiaotong-Liverpool University in Suzhou, China. But he suggests that more efficient algorithms and hardware accelerators could help.

Anil Ananthaswamy is a science journalist and author who writes about physics, computational neuroscience, and machine learning. His new book, WHY MACHINES LEARN: The Elegant Math Behind Modern AI, was published by Dutton (Penguin Random House US) in July.

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Your breath can give away a lot about you. Each exhalation contains all sorts of compounds, including possible biomarkers for disease or lung conditions, that could give doctors a valuable insight into your health.

Now a new smart mask, developed by a team at the California Institute of Technology, could help doctors check your breath for these signals continuously and in a noninvasive way. A patient could wear the mask at home, measure their own levels, and then go to the doctor if a flare-up is likely.

“They don’t have to come to the clinic to assess their inflammation level,” says Wei Gao, professor of Medical Engineering at Caltech and one of the smart mask’s creators. “This can be lifesaving.”

The smart mask, details of which were published in Science today, uses a two-part cooling system to chill the breath of its wearer. The cooling turns the breath into exhaled breath condensate (EBC).

EBC, essentially a liquid version of someone’s breath, is easier to analyze, because biomarkers like nitrite and alcohol content are more concentrated in a liquid than in a gas. The mask design takes inspiration from plants’ capillary abilities, using a series of microfluidic modules that create pressure to push the EBC fluid around to sensors in the mask.

The sensors are connected via Bluetooth to a device like a phone, where the patient has access to real-time health readings.

“The biggest challenge has always been collecting real-time samples. This problem has been solved. That’s a paradigm shift,” says Rajan Chakrabarty, professor of Environmental and Chemical Engineering at Washington University in St. Louis and who was not involved in the research.

The Caltech team tested the smart mask with patients, including several who had chronic obstructive pulmonary disease (COPD) or asthma or had just gotten over a covid-19 infection. They were testing the masks for comfort and breathability, but they also wanted to see if the masks actually worked at tracking useful biomarkers throughout a patient’s daily activities, such as exercise and work.

The mask picked up on higher levels of nitrite in patients who had asthma or other conditions that involved inflamed airways. It also picked up on higher alcohol content after a patient went out drinking, which demonstrates another potential application of the mask. Analyzing breath this way is more accurate than the typical breathalyzer test, which involves a patient blowing into a device. Blowing can produce imprecise results due to alcohol in saliva being spit out.

The researchers hope this is just the beginning. They plan to test the masks on a larger population, and if all goes well, commercialize the masks to get them out to a wider audience. They hope the mask will be a platform for broader application, where sensors for a range of biomarkers could be slotted in and out.

“What I would like to be able to do is take off their sensors, put in my sensors, and this becomes the building block for doing all other types of development,” says Albert Titus, professor and chair of the Department of Biomedical Engineering at the University at Buffalo and who wasn’t part of the Caltech team. “That’s where I’d like to see it go.”

For example, there may be the possibility to measure ketones in the breath, a high level of which is a sign of diabetes, or glucose levels, to help people with diabetes monitor their condition.

“The mask can be reconfigured for many different applications,” says Gao.

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Do animals have names? According to the poet T.S. Eliot, cats have three: the name their owner calls them (like George); a second, more noble one (like Quaxo or Cricopat); and, finally, a “deep and inscrutable” name known only to themselves “that no human research can discover.”

But now, researchers armed with audio recorders and pattern-recognition software are making unexpected discoveries about the secrets of animal names—at least with small monkeys called marmosets.

That’s according to a team at Hebrew University in Israel, who claim in the journal Science this week they’ve discovered that marmosets “vocally label” their monkey friends with specific sounds.

Until now, only humans, dolphins, elephants, and probably parrots had been known to use specific sounds to call out to other individuals.

Marmosets are highly social creatures that maintain contact through high-pitched chirps and twitters called “phee-calls.” By recording different pairs of monkeys placed near each other, the team in Israel says they found the animals will adjust their sounds toward a vocal label that’s specific to their conversation partner.

“It’s similar to names in humans,” says David Omer, the neuroscientist who led the project. “There’s a typical time structure to their calls, and what we report is that the monkey fine-tunes it to encode an individual.”

These names aren’t really recognizable to the human ear; instead, they were identified via a “random forest,” the statistical machine learning technique Omer’s team used to cluster, classify, and analyze the sounds.

To prove they’d cracked the monkey code—and learned the secret names—the team played recordings at the marmosets through a speaker and found they responded more often when their label, or name, was in the recording.

This sort of research could provide clues to the origins of human language, which is arguably the most powerful innovation in our species’ evolution, right up there with opposable thumbs. In years past, it’s been argued that human language is unique and that animals lack both the brains and vocal apparatus to converse.

But there’s growing evidence that isn’t the case, especially now that the use of names has been found in at least four distantly related species. “This is very strong evidence that the evolution of language was not a singular event,” says Omer.

Some similar research tactics were reported earlier this year by Mickey Pardo, a postdoctoral researcher, now at Cornell University, who spent 14 months in Kenya recording elephant calls. Elephants sound alarms by trumpeting, but in reality most of their vocalizations are deep rumbles that are only partly audible to humans.

Pardo also found evidence that elephants use vocal labels, and he says he can definitely get an elephant’s attention by playing the sound of another elephant addressing it. But does this mean researchers are now “speaking animal”?

Not quite, says Pardo. Real language, he thinks, would mean the ability to discuss things that happened in the past or string together more complex ideas. Pardo says he’s hoping to determine next if elephants have specific sounds for deciding which watering hole to visit—that is, whether they employ place names.

Several efforts are underway to discover if there’s still more meaning in animal sounds than we thought. This year, a group called Project CETI that’s studying the songs of sperm whales found they are far more complex than previously recognized. It means the animals, in theory, could be using a kind of grammar—although whether they actually are saying anything specific isn’t known.

Another effort, the Earth Species Project, aims to use “artificial intelligence to decode nonhuman communication” and has started helping researchers collect more data on animal sounds to feed into those models.

The team in Israel say they will also be giving the latest types of artificial intelligence a try. Their marmosets live in a laboratory facility, and Omer says he’s already put microphones in monkeys’ living space in order to record everything they say, 24 hours a day.

Their chatter, Omer says, will be used to train a large language model that could, in theory, be used to finish a series of calls that a monkey started, or produce what it predicts is an appropriate reply. But will a primate language model actually make sense, or will it just gibber away without meaning?

Only the monkeys will be able to say for sure.

“I don’t have any delusional expectations that they will talk about Nietzsche,” says Omer. “I don’t expect it to be extremely complex like a human, but I would expect it to help us understand something about how our language developed.”

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When someone loses part of a leg, a prosthetic can make it easier to get around. But most prosthetics are static, cumbersome, and hard to move. Now a new neural interface developed by MIT researchers and colleagues connects a bionic lower limb to nerve endings in the thigh, allowing it to be controlled by the brain so that it feels more like a natural body part.

“When you ask a patient ‘What is your body?’ they don’t include the prosthesis,” says MIT biophysicist Hugh Herr, SM ’93, one of the lead authors on the study, who lost both his lower legs in a climbing accident when he was 17. He says linking the brain to the prosthesis can have a positive emotional impact.

Getting the neural interface hooked up to a prosthetic takes two steps. First is surgery involving the portions of muscle that remain after a lower-leg amputation. The operation reconnects shin muscle, which contracts to make the ankle flex upward, to calf muscle, which counteracts this movement. The prosthetic can also be fitted at this point. In addition to enabling the prosthetic to move more dynamically, the procedure can reduce phantom-limb pain, and patients are less likely to trip and fall.

“The surgery stands on its own,” says Amy Pietrafitta, a para-athlete who received it in 2018. “I feel like I have my leg back.” But natural movements are still limited when the prosthetic isn’t connected to the nervous system.

In step two, surface electrodes measure nerve activity from the brain to the calf and shin muscles, indicating an intention to move the lower leg. A small computer in the bionic leg decodes those nerve signals and moves the leg accordingly.

“If you have intact biological limbs, you can walk up and down steps, for example, and not even think about it. It’s involuntary,” says Herr. “That’s the case with our patients, but their limb is made of titanium and silicone.”

The authors assessed the mobility of seven people using a neural interface and seven who’d had conventional amputations, all using the same type of prosthetic limb. Those with the neural interface could walk 41% faster and climb sloped surfaces and steps. They could also dodge obstacles more nimbly and had better balance. And they described feeling that the prosthetic was truly a part of their body rather than just a tool that they used to get around.

The procedure has become the standard of care at Brigham and Women’s Hospital in Boston. But the surface electrodes that give patients full neural control of their limbs are a few years away from being clinically implemented, and the interfaces have only been used in laboratory settings so far. Another limitation is that the muscle reattachment could be less effective if it’s done several years after an amputation.

Herr and his team hope to eventually replace the prosthetic’s surface electrodes with magnetic spheres, which can more accurately track muscle dynamics. “The goal that we have is to really reconstruct bodies, to rebuild bodies,” he says.

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Cloud has become a given for most organizations: according to PwC’s 2023 cloud business survey, 78% of companies have adopted cloud in most or all parts of the business. These companies have migrated on-premises systems to the cloud seeking faster time to market, greater scalability, cost savings, and improved collaboration.

Yet while cloud adoption is widespread, research by McKinsey shows that companies’ concerns around the resiliency and reliability of cloud operations, coupled with an ever-evolving regulatory environment, are limiting their ability to derive full value from the cloud. As the value of a business’s data grows ever clearer, the stakes of making sure that data is resilient are heightened. Business leaders now justly fear that they might run afoul of mounting data regulations and compliance requirements, that bad actors might target their data in a ransomware attack, or that an operational disruption affecting their data might grind the entire business to a halt.

For all its competitive advantages, moving to the cloud presents unique challenges for data resilience. In fact, the qualities of cloud that make it so appealing to businesses—scalability, flexibility, and the ability to handle rapidly changing data—are the same ones that make it challenging to ensure the resilience of mission-critical applications and their data in the cloud.

DOWNLOAD THE REPORT“A widely held misconception is that the durability of the cloud automatically protects your data,” says Rick Underwood, CEO of Clumio, a backup and recovery solutions provider. “But a multitude of factors in cloud environments can still reach your data and wipe it out, maliciously encrypt it, or corrupt it.”

Complicating matters is that moving data to the cloud can lead to reduced data visibility, as individual teams begin creating their own instances and IT teams may not be able to see and track all the organization’s data. “When you make copies of your data for all of these different cloud services, it’s very hard to keep track of where your critical information goes and what needs to be compliant,” says Underwood. The result, he adds, is a “Wild West in terms of identifying, monitoring, and gaining overall visibility into your data in the cloud. And if you can’t see your data, you can’t protect it.”

The end of traditional backup architectureUntil recently, many companies relied on traditional backup architectures to protect their data. But the inability of these backup systems to handle vast volumes of cloud data—and scale to accommodate explosive data growth—is becoming increasingly evident, particularly to cloud-native enterprises. In addition to issues of data volume, many traditional backup systems are ill-equipped to handle the sheer variety and rate of change of today’s enterprise data.

In the early days of cloud, Steven Bong, founder and CEO of AuditFile, had difficulty finding a backup solution that could meet his company’s needs. AuditFile supplies audit software for certified public accountants (CPAs) and needed to protect their critical and sensitive audit work papers. “We had to back up our data somehow,” he says. “Since there weren’t any elegant solutions commercially available, we had a home-grown solution. It was transferring data, backing it up from different buckets, different regions. It was fragile. We were doing it all manually, and that was taking up a lot of time.”

Frederick Gagle, vice president of technology for BioPlus Specialty Pharmacy, notes that backup architectures that weren’t designed for cloud don’t address the unique features and differences of cloud platforms. “A lot of backup solutions,” he says, “started off being on-prem, local data backup solutions. They made some changes so they could work in the cloud, but they weren’t really designed with the cloud in mind, so a lot of features and capabilities aren’t native.”

Underwood agrees, saying, “Companies need a solution that’s natively architected to handle and track millions of data operations per hour. The only way they can accomplish that is by using a cloud-native architecture.”

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Kamala Harris should stand with tech workers, not their bosses

—Stephen McMurtry is a Google Software Engineer and Communications Chair of the Alphabet Workers Union-CWA

Tangled up in the contest to be the next US president, there is another battle brewing: Silicon Valley vs. Silicon Valley. In Donald Trump’s corner are venture capitalists like Marc Andreessen and Peter Thiel, along with executives like Elon Musk. In the other are execs like LinkedIn founder Reid Hoffman and SV Angel investing mogul Ron Conway, who are backing Kamala Harris. Democracy appears to be at stake, and the weapon of choice is cold hard cash.

Yet as an elected board member of the Alphabet Workers Union, an affiliate of the Communications Workers of America, I urge Americans to take a step back and look critically at the picture in front of us. No matter who wins in November, Silicon Valley’s bosses are positioning themselves for victory.

Tech’s elite have long been the biggest winners in the US economy, and the movement to organize tech workers seeks to hold that elite accountable. If the next president favors our bosses’ interests over our own, the consequences could be dire for all working people in this country and many others.

We know how to fight back against a future Trump administration because we have been there before. What’s less clear is whether and to what extent we can count on a Harris administration to be our ally. Read the full story.

Canada’s 2023 wildfires produced more emissions than fossil fuels in most countries

Last year’s Canadian wildfires smashed records, burning about seven times more land in Canada’s forests than the annual average over the previous four decades. Eight firefighters were killed and 180,000 people displaced.

Now a new study reveals how these blazes can create a vicious cycle, contributing to climate change even as climate-fueled conditions make for worse wildfire seasons.

Emissions from 2023’s Canadian wildfires reached 647 million metric tons of carbon—the equivalent of the world’s fourth-highest emitter, following only China, the US, and India, if the fires were a country. The sky-high emissions from the fires reveals how human activities are pushing natural ecosystems to a place that’s making things tougher for our climate efforts. Read the full story.

—Casey Crownhart

This story is from The Spark, our weekly newsletter giving you the inside track on all the latest climate tech innovations. Sign up to receive it in your inbox every Wednesday.

AI’s growth needs the right interface

If you took a walk in Hayes Valley, San Francisco’s epicenter of AI froth, and asked the first dude-bro you saw about the future of the interface, he’d probably say something about the movie Her, about chatty virtual assistants that will help you do everything from organize your email to book a trip to Coachella.

Nonsense. Setting aside that Her was about how technology manipulates us into a one-sided relationship, you’d have to be pudding-brained to believe that chatbots are the best way to use computers. The real opportunity is close, but it isn’t chatbots.

Instead, it’s computers built atop the visual interfaces we know, but which we can interact with more fluidly, through whatever combination of voice and touch is most natural. Crucially, this won’t just be a computer that we can use. It’ll also be a computer that empowers us to break and remake it, to whatever ends we want. Read the full story.

—Cliff Kuang

This piece is from the latest print issue of MIT Technology Review, which is celebrating 125 years of the magazine! If you don’t already, subscribe now to get 25% off future copies once they land.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Telegram’s founder has been charged with a range of crimes
Pavel Durov is being investigated for his complicity in criminal activity on the app. (NYT $)
+ Although he’s been granted bail, he’s not allowed to leave France. (BBC)

2 California lawmakers have passed the AI safety bill
Now it’s up to the state’s governor to decide whether to sign it into law. (WP $)

3 Chinese EVs are going offline when their makers go bust
Owners are left unable to log into their car systems or start their engines. (Rest of World)
+ Why China’s EV ambitions need virtual power plants. (MIT Technology Review)

4 Nvidia is the new Apple
Its fervent superfans are throwing parties to celebrate its quarterly earnings. (NY Mag $)
+ But its colossal revenue failed to match Wall Street’s expectations. (FT $)
+ The AI boom is showing no sign of slowing. (WP $)

5 Meta is considering making new mixed-reality glasses
The headset, codenamed Puffin, is a hybrid of its Meta Quest VR headset and Meta’s Ray-Ban smart glasses. (The Information $)
+ It looks like Midjourney is opening up a hardware division. (Ars Technica)

6 Global deaths from hepatitis B and C are on the rise
Despite the development of promising new treatments. (Vox)
+ There was a mysterious surge of hepatitis in children two years ago. (MIT Technology Review)

7 Google says it’s fixed Gemini’s issues with generating humansSix months after the AI model produced historically inaccurate images. (The Verge)
+ It can’t be used to depict public figures in a photorealistic style. (NYT $)

8 Who owns the world’s genetic data?World leaders will hash out an answer at this fall’s Cop16 biodiversity summit. (The Guardian)
+ How environmental DNA is giving scientists a new way to understand our world. (MIT Technology Review)

9 Viral fame is a double-edged sword for TikTokers
It’s shockingly easy to squander the opportunities that come with it. (Fast Company $)

10 This AI model can simulate video game Doom in real time
It’s effectively acting as a limited game engine. (Ars Technica)
+ It’s the first engine of its kind powered entirely by a neural model. (404 Media)
+ How generative AI could reinvent what it means to play. (MIT Technology Review)

Quote of the day

“It seems really freakin’ dead.”

—Tom Smith, who sells NFTs of anthropomorphized cannabis plants, offers a frank assessment of this year’s ‘Super Bowl of NFT’ event to the Verge.

The big story

Responsible AI has a burnout problem

October 2022

Margaret Mitchell had been working at Google for two years before she realized she needed a break. Only after she spoke with a therapist did she understand the problem: she was burnt out.

Mitchell, who now works as chief ethics scientist at the AI startup Hugging Face, is far from alone in her experience. Burnout is becoming increasingly common in responsible AI teams.

All the practitioners MIT Technology Review interviewed spoke enthusiastically about their work: it is fueled by passion, a sense of urgency, and the satisfaction of building solutions for real problems. But that sense of mission can be overwhelming without the right support. Read the full story.

—Melissa Heikkilä

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• The next time you’re stuck for what to watch on Netflix, why not give its cheat codes a go?
• These photos of rural roadside American attractions are amazing.
• No one’s cooler than Jimi Hendrix.
• Here’s everything you need to know about the Paris Paralympics, which kicked off yesterday.

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Tangled up in the contest to be the next US president, there is another battle brewing: Silicon Valley vs. Silicon Valley. In Donald Trump’s corner are venture capitalists like Marc Andreessen and Peter Thiel, along with executives like Elon Musk. In the other are execs like LinkedIn founder Reid Hoffman and SV Angel investing mogul Ron Conway, who are backing Kamala Harris. Democracy appears to be at stake, and the weapon of choice is cold hard cash.

Yet as an elected board member of the Alphabet Workers Union, an affiliate of the Communications Workers of America, I urge Americans to take a step back and look critically at the picture in front of us. No matter who wins in November, Silicon Valley’s bosses are positioning themselves for victory. It’s a familiar hedge that goes back decades, but this time is different because over the past four years hundreds of thousands of tech workers have been clawing back power. Tech’s elite have long been the biggest winners in the US economy, and the movement to organize tech workers seeks to hold that elite accountable.

If the next president favors our bosses’ interests over our own, the consequences could be dire for all working people in this country and many others. We know how to fight back against a future Trump administration because we have been there before. What’s less clear is whether and to what extent we can count on a Harris administration to be our ally.

On stage at the Democratic National Convention, Vice President Harris vowed to center the concerns of working people over those of corporate America. If she stays committed to that path in the face of Silicon Valley’s well-funded opposition, she will find dedicated allies in tech workers.

Massive layoffs and brutal union-busting have become routine across the tech industry in recent years, enacted by executives with ties to both sides of the aisle. And many of the biggest innovations coming out of Silicon Valley over the past decade have been distinctly targeted at cutting labor costs and skirting labor laws. This has triggered a race to the bottom that starts with “gigified” outsourcing and—if the bosses have their way—ends in replacing as much human labor as possible with generative AI. These cost-cutting actions affect not only tech workers’ paychecks but the safety and quality of tech products with massive user bases.

Some execs are getting more comfortable publicly airing their anti-labor opinions. Recently, in an X Spaces conversation, Trump casually lauded Musk’s mass firing of workers as a way to deal with strikes. Earlier this year, Amazon CEO Andy Jassy violated federal labor law by arguing that workers would actually be “less empowered” if they unionized. On the automation front, executives of Nvidia, Duolingo, Klarna, Cisco, and IBM have recently made clear that they intend to use AI to replace human workers.

But in government and through grassroots campaigning, workers and labor advocates are fighting back. The Justice Department, the Federal Trade Commission, and the National Labor Relations Board under the Biden-Harris administration have been dogged in their pursuit of corporate overreach and labor violations by tech companies and the executives who run them. The DOJ has fought for fair hiring practices: the department fined Apple $25 million for hiring discrimination. Lina Khan’s FTC has attempted to ban noncompete agreements—a staple in tech companies’ at-will employment contracts, which have a chilling effect on workers’ ability to seek better pay and benefits.

Moreover, the agency has been consistently taking labor effects into account when evaluating mergers. This consideration moves beyond the tired consumer welfare standard and seeks to make sure that competition favors workers as well as consumers. And the NLRB has targeted outsourcing by more strictly enforcing a “joint employer” rule that makes it harder for companies to use subcontracting as a way to circumvent the minimum wage and other responsibilities.

On the ground, we workers have been simultaneously forming, joining, and strengthening unions to push conversation and action forward. The Campaign to Organize Digital Employees (CODE-CWA) has led the charge for the industry, organizing at companies ranging from Act Blue, the fundraising platform that supports many Democratic candidates, to blue-chip megacorp Microsoft. Our unions have filed petition after petition against employers, and the NLRB has tirelessly worked to enforce the laws our bosses violate, earning wins for labor across the board. In fact, the NLRB has been so successful that some tech companies—including Amazon and SpaceX—are attempting to cut the board off at the knees, claiming that its long-standing role in administering labor relations is unconstitutional.

For those of us accustomed to hard-fought progress and frequent setbacks for labor’s Davids under the thumb of corporate Goliaths, the last few years have been a true bright spot. And we are determined to keep fighting, and keep winning, with or without the support of the next president.

Will either candidate keep pushing forward for labor? The answer is not so clear. Monied tech interests are lining up on both sides to advocate for looser regulation. While pro-Trump venture capitalists Andreessen and Ben Horowitz cited euphemistic “bad government policies” as the number one threat to the tech industry, the Silicon Valley powers that be on Harris’s side haven’t exactly come out swinging for labor. In fact, Hoffman said that the FTC’s Khan is “waging war on American business” and urged Harris to fire her.

It’s not evident yet if Harris shares the views of her billionaire supporters, but she’s certainly chasing their money. A recent Harris campaign fundraiser in San Francisco bagged $13 million from a guest list replete with tech executives. And the vice president is reportedly courting tech bosses more directly, sending aides to meet with crypto leaders and venture capital firms. Her ties to the industry are long-standing and often personal; she’s known to be close with both former Facebook COO Sheryl Sandberg and Laurene Powell Jobs, and her brother-in-law is Uber’s chief legal officer.

While Harris’s team has been having conversations and exploring options, it has not yet announced any economic agenda or approach to regulation, innovation, or labor. It’s savvy to get the money first without making public promises. But Harris should be trying to court our votes, too—not just our bosses’ financial support. In recent memory, workers in the tech industry have demonstrated progressive energy. While campaigning in 2020, Bernie Sanders proudly voiced solidarity with workers against their billionaire bosses. And tech workers turned out for him, donating more to Bernie than to any other presidential candidate during the primaries—close to twice as much as to Elizabeth Warren, the second-favorite candidate for the group. Harris could leverage that kind of power in November if she truly commits to the cause.

Now is the moment for Harris to step up and make a statement in support of workers, promising to continue, if not expand upon, the Biden-Harris approach to Big Tech. Some may remember that when she ran for president in 2020, Senator Harris sided with Uber drivers and against her brother-in-law’s interests during a fight about gig workers’ rights in California. Unions like ours—as well as any American who believes that fair labor practices are essential to a functioning democracy—can continue to apply pressure on Harris and her team to take a strong stand for worker rights and protections. Indeed, the United Auto Workers (UAW) filed federal labor charges against Trump and Musk after those careless comments at the Spaces event, whereas President Biden walked a picket line with striking auto workers. Voices like theirs and ours—the voices of the hundreds of thousands of workers we represent—will continue to be raised. If we aren’t heard, we will get louder.

The stakes in November are high, and the only truly democratic future is one with fair wages, worker protections, and shared abundance. Tech elites stand in united opposition to such a future and are actively developing the AI tools to undermine it. Tech workers will continue to expand our collective power to fight those elites. The only open question is whether the next administration will be on our side or theirs.

Stephen McMurtry is a Google Software Engineer and Communications Chair of the Alphabet Workers Union-CWA

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This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Last year’s Canadian wildfires smashed records, burning about seven times more land in Canada’s forests than the annual average over the previous four decades. Eight firefighters were killed and 180,000 people displaced.

Now a new study reveals how these blazes can create a vicious cycle, contributing to climate change even as climate-fueled conditions make for worse wildfire seasons. Emissions from 2023’s Canadian wildfires reached 647 million metric tons of carbon, according to the study published today in Nature. If the fires were a country, they’d rank as the fourth-highest emitter, following only China, the US, and India. The sky-high emissions from the fires reveals how human activities are pushing natural ecosystems to a place that’s making things tougher for our climate efforts.

“The fact that this was happening over large parts of Canada and went on all summer was really a crazy thing to see,” says Brendan Byrne, a scientist at the NASA Jet Propulsion Laboratory and the lead author of the study.

Digging back into the climate record makes it clear how last year’s conditions contributed to an unusually brutal fire season, Byrne says; 2023 was especially warm and especially dry, both of which allow fires to spread more quickly and burn more intensely.

A few regions were especially notable in the blazes, like parts of Quebec, a typically wet area in the east of Canada that saw half the normal precipitation. These fires were the ones generating smoke that floated down the east coast of the US. But overall, what was so significant about the 2023 fire season was just how widespread the fire-promoting conditions were, Byrne says.

While climate change doesn’t directly spark any one fire, researchers have traced hot, dry conditions that worsen fires to the effects of human-caused climate change. The extreme fire conditions in eastern Canada were over twice as likely because of climate change, according to a 2023 analysis by World Weather Attribution.

And in turn, the fires are releasing massive amounts of greenhouse gases into the atmosphere. By combining satellite images of the burned areas with measurements of some of the gases emitted, Byrne and his team were able to tally up the total carbon released into the atmosphere with more accuracy than estimates that rely on the images alone, he says.

In total, the fires contributed at least four times more carbon to the atmosphere than all fossil-fuel emissions in Canada last year.

Fires are part of natural, healthy ecosystems, and burns on their own don’t necessarily represent a disaster for climate change. After a typical fire season, a forest begins to regrow, capturing carbon dioxide from the atmosphere as it does so. This continues a cycle in which carbon moves around the planet.

The problem comes if and when that cycle gets thrown off—for instance, if fires are too intense and too widespread for too many years. And there’s reason to be nervous about future fire seasons. While 2023’s conditions were unusual compared with the historical record, climate modeling reveals they could be normal by the 2050s.

“I think it’s very likely that we’re going to see more fires in Canada,” Byrne tells me. “But we don’t really understand how that’s going to impact carbon budgets.”

What Byrne means by a carbon budget is the quantity of greenhouse gases we can emit into the atmosphere before we shoot past our climate goals. We have something like seven years left of current emissions levels before we’re more likely than not to pass 1.5 °C of warming over preindustrial levels, according to the 2023 Global Carbon Budget Report.

It was already clear that we need to stop emissions from power plants, vehicles, and a huge range of other clearly human activities to address climate change. Last year’s wildfires should increase the urgency of that action, because pushing natural ecosystems beyond what they can handle will only add to the challenge going forward.

Now read the rest of The SparkRelated readingThis company wants to use balloons to better understand the conditions on the ground before wildfires start in Colorado, as Sarah Scoles covered in a story earlier this summer.

Canada isn’t the only country to see unusual fires in recent years. My colleague James Temple covered Australia’s intense 2019-2020 wildfire season.

Another thingWant to try out solar geoengineering? A new AI tool allows you to do just that—sort of.

Andrew Ng has released an online program that simulates what might happen under different emissions scenarios if technologies that can block out some sunlight are used in an effort to slow warming. Read the story here and give the simulator a try.

Keeping up with climate Scientists want to genetically engineer cows’ microbiomes to cut down on methane emissions. The animals’ digestive systems rely on archaea that emit the powerful greenhouse gas. Tweaking them could be a major help in cutting climate pollution from agriculture. (Washington Post)

Some big tech companies are using tricky math that can obscure the true emissions from rising electricity use, in part due to AI. Buying renewable energy credits can make a company’s energy use look better on paper, but the practice has some problems. (Bloomberg)

→ How companies reach their emissions goals can be more important than how quickly they do so. (MIT Technology Review)

The midwestern US is dealing with hot weather and high humidity, in part because of something called corn sweat. Crops naturally release water into the air when it’s warm, causing higher humidity. (Scientific American)

Hydrogen can provide an alternative to fossil fuels, but it likely won’t have universally positive effects in every industry. Hydrogen will be most useful in sectors like chemical production and least so in buildings and light-duty vehicles, according to a new report. (Latitude Media)

→ Here’s why hydrogen vehicles are losing the race to power cleaner cars. (MIT Technology Review)

Batteries are far outpacing natural gas in new additions to the US grid. In the first half of 2023, 96% of such additions were from renewable sources, batteries, or nuclear power. (Wired)

Tesla agreed to open its Supercharger network to vehicles from other automakers last year, but the plan has been plagued by delays. Drivers should be able to access the network next year, but so far only two companies have gotten past the first step of updating the software needed. (New York Times)

Sage Geosystems, a company using geothermal technology to generate and store energy, announced it has an agreement to supply 150 megawatts of power to Meta. (Canary Media)

Coal powers about 63% of China’s electric grid today, and the country is the world’s largest consumer of the fuel. But progress with technologies like hydropower and nuclear suggests the country could shift to lower-emissions energy sources. (Heatmap)

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Even the most capable robots aren’t great at sensing human touch; you typically need a computer science degree or at least a tablet to interact with them effectively. That may change, thanks to robots that can now sense and interpret touch without being covered in high-tech artificial skin.It’s a significant step toward robots that can interact more intuitively with humans.

To understand the new approach, led by the German Aerospace Center and published today in Science Robotics, consider the two distinct ways our own bodies sense touch. If you hold your left palm facing up and press lightly on your left pinky finger, you may first recognize that touch through the skin of your fingertip. That makes sense–you have thousands of receptors on your hands and fingers alone. Roboticists often try to replicate that blanket of sensors for robots through artificial skins, but these can be expensive and ineffective at withstanding impacts or harsh environments.

But if you press harder, you may notice a second way of sensing the touch: through your knuckles and other joints. That sensation–a feeling of torque, to use the robotics jargon–is exactly what the researchers have re-created in their new system.

Their robotic arm contains six sensors, each of which can register even incredibly small amounts of pressure against any section of the device. After precisely measuring the amount and angle of that force, a series of algorithms can then map where a person is touching the robot and analyze what exactly they’re trying to communicate. For example, a person could draw letters or numbers anywhere on the robotic arm’s surface with a finger, and the robot could interpret directions from those movements. Any part of the robot could also be used as a virtual button.

It means that every square inch of the robot essentially becomes a touch screen, except without the cost, fragility, and wiring of one, says Maged Iskandar, researcher at the German Aerospace Center and lead author of the study.

“Human-robot interaction, where a human can closely interact with and command a robot, is still not optimal, because the human needs an input device,” Iskandar says. “If you can use the robot itself as a device, the interactions will be more fluid.”

A system like this could provide a cheaper and simpler way of providing not only a sense of touch, but also a new way to communicate with robots. That could be particularly significant for larger robots, like humanoids, which continue to receive billions in venture capital investment.

Calogero Maria Oddo, a roboticist who leads the Neuro-Robotic Touch Laboratory at the BioRobotics Institute but was not involved in the work, says the development is significant, thanks to the way the research combines sensors, elegant use of mathematics to map out touch, and new AI methods to put it all together. Oddo says commercial adoption could be fairly quick, since the investment required is more in software than hardware, which is far more expensive.

There are caveats, though. For one, the new model cannot handle more than two points of contact at once. In a fairly controlled setting like a factory floor that might not be an issue, but in environments where human-robot interactions are less predictable, it could present limitations. And the sorts of sensors needed to communicate touch to a robot, though commercially available, can also cost tens of thousands of dollars.

Overall, though, Oddo envisions a future where skin-based sensors and joint-based ones are merged to give robots a more comprehensive sense of touch.

“We humans and other animals have integrated both solutions,” he says. “I expect robots working in the real world will use both, too, to interact safely and smoothly with the world and learn.”

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In 2019, an agency within the U.S. Department of Defense released a call for research projects to help the military deal with the copious amount of plastic waste generated when troops are sent to work in remote locations or disaster zones. The agency wanted a system that could convert food wrappers and water bottles, among other things, into usable products, such as fuel and rations. The system needed to be small enough to fit in a Humvee and capable of running on little energy. It also needed to harness the power of plastic-eating microbes.

“When we started this project four years ago, the ideas were there. And in theory, it made sense,” said Stephen Techtmann, a microbiologist at Michigan Technological University, who leads one of the three research groups receiving funding. Nevertheless, he said, in the beginning, the effort “felt a lot more science-fiction than really something that would work.”

In one reactor, shown here at a recent MTU demonstration, some deconstructed plastics are subject to high heat and the absence of oxygen — a process called pyrolysis.KADEN STALEY/MICHIGAN TECHNOLOGICAL UNIVERSITYThat uncertainty was key. The Defense Advanced Research Projects Agency, or DARPA, supports high-risk, high-reward projects. This means there’s a good chance that any individual effort will end in failure. But when a project does succeed, it has the potential to be a true scientific breakthrough. “Our goal is to go from disbelief, like, ‘You’re kidding me. You want to do what?’ to ‘You know, that might be actually feasible,’” said Leonard Tender, a program manager at DARPA who is overseeing the plastic waste projects.

The problems with plastic production and disposal are well known. According to the United Nations Environment Program, the world creates about 440 million tons of plastic waste per year. Much of it ends up in landfills or in the ocean, where microplastics, plastic pellets, and plastic bags pose a threat to wildlife. Many governments and experts agree that solving the problem will require reducing production, and some countries and U.S. states have additionally introduced policies to encourage recycling.

For years, scientists have also been experimenting with various species of plastic-eating bacteria. But DARPA is taking a slightly different approach in seeking a compact and mobile solution that uses plastic to create something else entirely: food for humans.

In the beginning, the effort “felt a lot more science-fiction than really something that would work.”

The goal, Techtmann hastens to add, is not to feed people plastic. Rather, the hope is that the plastic-devouring microbes in his system will themselves prove fit for human consumption. While Techtmann believes most of the project will be ready in a year or two, it’s this food step that could take longer. His team is currently doing toxicity testing, and then they will submit their results to the Food and Drug Administration for review. Even if all that goes smoothly, an additional challenge awaits. There’s an ick factor, said Techtmann, “that I think would have to be overcome.”

The military isn’t the only entity working to turn microbes into nutrition. From Korea to Finland, a small number of researchers, as well as some companies, are exploring whether microorganisms might one day help feed the world’s growing population.

According to Tender, DARPA’s call for proposals was aimed at solving two problems at once. First, the agency hoped to reduce what he called supply-chain vulnerability: During war, the military needs to transport supplies to troops in remote locations, which creates a safety risk for people in the vehicle. Additionally, the agency wanted to stop using hazardous burn pits as a means of dealing with plastic waste. “Getting those waste products off of those sites responsibly is a huge lift,” Tender said.

A research engineer working on the MTU project takes a raw sample from the pyrolysis reactor, which can be upcycled into fuels and lubricants.KADEN STALEY/MICHIGAN TECHNOLOGICAL UNIVERSITYThe Michigan Tech system begins with a mechanical shredder, which reduces the plastic to small shards that then move into a reactor, where they soak in ammonium hydroxide under high heat. Some plastics, such as PET, which is commonly used to make disposable water bottles, break down at this point. Other plastics used in military food packaging — namely polyethylene and polypropylene — are passed along to another reactor, where they are subject to much higher heat and an absence of oxygen.

Under these conditions, the polyethylene and polypropylene are converted into compounds that can be upcycled into fuels and lubricants. David Shonnard, a chemical engineer at Michigan Tech who oversaw this component of the project, has developed a startup company called Resurgent Innovation to commercialize some of the technology. (Other members of the research team, said Shonnard, are pursuing additional patents related to other parts of the system.)

After the PET has broken down in the ammonium hydroxide, the liquid is moved to another reactor, where it is consumed by a colony of microbes. Techtmann initially thought he would need to go to a highly contaminated environment to find bacteria capable of breaking down the deconstructed plastic. But as it turned out, bacteria from compost piles worked really well. This may be because the deconstructed plastic that enters the reactor has a similar molecular structure to some plant material compounds, he said. So the bacteria that would otherwise eat plants can perhaps instead draw their energy from the plastic.

Materials for the MTU project are shown at a recent demonstration. Before being placed in a reactor, plastic feedstocks (bottom row) are mechanically shredded into small pieces.KADEN STALEY/MICHIGAN TECHNOLOGICAL UNIVERSITYAfter the bacteria consume the plastic, the microbes are then dried into a powder that smells a bit like nutritional yeast and has a balance of fats, carbohydrates, and proteins, said Techtmann.

Research into edible microorganisms dates back at least 60 years, but the body of evidence is decidedly small. (One review estimated that since 1961, an average of seven papers have been published per year.) Still, researchers in the field say there are good reasons for countries to consider microbes as a food source. Among other things, they are rich in protein, wrote Sang Yup Lee, a bioengineer and senior vice president for research at Korea Advanced Institute of Science and Technology, in an email to Undark. Lee and others have noted that growing microbes requires less land and water than conventional agriculture. Therefore, they might prove to be a more sustainable source of nutrition, particularly as the human population grows.

The product from the microbe reactor is collected in a glass jar. The microbes can be dried into a powder for human consumption — once they are deemed safe by regulators. After PET is broken down in the ammonium hydroxide, the liquid is moved to a reactor where it is consumed by a colony of microbes.Lee reviewed a paper describing the microbial portion of the Michigan Tech project, and said that the group’s plans are feasible. But he pointed out a significant challenge: At the moment, only certain microorganisms are considered safe to eat, namely “those we have been eating thorough fermented food and beverages, such as lactic acid bacteria, bacillus, some yeasts.” But these don’t degrade plastics.

Before using the plastic-eating microbes as food for humans, the research team will submit evidence to regulators indicating that the substance is safe. Joshua Pearce, an electrical engineer at Western University in Ontario, Canada, performed the initial toxicology screening, breaking the microbes down into smaller pieces, which they compared against known toxins.

“We’re pretty sure there’s nothing bad in there,” said Pearce. He added that the microbes have also been fed to C. elegans roundworms without apparent ill-effects, and the team is currently looking at how rats do when they consume the microbes over the longer term. If the rats do well, then the next step would be to submit data to the Food and Drug Administration for review.

Before using the plastic-eating microbes as food for humans, the research team will submit evidence to regulators indicating that the substance is safe.

At least a handful of companies are in various stages of commercializing new varieties of edible microbes. A Finnish startup, Solar Foods, for example, has taken a bacterium found in nature and created a powdery product with a mustard brown hue that has been approved for use in Singapore. In an email to Undark, chief experience officer Laura Sinisalo said that the company has applied for approval in the E.U. and the U.K., as well as in the U.S., where it hopes to enter the market by the end of this year.

Even if the plastic-eating microbes turn out to be safe for human consumption, Techtmann said, the public might still balk at the prospect of eating something nourished on plastic waste. For this reason, he said, this particular group of microbes might prove most useful on remote military bases or during disaster relief, where it could be consumed short-term, to help people survive.

“I think there’s a bit less of a concern about the ick factor,” said Techtmann, “if it’s really just, ‘This is going to keep me alive for another day or two.’”

This article was originally published on Undark. Read the original article.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

This rare earth metal shows us the future of our planet’s resources

For nearly as long as we’ve extracted materials from our planet, we’ve been trying to predict how long they will be able to meet our demand. How much can we pump from a well, or wrest from a mine, before we need to reconsider what we’re building and how?

We’re in the middle of a potentially transformative moment. Metals discovered barely a century ago now underpin the technologies we’re relying on for cleaner energy, and not having enough of them could slow progress.

Take neodymium, one of the rare earth metals. It’s used in cryogenic coolers to reach ultra-low temperatures needed for devices like superconductors and in high-powered magnets that power everything from smartphones to wind turbines. And very soon, demand for it could outstrip supply. What happens then? And what does it reveal about issues across wider supply chains? Read our story to find out.

—Casey Crownhart

This piece is from the forthcoming print issue of MIT Technology Review, which is celebrating 125 years of the magazine! It’s set to go live on Wednesday August 28, so if you don’t already, subscribe now to get a copy when it lands.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Google will face a trial claiming that it misled Chrome users
The lawsuit alleges that the browser collected user data without their permission. (WP $)
+ The case was originally dismissed in 2022, but was reversed on appeal. (The Verge)

2 OpenAI will let companies customize its most powerful model
Businesses can fine-tune GPT-4o to include their own data for the first time. (Bloomberg $)
+ OpenAI has also hashed out a deal with media giant Condé Nast. (Wired $)
+ How to fine-tune AI for prosperity. (MIT Technology Review)

3 CrowdStrike has had a rough month
The cyber security firm has accused its rivals of making ‘misguided’ attacks in the wake of its colossal global IT outage. (FT $)
+ The outage was caused by a botched update. (MIT Technology Review)

4 Waymo is making 100,000 robotaxi trips a week
That’s double the amount of journeys it was making in May. (NBC News)
+ What’s next for robotaxis. (MIT Technology Review)

5 A law that protects tech giants is being used against them
Section 230 shields tech firms from legal liability. A Massachusetts professor is testing its limits. (NYT $)

6 The hype around hydrogen continues to buildParticularly regarding ‘gold’ hydrogen, which doesn’t require energy to produce. (Wired $)
+ But producing green hydrogen is easier said than done. (FT $)
+ Hydrogen could be used for nearly everything. It probably shouldn’t be. (MIT Technology Review)

7 Taiwan is putting its fish farms to workThey’re doubling up as solar plants, creating a new aquavoltaics facility. (IEEE Spectrum)

8 China’s fast fashion giants are feuding againShein has accused Temu of ripping off its designs, an accusation that major brands have long leveled against Shein itself. (404 Media)
+ Shein’s lawsuit comes as Temu is attempting to infiltrate the US. (The Register)
+ The pair have a long, litigious history of suing each other. (MIT Technology Review)

9 How to make food from plastic
No, really. (Undark Magazine)
+ Think that your plastic is being recycled? Think again. (MIT Technology Review)

10 China is going wild for a Ming dynasty epic video game
Players need a good knowledge of Journey to the West to progress. (Reuters)
+ Its creators are hoping it’ll prove a hit with Western audiences too. (NYT $)

Quote of the day

“If we take our clothes off when in our communities, then it has to be shown in the same way on the internet.”

—Chirley Pankara, a doctor in anthropology at the University of São Paulo and an Indigenous activist, says social media’s anti-nudity policies censor Indigenous practices, Rest of World reports.

The big story

The messy quest to replace drugs with electricity

May 2024

In the early 2010s, electricity seemed poised for a hostile takeover of your doctor’s office. Research into how the nervous system—the highway that carries electrical messages between the brain and the body— controls the immune response was gaining traction.

And that had opened the door to the possibility of hacking into the body’s circuitry and thereby controlling a host of chronic diseases, as if the immune system were as reprogrammable as a computer.

To do that you’d need a new class of implant: an “electroceutical.” These devices would replace drugs. No more messy side effects. And no more guessing whether a drug would work differently for you and someone else. In the 10 years or so since, around a billion dollars has accreted around the effort. Despite that, electroceuticals have still not taken off as hoped. But could that be about to change? Read our story.

—Sally Adee

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Ditch the calorie counting and apply sun cream around your eyes: here’s how to live well these days.
• Emily in Paris is back, and its shooting locations are as dreamy as ever.
• You can’t tickle yourself, no matter how hard you try.
• This tasty-looking food has been upcycled.

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Leaving aside meteorites that strike Earth’s surface and spacecraft that get flung out of its orbit, the quantity of materials available on this planet isn’t really changing all that much.

That simple fact of our finite resources becomes clearer and more daunting as the pace of technological change advances and our society requires an ever wider array of material inputs to sustain it. So for nearly as long as we’ve systematically extracted these substances, we’ve been trying to predict how long they will be able to meet our demand. How much can we pump from a well, or wrest from a mine, before we need to reconsider what we’re building and how?

Those predictions have grown increasingly complicated. And now it’s also a matter of how much we can pull from manufactured and discarded objects. Can we recycle parts of that iPhone, or the guts of that massive wind turbine? How much of any given object can we recirculate into our churning technological economy?

Estimates of how much material we’ll have access to in the future tend to have a tricky, often implicit assumption at their center: that we’ll be making roughly the same products with the same materials as today. But technology moves quickly, and by the time we understand what we might need next, or develop a specialized system to mine or recycle it, the next generation of tech might render all our assumptions obsolete.

We’re in the middle of a potentially transformative moment. The materials we need to power our world are beginning to shift from fossil fuels to energy sources that don’t produce the greenhouse-gas emissions changing our climate. Metals discovered barely more than a century ago now underpin the technologies we’re relying on for cleaner energy, and not having enough of them could slow progress.

Take neodymium, one of the rare earth metals. While far from a household name, it’s a metal that humans have relied on for generations. Since the early 20th century, neodymium has been used to give decorative glass a purplish hue. Today, it’s used in cryogenic coolers to reach ultra-low temperatures needed for devices like superconductors and in high-powered magnets that power everything from smartphones to wind turbines.

Demand for neodymium-based magnets could outstrip supply in the coming decade. The longer-term prospects for the metal’s supply aren’t as dire, but a careful look at neodymium’s potential future reveals many of the challenges we’ll likely face across the supply chain for materials in the coming century and beyond.

Peak panicBefore we get into our material future, it’s important to point out just how hard it’s always been to make accurate predictions of this kind. Just look at our continuous theorizing about the supply of fossil fuels.

One version of the story, told frequently in economics classes, goes something like this: Given that there’s a limited supply of oil, at some point the world will run out of it. Before then, we should reach some maximum amount of oil extraction, and then production will start an irreversible decline. That high point is known as “peak oil.”

This idea has been traced back as far as the early 1900s, but one of the most famous analyses came from M. King Hubbert, who was a geologist at Shell. In a 1956 paper, Hubbert considered the total amount of oil (and other fossil fuels, like coal and natural gas) that geologists had identified on the planet. From the estimated supply and the amount the world had burned through, he predicted that oil production in the US would peak and begin declining between 1965 and 1970. The peak of world oil production, he predicted, would come a bit later, in 2000.

For a while, it looked as if Hubbert was right. US oil production increased until 1970, when it reached a dramatic peak. It then declined for decades afterward, until about 2010. But then advances in drilling and fracking techniques unlocked hard-to-reach reserves. Oil production skyrocketed in the US through the 2010s, and as of 2023, the country was producing more oil than ever before.

Peak-oil panic has long outlived Hubbert, but every time economists and geologists have predicted that we’ve reached, or are about to reach, the peak of oil production, they’ve missed the mark (so far).

Now there’s a new reason we might see fossil-fuel production actually peak and eventually fall off: the energy transition. That’s shorthand for the grand effort to shift away from energy sources that produce greenhouse gases and toward renewables and other low-carbon options.

Hubbert’s theory suggested that a fixed supply would force production to decline from a peak. But as the world wakes up to the dangers of climate change, and as low-carbon energy sources like wind, solar, and nuclear take off, we may wind up leaving some coal, oil, and natural gas in the ground. Simply put, production might head back down because of a lack of demand, not a lack of supply.

Those newly ascendant energy sources, though, are ironically a new source of “peak” panic. Solar panels, wind turbines, and batteries may not require fuel, but they do require a host of metals, including lithium, copper, steel, and rare earths like neodymium.

Neodymium is crucial for powering many of our devices. And we could be facing a supply crunch.GETTY IMAGESIf we extract, process, use, and discard these metals, conceptually there must be some point in the future when we run out of them. And as the energy transition has gotten underway, plenty of forecasts have attempted to understand which metals we should worry about and when they might start to be depleted. But experts say that understanding the availability of resources in this sector is much more complicated than picking out a single future peak.

“The peak modeling thing is something that doesn’t really apply to metals,” says Simon Jowitt, director of the Center for Research in Economic Geology at the University of Nevada, Reno. It’s nearly impossible to understand whether we’ve reached a peak in production for any given material, or even whether those peaks can be predicted, as Jowitt said in a 2020 paper.

Let’s take a closer look at neodymium. Reserves of the metal—the amount we know about that’s economically feasible to extract—have been estimated at 12.8 million tons. To keep the world from warming more than 1.5 °C over preindustrial levels, we might need as much as 121,000 tons every year just for wind turbines, according to a 2023 study on the material demands of the energy transition. Depending on how much material we assume makes it from the mine into final products, we could burn through those reserves in roughly a century.

If we extract, process, use, and discard these metals, conceptually there must be some point in the future when we run out of them.

The problem with this thinking, though, is that reserves and resources are far from fixed. Geologists discover new deposits all the time, for one thing. And what was considered too expensive and difficult to mine a few decades ago might be possible to extract with today’s technology. So instead of being slowly depleted, those material supplies have roughly kept up with production.

“We are currently producing more metals than ever before and have more metal resources and reserves than ever before,” as Jowitt put it in his paper.

And the question, he says, isn’t whether we’ll blow through what’s theoretically available on the planet, or even whether we’ll soon run out of material we can access and mine. It’s whether we’re willing to accept the social, ecological, and geopolitical consequences of how we mine today, and whether we might be able to change those for the better. Because we may be mining a lot more of some materials in the near future.

Big digsDemand for rare earths is expected to explode in the coming decades, driven largely by the increased need for neodymium-based magnets. These magnets, commonly made from a mixture of neodymium, iron, and boron with other elements sprinkled in, produce a stronger magnetic field with less material than other magnets available today.

While demand for neo magnets will likely triple in the coming decade, global production of neodymium will only double, according to Adamas Intelligence, a consulting firm specializing in strategic metals and minerals. It can take close to a decade to build new mines, and those long lead times could contribute to a supply crunch, says Seaver Wang, climate co-­director at the Breakthrough Institute, an environmental think tank.

Short periods when demand outstrips supply can lead to volatility, high prices, and slower deployment of new technologies. In a time as fast-moving as our current energy transition, those challenging economic conditions could have far-reaching effects, potentially entrenching old technologies and stalling progress.

But despite these expected challenges and the resulting potential for volatility, there is, in theory, plenty of neodymium to go around. Despite their name, most rare earth metals aren’t terribly rare. Many are about as abundant in Earth’s crust as copper, and neodymium is roughly 1,000 times more common in the crust than platinum or gold.

However, unlike those metals, rare earths aren’t often found in concentrated deposits. Getting one ton of metal concentrate can require moving a thousand tons of rocks.

This mining and refining process can be technically complicated and environmentally damaging, in part because rare earth metals are chemically similar to each other and difficult to separate without using harsh chemicals, says Julie Klinger, an associate professor at the University of Delaware who studies the global market for these materials.

Extraction often relies on dissolving crushed-up ore in strong acid. Mines that don’t carefully contain the waste material and the used chemicals risk polluting local waterways. Rare earth mines also often need to handle radioactive waste, since elements like thorium and uranium are common in and around the minerals that are mined to extract rare earths.

There are efforts underway to mine without producing dangerous waste, and new sites are attempting to squeeze as much finished product out of their initial mined material as possible, reintroducing scraps back into the refining process so less ends up in the waste. Others are taking another look at waste from previous mining efforts.

But some experts hope to entirely rethink material supply. Instead of extracting new materials, what if we look to what’s already been dug out of the ground?

Around and aroundFollow the path of many commonly used metals, and you’ll likely trace a straight line that leads from the mine to a product and, eventually, to some version of a trash can. In an effort to ease supply concerns and environmental damage, some experts are calling for a new way of using materials, one that focuses on reducing waste or eliminating it altogether.

Such a system would bend the line that goes from mine to trash into a new shape, so extracted materials are in use for as long as possible—maybe even forever. A whole host of strategies can extend the lifetime of materials, from repairing and refurbishing products to disassembling them and recycling the metals in them once the products are beyond repair.

This can start well before products even get to consumers, by making the most of materials as they’re taken out of the ground. Where recycling really gets difficult is the point at which the materials have left a company and gone into devices, says Ikenna Nlebedim, a research scientist at Ames National Laboratory.

Follow the path of many commonly used metals, and you’ll likely trace a straight line that leads from the mine to a product and, eventually, to some version of a trash can.

Today, a small but difficult-to-quantify fraction of rare earth elements are recycled from products that have reached the end of their useful life. (Many in the industry put the figure at roughly 1%, though there’s little data available on rare earth collection, Nlebedim says.) With the looming increase in expected demand, several companies, including Noveon, REEcycle, and Cyclic Materials, are working to increase that amount, setting up the beginning of a recycling industry.

A major challenge for rising magnet recyclers is that magnets tend to make up a tiny fraction of a product’s total weight. Picking through heaps of products to recover them is an imperfect system, and magnet recyclers are left with other valuable materials that they have no interest in—and no effective process for isolating.

Neodymium nitrate photographed under polarized light.GETTY IMAGESIn the future, economical recycling of rare earths might require a broader infrastructure for recycling the rest of a device, Nlebedim says. A centralized dismantling system would allow the recovery of materials like copper, gold, and platinum group metals that are often found in the same products as rare earths. This setup would allow more of the material in waste products to be reused than is possible now, when a company will go after the highest-­value, easiest-to-extract materials and toss the rest into a shredder.

Casting a wider net to recover more materials could help create a more stable supply for metals. That could be a major help if the materials considered valuable in the future are different from the ones with the most value today.

Quick shiftsTechnology moves quickly, and many of the materials that are critical to us today weren’t even in use a century ago.

Just look at the history of Mountain Pass Mine, a rare earth mine in California. The mine’s critical product has changed every 20 years or so since production started in 1952, says Michael Rosenthal, cofounder and chief operating officer of MP Materials, the site’s owner.

In the 1960s, Mountain Pass produced the europium used in color television screens of the time. In the following decades the target was cerium, which was useful for the glass used in televisions with cathode ray tubes. Since CRTs have been replaced with new technology like LED screens, demand for cerium has decreased. Now the mine focuses on neodymium and praseodymium, another ingredient sometimes used in magnets.

Yet even as geologists are scouting new mines and companies are springing up to start building recycling systems, researchers are working to make rare earth magnets less central to our technological future, or maybe even obsolete.

Today, neodymium is necessary in these powerful magnets to wrangle the electrons in iron so that they spin consistently in the same direction, producing a strong magnetic field. There aren’t any alternatives that can match their performance.

However, there could be options on the way. Niron Magnetics is working to build iron nitride magnets, which produce a powerful magnetic field without the need for any rare earth metals. The company opened its first manufacturing facility in early 2024, and while its products can’t sub in for high-quality neo magnets just yet, there’s no fundamental reason they won’t be able to in the future. If Niron or other companies are able to develop new magnets, it could mean a shift in the rare earth market that quickly makes the current magnet recycling systems irrelevant.

In a perfectly sustainable world, we would use and reuse materials dug out of the ground indefinitely. But as our technology shifts and our lives change, it can be difficult to end the loop where it began. Instead, our material economy may morph into the shape of a spiral. Resources may not end up quite where they started— rather, the system we’ve set up to extract and use them will continue to chase technological progress, maybe endlessly.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How to fine-tune AI for prosperity

Predictions abound on how the growing list of generative AI models will transform the way we work and organize our lives, providing instant advice on everything from financial investments to where to spend your next vacation.

But for economists, the most critical question around our obsession with AI is how the fledgling technology will (or won’t) boost overall productivity, and if it does, how long it will take. Can the technology lead to renewed prosperity after years of stagnant economic growth? Read the full story.

—David Rotman

Fighting for a future beyond the climate crisis

When it comes to climate breakdown and the extinction crisis, the question often asked is: How can we have hope?

But maybe hope is the wrong emotion to focus on. Instead, we need shock and awe in the face of the majesty and fragility of nature, humility in the face of the vastness of the transformations our kind has set in motion—a bristling realization of imminent peril. Read the full story.

—Lydia Millet

This piece is from the forthcoming print issue of MIT Technology Review, which is celebrating 125 years of the magazine! It’s set to go live on Wednesday August 28, so if you don’t already, subscribe now to get a copy when it lands.

Why you’re about to see a lot more drones in the sky

For decades, the Federal Aviation Administration (FAA) has restricted people’s ability to fly drones in shared airspaces or dense neighborhoods. That’s made it hard to deliver futuristic ideas like drones delivering our packages.

But that’s changing. The agency recently granted Amazon’s Prime Air program approval to fly drones beyond the visual line of sight in parts of Texas, and also granted similar waivers to hundreds of police departments around the country.

However, there’s an even bigger change coming in less than a month. It promises to be the most significant drone decision in decades, and one that will decide just how many drones we all can expect to see and hear buzzing above the US on a daily basis. Read the full story.

—James O’Donnell

This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 US officials confirmed Iran hacked Donald Trump’s campaign
The hackers also attempted to infiltrate the Democrat campaign. (The Guardian)
+ They tricked victims into sharing sensitive information via ‘social engineering.’ (FT $)
+ Officials believe Iran is trying to sow discord ahead of the presidential election. (AP)

2 AI is helping to personalize treatment for Parkinson’s
Individualized algorithms tailor the amount of electrical stimulation patients receive. (NYT $)
+ The first clinical trial of the technology appears to be promising. (FT $)
+ Here’s how personalized brain stimulation could treat depression. (MIT Technology Review)

3 Power generation in the US is at its highest point in 21 years
Surprise surprise, it’s because of AI. (Bloomberg $)
+ Locals in India claim Microsoft’s new data center is dumping waste nearby. (Rest of World)
+ AI is an energy hog. This is what it means for climate change. (MIT Technology Review)

4 The EU is probing Chinese subsidies and importsIt covers everything from EVs to solar panels. (Reuters)

5 A rocket exploded during a test launch in the UK
And it’s not immediately clear why. (BBC)

6 Deadly lightning strikes are on the riseRising global temperatures are fuelling more frequent dangerous storms. (Wired $)

7 Even the most resilient coral reefs are struggling with climate changeA tough Caribbean reef is reaching its limits. (Vox)
+ The race is on to save coral reefs—by freezing them. (MIT Technology Review)

8 AI-enabled cheating is getting worseUniversities need a robust plan to fight it—and fast. (The Atlantic $)
+ ChatGPT is going to change education, not destroy it. (MIT Technology Review)

9 This startup uses AI to create new episodes of South Park
It’s becoming the latest way to keep fandoms paying for their favorite media. (The Information $)

10 Would you meet up with a stranger for breakfast?
An app is matching diners seeking deep conversations over eggs and bacon. (WP $)

Quote of the day

“It’s very hard to have a democratic society if people can’t believe the things that they see and hear with their own eyes.”

—Robert Weissman, co-president of non-profit Public Citizen, tells the Verge about the dangers of Donald Trump sharing fake AI-generated images, including one of Taylor Swift endorsing him.

The big story

How climate vulnerability and the digital divide are linked

June 2023

Walking around low-income neighborhoods throughout the US, Monica Sanders has noticed a pattern. The adjunct professor of law at Georgetown University measures Wi-Fi speeds as part of a project drawing connections between a host of indicators at the intersection of internet availability, environmental risk, and historical racial inequity.

Sanders has found that a lack of internet access mirrors other inequities. In neighborhoods shaped by racism and insufficient infrastructure investment, residents can face disproportionate risk from climate change, affecting everything from flood vulnerability to the ability to get disaster warnings. And she wants to empower them to tackle whatever next comes their way. Read the full story.

—Colleen Hagerty

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• It’s never too late—you can become an athlete at any age.
• Glassblowing will never not be fascinating.
• It’s not just you, toilet roll really is getting smaller.
• Legendary producer Aphex Twin has a surprising side hustle as a wedding DJ.

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When it comes to climate breakdown and the extinction crisis, the question I get most often is: How can we have hope?

People ask me this in a range of contexts—in Q&A sessions, in emails, and on podcasts and radio shows, whether I’m doing outreach for my novels, like A Children’s Bible or Dinosaurs, or for nonfiction like We Loved It All, my new memoir. I see numerous iterations of it in the media and my social feeds and hear accounts of its ubiquity from writer friends, scientist and lawyer colleagues, activists and community organizers.

I’ve thought about the impulse behind the asking and am left with the lingering sense that many of us tend, in this cultural moment, to privilege our feelings on these existential threats over reason, say, or moral virtue, or apparently antiquated notions of civic and collective duty. Feelings are the beacon we entrust with shining a path through the fog to guide us home—anger and aggrievement, maybe, on the right of the political spectrum, and on the left something akin to defensive self-righteousness.

It’s almost as though we lay our fate at the feet of feelings and wait for deliverance.

In the realm of emotion, hope guards against despair, whose rationalized intellectual output is cynicism—a free pass out of the tension of grappling with our responsibility to the future, with the difficulty and possible unpleasantness of engagement and resistance. But like cynicism, hope is its own free pass, filling the space of subjectivity with a passive expectation of relief. For the most part “hope” functions as a unit of rhetoric, as amorphous as “happiness” or “freedom”: a shredded flag in the discourse around climate doomsaying and denial that can only droop over a citadel under relentless siege. If we rely on hope, we give up agency. And that may be seductive, but it’s also surrender.

It’s possible that feelings aren’t our most useful gift. Other animals have feelings too, yet they haven’t radically modified the planet toward unlivability; we’ve done so by pairing our feelings with the unique combination of capabilities that were our species’ answers to the pressures of evolution. These include communication and collaboration, the sophisticated languages we share, our ability to conceptualize a distant past and future and make tools with our opposable thumbs—capacities that, together, have allowed us to construct empires and complex machines and cast our intelligence into the deep sea and the far-off thermosphere. Even beyond the sun.

Yet the mission we chose to undertake has been one guided by desire and by a framework of ideas we’ve built to justify projecting that desire into the appropriation and liquidation of our resource base. The result has been voracious production and reproduction. Over the course of just a handful of fast-moving centuries, that hysterical vector of taking and making has landed us in a state of emergency that suddenly appears, with a high degree of credibility, poised to bury us under the sea or burn us off the land: in effect to steam open our small envelope of life and peel our paper-thin atmosphere, forests and rivers, grasslands and tundra and reefs and polar icescapes and the creatures they sustain, right off the surface of the world.

To fathom the danger of our situation, to let its immediacy dawn on us and drive us to act, it’s true that emotion is required. But in the stable of emotions to which we have ready access, hope is a pale horse. To spark an understanding of our history of error and push us to reconceive and heal as passionately as we now lay waste, we need to embrace a more extraordinary recognition.

We need shock and awe in the face of the majesty and fragility of nature, humility in the face of the vastness of the transformations our kind has set in motion—a bristling realization of imminent peril, a visceral apprehension of the nonfungibility of our zone of life. Of this marvelous place, infinitesimal in the solar system if not the galaxy, that has given us, on the thin skin of a solitary planet, the combination of flowing water and breathable air that are the preconditions for life.

Only awe can drive us to work as frenziedly from fear as, it might be argued, we’ve worked from greed until now.

More than ordinary emotions, we need an encounter with the shock of our finitude, a sensation of awe, reverence, and astonishment before the richness and precariousness of being.

Ordinary emotions let us blunder through the onslaught of information in the slow befuddlement of a stubborn belief that the familiar is bound to persist. But without a swift, far-reaching, and cooperative global effort, the familiar will not persist. Social and political stability will vanish along with biological and geophysical vanishments—the disappearance of coral reefs, for instance, whose absence will denude the oceans of diversity, or the collapse of the AMOC, the Atlantic meridional overturning circulation, under the influx of fresh water from melting ice, which could render Northern Europe inhospitably cold, raise sea levels along the US Eastern Seaboard, and overheat the tropics.

In the realm of emotion, awe is the prerequisite to action. Not hope. Only awe can drive us to work as frenziedly from fear as, it might be argued, we’ve worked from greed until now. And whether it’s music or nature or art or religion that leaves us awestruck or just a simple decision to suddenly, deeply notice the world beyond ourselves, each of these requires the suspension of chatter—a willingness to halt and stand still within the rushing momentum of daily life.

If we wish to thrive beyond it, the next century will have to be a time of unmaking and remaking: unmaking the technologies and culture of fossil fuels and their massive, entrenched infrastructure and remaking our template for prosperity from one based on limitless growth into one aimed at accommodation to a delicate biosphere. This means, among other key policy steps, defending and funding reproductive rights, equity, and education both at home and abroad—chiefly for women, since women’s access to education is a central driver of the lower birth rates that will be crucial to living within our means.

To champion makerdom alone as the answer is to add willful ignorance to hubris. It’s a fact that we need to manufacture and rapidly propagate better tools—energy and food delivery systems that don’t disintegrate our life support to fuel our daily activities—and, equally, it’s a lie that better making by itself can save us or the other life forms we depend on.

Less making and unmaking are also the solution—less making of what we do not need and more unmaking of harmful machines and ideas. The sprawling patrimony of bad ideas—that Homo sapiens reigns supreme over nature and so is miraculously independent of it, in defiance of ecology and physics; that market capitalism is the unassailable apogee of civilization and ongoing expansion the correct communal goal, including endless human procreation cheered on by neoliberal economists who whinge over declining birth rates in industrialized nations—should be dismantled as steadily as the destructive machines.

Neither the United States nor the world community has mechanisms in place to adequately curb potentially catastrophic enterprise, either when that enterprise is demonstrably causing climate chaos or when it purports to meet the demand for fixes. Treaties made under international law have been famously toothless to date, while the US legal system, which does possess sharp teeth, defers to the legislative bounds established by a Congress deeply beholden to fossil fuels and related industries bent on maintaining the status quo. And that legal system, far from being disposed to address the exceptionally high public health and security risks posed by climate change and extinction, is clearly, through the recent stacking of courts with antigovernment and antiscience jurists, in the business of radically increasing its deference to private actors as it erodes the rights of the dispossessed and the power of federal oversight.

If we in this country can’t rely on the legislative or judicial branches of our central government to tackle the crises of their own volition, while the executive branch directs, at best, movement toward renewables without movement away from fossils; if we can’t rely on the myopic and nihilistic companies dominating the energy sector to pivot anytime soon; then who remains to help us? To whom can we turn, we who exist, always and only, here and nowhere else, in this walled city of the Earth under such terrible siege?

The answer may be, for now, only ourselves. Those of us who have language and believe in the wisdom science can offer. Who know the surpassing vulnerability of the rivers and prairies, the jungles and wetlands, the cypress swamps of South Florida, the Cape Floristic Region of South Africa, the Siberian taiga, the Tropical Andes, Madagascar, the island Caribbean. Who can gaze into the future and, beholding the prospect of a frightening and emptier world for our descendants, feel compelled to fight on behalf of the one we have.

Lydia Millet is the author of more than a dozen novels, including A Children’s Bible; her most recent book, We Loved It All: A Memory of Life, is her first work of nonfiction.

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If you follow drone news closely—and you’re forgiven if you don’t—you may have noticed over the last few months that the Federal Aviation Administration (FAA) has been quite busy. For decades, the agency had been a thorn in the side of drone evangelists, who wanted more freedom to fly drones in shared airspaces or dense neighborhoods. The FAA’s rules have made it cumbersome for futuristic ideas like drones delivering packages to work at scale.

Lately, that’s been changing. The agency recently granted Amazon’s Prime Air program approval to fly drones beyond the visual line of sight from its pilots in parts of Texas. The FAA has also granted similar waivers to hundreds of police departments around the country, which are now able to fly drones miles away, much to the ire of privacy advocates.

However, while the FAA doling out more waivers is notable, there’s a much bigger change coming in less than a month.It promises to be the most significant drone decision in decades, and one that will decide just how many drones we all can expect to see and hear buzzing above us in the US on a daily basis.

By September 16—if the FAA adheres to its deadline—the agency must issue a Notice of Proposed Rulemaking about whether drones can be flown beyond a visual line of sight. In other words, rather than issuing one-off waivers to police departments and delivery companies, it will propose a rule that applies to everyone using the airspace and aims to minimize the safety risk of drones flying into one another or falling and injuring people or property below.

The FAA was first directed to come up with a rule back in 2018, but it hasn’t delivered. The September 16 deadline was put in place by the most recent FAA Reauthorization Act, signed into law in May. The agency will have 16 months after releasing the proposed rule to issue a final one.

Who will craft such an important rule, you ask? There are 87 organizations on the committee. Half are either commercial operators like Amazon and FedEx, drone manufacturers like Skydio, or other tech interests like Airbus or T-Mobile. There are also a handful of privacy groups like the American Civil Liberties Union, as well as academic researchers.

It’s unclear where exactly the agency’s proposed rule will fall, but experts in the drone space told me that the FAA has grown much more accommodating of drones, and they expect this ruling to be reflective of that shift.

If the rule makes it easier for pilots to fly beyond their line of sight, nearly every type of drone pilot will benefit from fewer restrictions. Groups like search and rescue pilots could more easily use drones to find missing persons in the wilderness without an FAA waiver, which is hard to obtain quickly in an emergency situation.

But if more drones take to the skies with their pilots nowhere in sight, it will have massive implications. “The [proposed rule] will likely allow a broad swatch of operators to conduct wide-ranging drone flights beyond their visual line of sight,” says Jay Stanley, a senior policy analyst at the American Civil Liberties Union’s Speech, Privacy, and Technology Project. “That could open up the skies to a mass of delivery drones (from Amazon and UPS to local ‘burrito-copters’ and other deliveries), local government survey or code-enforcement flights, and a whole new swath of police surveillance operations.”

Read more about what’s coming next for drones from me here.

Now read the rest of The AlgorithmDeeper LearningThe US wants to use facial recognition to identify migrant children as they ageThe US Department of Homeland Security (DHS) is looking into ways it might use facial recognition technology to track the identities of migrant children, “down to the infant,” as they age, according to John Boyd, assistant director of the department’s Office of Biometric Identity Management (OBIM), where a key part of his role is to research and develop future biometric identity services for the government. The previously unreported project is intended to improve how facial recognition algorithms track children over time.

Why this matters: Facial recognition technology (FRT) has traditionally not been applied to children, largely because training data sets of real children’s faces are few and far between, and consist of either low-quality images drawn from the internet or small sample sizes with little diversity. Such limitations reflect the significant sensitivities regarding privacy and consent when it comes to minors. A DHS program specifically trained on images of children, immigrants’ rights organizations and privacy advocates told MIT Technology Review, raises serious concern about whether children will be able to opt out of biometric data collection. Read more from Eileen Guo here.

Bits and BytesA new public database lists all the ways AI could go wrong

The AI Risk Repository documents over 700 potential risks advanced AI systems could pose. It’s the most comprehensive source yet of information about previously identified issues that could arise from the creation and deployment of these models. (MIT Technology Review)

Escaping Spotify’s algorithm

According to a 2022 report published by Distribution Strategy Group, at least 30% of songs streamed on Spotify are recommended by AI. By delivering what people seem to want, has Spotify killed the joy of music discovery? (MIT Technology Review)

How ‘Deepfake Elon Musk’ became the internet’s biggest scammer

An AI-powered version of Mr. Musk has appeared in thousands of inauthentic ads, contributing to billions in fraud. (The New York Times)

Google’s conversational assistant Gemini Live has launched

Google’s Gemini Live, which was teased back in May, is the company’s closest answer to OpenAI’s GPT-4o. The model can hold conversations in real time and you can interrupt it mid-sentence. Google finally rolled it out earlier this week. (Google)

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When Chad Syverson loads the US Bureau of Labor Statistics website these days looking for the latest data on productivity, he does so with a sense of optimism that he hasn’t felt in ages.

The numbers for the last year or so have been generally strong for various financial and business reasons, rebounding from the early days of the pandemic. And though the quarterly numbers are notoriously noisy and inconsistent, the University of Chicago economist is scrutinizing the data to spot any early clues that AI-driven economic growth has begun.

Any effect on the current statistics, he says, will likely still be quite small and won’t be “world-changing,” so he’s not surprised that signs of AI’s impact haven’t been detected yet. But he’s watching closely, with the hope that over the next few years AI could help reverse a two-decade slump in productivity growth that is undermining much of the economy. If that does happen, Syverson says, “then it is world changing.”

The newest versions of generative AI are bedazzling, with lifelike videos, seemingly expert-sounding prose, and other all too humanlike behaviors. Business leaders are fretting over how to reinvent their companies as billions flow into startups, and the big AI companies are creating ever more powerful models. Predictions abound on how ChatGPT and the growing list of large language models will transform the way we work and organize our lives, providing instant advice on everything from financial investments to where to spend your next vacation and how to get there.

But for economists like Syverson, the most critical question around our obsession with AI is how the fledgling technology will (or won’t) boost overall productivity, and if it does, how long it will take. Think of it as the bottom line to the AI hype machine: Can the technology lead to renewed prosperity after years of stagnant economic growth?

Productivity growth is how countries become richer. Technically, labor productivity is a measure of how much a worker produces on average; innovation and technology advances account for most of its growth. As workers and businesses can make more stuff and offer more services, wages and profits go up—at least in theory, and if the benefits are shared fairly. The economy expands, and governments can invest more and get closer to balancing their budgets. For most of us, it feels like progress. It’s why, until the last few decades, most Americans believed their standard of living and financial opportunities would be greater than those of their parents and grandparents.

But when productivity growth is flat or nearly flat, the pie is no longer growing. Even a 1% annual slowdown or speedup can spell the difference between a struggling economy and a flourishing one. In the late 1990s and early 2000s, US labor productivity grew at a healthy rate of nearly 3% a year as the internet age took off. (It grew even faster, well over 3%, in the booming years after World War II). But since about 2005, productivity growth in most advanced economies has been dismal.

There are various possible culprits to blame. But there is a common theme: The seemingly brilliant technologies invented over the last two decades, from the iPhone to ubiquitous search engines to all-consuming social media, have grabbed our attention yet failed to deliver large-scale economic prosperity.

In 2016, I wrote an article titled “Dear Silicon Valley: Forget Flying Cars, Give Us Economic Growth.” I argued that while Big Tech was making breakthrough after breakthrough, it was largely ignoring desperately needed innovations in essential industrial sectors, such as manufacturing and materials. In some ways, it made perfect financial sense: Why invest in these mature, risky businesses when a successful social media startup could net billions?

But such choices came with a cost in sluggish productivity growth. While a few in Silicon Valley and elsewhere became fabulously wealthy, at least some of the political chaos and social unrest experienced in a number of advanced economies over the last few decades can be blamed on the failure of technology to increase financial opportunities for many workers and businesses and expand vital sectors of the economy across different regions.

Some preach patience: The breakthroughs will take time to work through the economy but once they do, watch out! That’s probably true. But so far, the result is a deeply divided country where the techno-optimism—and immense wealth—oozing out from Silicon Valley seem relevant to only a few.

It’s still too early to know how things will shake out this time around—whether generative AI is truly a once-in-a-century breakthrough that will spur a return to financial good times or whether it will do little to create real widespread prosperity. Put another way, will it be like the harnessing of electricity and the invention of the electric motor, which led to an industrial boom, or more like smartphones and social media, which have consumed our collective consciousness without bringing significant economic growth?

For AI, particularly generative models, to have a greater economic impact than other digital advances over the last few decades, we will need to use the technology to transform productivity across the economy—even in how we generate new ideas. It’s a huge undertaking and won’t happen overnight, but we’re at a critical inflection point. Do we start down that path to broadly increased prosperity, or do the creators of today’s breakthrough AI continue to ignore the vast potential of the technology to truly improve our lives?

Cold water on (over)heated speculationA series of studies over the last year show how generative AI can boost productivity for people doing various jobs. Economists at Stanford and MIT have found that those working in call centers are 14% more productive when using AI conversational assistance; notably, there was a 35% improvement in the performance of inexperienced and low-skilled workers. Another study showed that software engineers could code twice as fast with the technology’s help.

Last year, Goldman Sachs calculated that generative AI would likely boost overall productivity growth by 1.5 percentage points every year in developed countries and increase global GDP by $7 trillion over 10 years. And some predict that the effects will appear soon.

Anton Korinek, an economist at the University of Virginia, says the added growth has not yet shown up in the productivity numbers because it takes time for generative AI to diffuse throughout the economy. But he predicts a 1% to 1.5% boost to US productivity by next year. And if there continue to be breakthroughs in generative AI models—think ChatGPT5—the eventual impact could be “significantly higher,” says Korinek.

Not everyone is so bullish. Daron Acemoglu, an MIT economist, says his calculations are a “corrective against those who say that within five years the entire US economy is going to be transformed.” As he sees it, “generative AI could be a big deal. We don’t know yet. But if it is, we’re not going to see transformative effects within 10 years—it’s too soon. It will take time.”

MIT’s Daron Acemoglu calculates that any productivity growth from generative AI will be modest over the next 10 years, and far less than many predict.JARED CHARNEY/MITIn April, Acemoglu posted a paper predicting that generative AI’s impact on total factor productivity (TFP)—the portion that specifically reflects the contribution from innovation and new technologies—will be around 0.6% in total over 10 years, far less than Goldman Sachs and others expect. For decades, TFP growth has been sluggish, and he sees generative AI doing little to significantly reverse the trend—at least in the short term.

Acemoglu says he expects relatively modest productivity gains from generative AI because its Big Tech creators have largely had a narrow focus on using AI to replace people with automation and to enable “online monetization” of search and social media. To have a greater impact on productivity, he argues, AI needs to be useful for a far broader portion of the workforce and relevant for more parts of the economy. Critically, it needs to be used to create new types of jobs, not just to replace workers.

Acemoglu argues that generative AI could be used to expand the capabilities of workers by, for example, supplying real-time data and reliable information for many types of jobs. Think of an intelligent AI agent, but one versed on the intricacies of, say, factory-floor production. Yet, he writes, “these gains will remain elusive unless there is a fundamental reorientation of the [tech] industry, including perhaps a major change in the architecture of the most common generative AI models.”

It’s tempting to think that perhaps it’s simply a matter of tweaking today’s large foundation models with the appropriate data to make them widely useful for various industries. But in fact, we will need to rethink the models and how they can be more effectively deployed in a far broader range of uses.

Producing progressTake manufacturing. For many years, it was one of the important sources of productivity gains in the US economy. It still accounts for much of the country’s R&D. And recent increases in automation and the use of industrial robots might suggest that manufacturing is becoming more productive—but that has not been the case. For somewhat mysterious reasons, productivity in US manufacturing has been a disaster since about 2005, which has played an outsize role in the overall productivity slowdown.

The promise of generative AI in reviving productivity is that it could help integrate everything from initial materials and design choices to real-time data from sensors embedded in production equipment. Multimodal capabilities could allow a factory worker to, say, snap a picture of a problem and ask the AI model for a solution based on the image, the company’s operating manual, any relevant regulatory guidelines, and vast amounts of real-time data from the machinery.

That’s the vision, at least.

The reality is that efforts to deploy today’s foundation models in design and manufacturing are in their very early days. Use of AI so far has been limited to “narrow domains,” says Faez Ahmed, an MIT mechanical engineer specializing in machine learning—think scheduling maintenance on the basis of data from a particular piece of equipment. In contrast, generative AI models could, in theory, be broadly useful for everything from improving initial designs with real data to monitoring the steps of a production process to analyzing performance data on the factory floor.

In a paper released in March, a team of MIT economists and mechanical engineers (including Acemoglu and Ahmed) identified numerous opportunities for generative AI in design and manufacturing, before concluding that “current [generative AI] solutions cannot accomplish these goals due to several key deficiencies.” Chief among the shortcomings of ChatGPT and other AI models are their inability to supply reliable information, their lack of “relevant domain knowledge,” and their “unawareness of industry-standards requirements.” The models are also ill designed to handle the spatial problems on manufacturing floors and the various types of data created by production equipment, including old machinery.

The biggest difficulty is that existing generative AI models lack the appropriate data, says Ahmed. They are trained on data scraped from the internet, and “it’s a lot more about cats and dogs and multimedia content rather than how do you actually operate a lathe machine,” he says. “The reason these models perform relatively poorly on manufacturing tasks is that they’ve never seen manufacturing tasks.”

Gaining access to such data is tricky because much of it is proprietary. “Some people are really scared that a model will take my data and run away with it,” he says. A related problem is that manufacturing requires precision and, often, adherence to strict industry or government guidelines. “If the systems are not precise and not trustworthy, people are less likely to use them,” he says. “And it’s a chicken-and-egg problem: because the models are not precise; because there is no data.”

The MIT researchers called for a “next generation” of AI models that would be tailored to manufacturing. But there is a problem: Creating a manufacturing-relevant AI that takes advantage of the power of foundation models will require close collaboration between industry and AI companies, and that’s something still in its nascent stage.

The lack of progress so far, says Ranveer Chandra, managing director of research for industry at Microsoft Research, “is not because people are not interested, or they don’t see the business value.” The holdup is finding ways to secure the data and make sure it is in a useful form and provides relevant answers to specific manufacturing questions.

Microsoft is pursuing several strategies. One is asking the foundation model to base its answers on a company’s proprietary data—say, a company’s operations manual and production data. A far more difficult but appealing alternative is fine-tuning the underlying architecture of the model to better suit manufacturing. Yet another approach: so-called small language models, which also can be trained specifically on the data from a company. Since they are smaller than foundation models like GPT-4, they need less computational power and can be more targeted to specific manufacturing tasks.

“But this is all research at this point,” says Chandra. “Have we solved it? Not yet.”

A gold mine of new ideasUsing AI to boost scientific discovery and innovation could have the greatest overall productivity impact over the long term. Economists have long recognized new ideas as the source of long-term growth, and the hope is that new AI tools could turbocharge the search for them. While improving the efficiency of, say, a call center worker could mean a one-time jump in productivity in that business, using AI to improve the process of inventing new technologies and business practices—to create useful new ideas—could lead to an enduring increase in the rate of economic growth as it reshapes the innovation process and the way research is done.

There are already tantalizing clues to AI’s potential.

Most notably, Google DeepMind, which defines its mission as “solving some of the hardest scientific and engineering challenges of our time,“ says more than 2 million users have accessed its deep-learning AI system to predict protein folding. Many drugs target a particular protein, and knowing the 3D structure of such proteins—something that traditionally takes painstaking lab analysis—could be an invaluable step in creating new medicines. In May, Google released AlphaFold 3, claiming it “predicts the structure and interactions of all of life’s molecules“ to help identify how various biomolecules alter each other, providing an even more powerful guide for finding new drugs.

Creators of AI models, including DeepMind and Microsoft Research, are also working on other problems in biology, genomics, and materials science. The hope is that generative AI could help scientists glean key information from the vast data sets common in these fields, making it easier and faster to, say, discover new drugs and materials.

We badly need such a boost. A few years ago, a team of leading economists wrote a paper called “Are Ideas Getting Harder to Find?“ and found that it takes more and more researchers and money to find the kinds of new ideas that are key to sustaining technology advances. The problem, in technical terms, is that research productivity—the output of ideas given the number of scientists—is falling rapidly. In other words—yes, ideas are getting harder to find. We’ve generally kept up by adding more researchers and investing more in R&D, but overall US research productivity itself is in a deep decline.

To uphold Moore’s Law, which predicts that the number of transistors on a chip will double roughly every two years, the semiconductor industry needs 18 times more researchers than it had in the early 1970s. Likewise, it takes far more scientists to come up with roughly the same number of new drugs than it did a few decades ago.

Could AI dream up safe and effective new drugs and find astonishing new materials for computation and clean energy?

John Van Reenen, a professor at the London School of Economics and one of the authors of the paper, knows it’s still too early to see any real change in the productivity data from AI, but he says, “The hope is that [it] can make some difference.” AlphaFold is “a poster child” for how AI can change science, he says, and “the question is whether this can go from anecdotes to something more systematic.”

The ambition is not only to supply various tools that will make the lives of scientists easier, like automated literature search, but for AI itself to come up with original and useful scientific ideas that would otherwise evade researchers. In that vision, AI dreams up new compounds that are more effective and safer than existing drugs, and astonishing materials that expand the possibilities of computation and clean energy. The goal is especially compelling because the universe of potential molecules is virtually unlimited. Navigating such a nearly infinite space and exploring the vast number of possibilities is what machine learning is especially good at.

But don’t hold your breath for AI’s Thomas Edison moment. Though the scientific popularity of AlphaFold has raised expectations for the potential of AI, it is still very early days in turning the research into actual products—whether new drugs or novel materials. In a recent analysis, a team of MIT scientists put it this way: “Generative AI has undoubtedly broadened and accelerated the early stages of chemical design. However, real-world success takes place further downstream, where the impact of AI has been limited so far.”

In fact, the process of turning the intriguing scientific advances in using AI into actual, useful stuff is still very much in its infancy.

It’s a material worldPerhaps nowhere is the excitement over AI’s potential to transform research greater than in the often neglected field of materials discovery. The world desperately needs better materials. We need them for cheaper and more powerful batteries and solar cells, and for new types of catalysts that would make cleaner industrial processes possible; we need practical high-temperature superconductors to revolutionize how we transport electricity.

So when DeepMind said it had used deep learning to discover some 2.2 million inorganic crystals—including some 380,000 predicted to be stable and promising candidates for actual synthesis—the report was greeted with great excitement, especially in the AI community. A materials revolution! It seemed like a gold mine of new stuff—“an order-of-magnitude expansion in stable materials known to humanity,” wrote the DeepMind researchers in Nature. The DeepMind database, called GNoME (an acronym for “graph networks for materials exploration”), is “equivalent to 800 years of knowledge,” according to the company’s media release.

But in the months after the paper, some researchers disputed the hype. Materials scientists at the University of California, Santa Barbara, published a paper in which they reported finding “scant evidence“ that any of the structures in the DeepMind database fulfilled the “trifecta of novelty, credibility, and utility.“

For some tasked with finding new materials, the huge databases of possible inorganic crystals, many of which may not be stable enough to actually exist, seems like a distraction. “If you spam us with 400,000 new materials and we don’t even know which one of those are realistic, then we don’t know which one of those will be good for a battery or catalyst or whatever you want to make them. Then this information is not useful,” says Leslie Schoop, a chemist at Princeton who co-wrote a paper describing the challenges of using automation and AI in materials discovery and synthesis.

To be clear, this doesn’t mean that AI won’t prove to be important in materials science and chemistry. Even critics say they are excited by the long-term possibilities. But the criticisms hint at just how early we are in using AI to tackle the daunting task of materials discovery and making it a reliable tool for finding new compounds that are better than existing ones.

It’s extremely expensive and time-consuming to make and test any possible new material. What industrial researchers really need are reliable clues pointing to materials that are predictably stable, can be synthesized, and likely have intriguing properties, including being cheap to make.

The GNoME database probably includes interesting compounds, say its DeepMind scientific creators. But they acknowledge it’s only a preliminary step in showing how AI could help in materials discovery. Much work remains to broaden its usefulness.

Ekin Dogus Cubuk, a Google research scientist and coauthor of the Nature paper, describes the work it reports as an advance in predicting a large number of possible inorganic crystals that are stable, based on quantum-mechanical calculations, at absolute zero, where atomic motion comes to a standstill. Such predictions could be useful for those running computational simulations of new materials—a very early stage of materials discovery.

But, he says, machine learning has not yet been used to predict crystals that are stable at room temperature. After that is achieved comes the goal of using AI to predict how structures can be synthesized in the lab, and eventually how to make them at larger scale. All that must be done before machine learning can really transform the lengthy and expensive process of coming up with new materials, he says.

For those hoping that AI models could boost economic productivity by transforming science, one lesson is clear: Be patient. Such scientific advances could well have an impact one day. But it will take time—likely measured in decades.

The Solow paradoxAs senior vice president for research, technology, and society at Google, James Manyika is unsurprisingly enthusiastic about the huge potential for AI to transform the economy. But he is far from an unabashed cheerleader, mindful of the lessons gleaned from his years of studying how technologies affect productivity.

Before joining Google in 2022, Manyika spent several decades as a consultant, a researcher, and finally chairman of the McKinsey Global Institute, the economic research arm of the consulting giant. At McKinsey he became a leading authority on the link between technology and economic growth, and he counts Robert Solow—the MIT economist who won the 1987 Nobel Prize for explaining how technological advances are the main source of productivity growth—as an early mentor.

Among the lessons from Solow, who died late last year at the age of 99, is that even powerful technologies can take time to affect economic growth. In 1987, Solow quipped: “You can see the computer age everywhere but in the productivity statistics.” At the time, information technology was undergoing a revolution, most visible with the introduction of the personal computer. Yet productivity, as measured by economists, was sluggish. This became known as the Solow paradox. It wasn’t until the late 1990s, decades after the birth of the computer age, that productivity growth began to finally pick up.

History has taught Manyika to be circumspect in predicting how and when the overall economy will feel the impact of generative AI. “I don’t have a time frame,” he says. “The estimates [of productivity gains] are generally spectacularly large, but when it comes to a question of time frame, I say ‘It depends.’”

Specifically, he says it depends on what economists call “the pace of diffusion”—basically, how quickly users take up the technology both within sectors and across sectors. It also hinges on the ability of various users, especially businesses in the largest sectors of the economy, to “[reorganize] functions and tasks and processes to capitalize on the technology” and to make their operations and workers more productive. Without those pieces, we’ll be stuck in “Solow paradox land,” says Manyika.

“Tech can do whatever tech wants, and it doesn’t really matter from a labor productivity standpoint,” he says, since its workforce is relatively small. “We have to have changes happen in the largest sectors before we can start to see productivity gains at an economy level.”

Late last year, Manyika co-wrote a piece in Foreign Affairs called “The Coming AI Economic Revolution; Can Artificial Intelligence Reverse the Productivity Slowdown?” In it, the authors offered a decidedly optimistic though cautious answer.

“By the beginning of the next decade, the shift to AI could become a leading driver of global prosperity,” they wrote, because it has the potential to affect “just about every aspect of human and economic activity.” They added: “If these innovations can be harnessed, AI could reverse the long-term declines in productivity growth that many advanced economies now face.” But it’s a big if, they acknowledged, saying it “won’t happen on its own” and will require “positive policies that foster AI’s most productive uses.”

Google’s James Manyika says AI’s impact on the economy is potentially huge but will depend on how quickly business users adopt and deploy the technology.ARNO MIKKOR/WIKIMEDIA COMMONSThe call for policies is a recognition of the immense task ahead, and an acknowledgment that even giant AI companies like Google can’t do it alone. It will take widespread investments in infrastructure and additional innovations by governments and businesses.

Companies ranging from small startups to large corporations will need to take the foundation models, such as Google’s Gemini, and “tailor them for their own applications in their own environments in their own domains,” says Manyika. In a few cases, he says, Google has done some of the tailoring, “because it’s kind of interesting to us.”

For example, Google released Med-Gemini in May, using the multimodal abilities of its foundation model to help in a wide range of medical tasks, including making diagnostic decisions based on imaging, videos of surgeries, and information in electronic health records. Now, says Manyika, it’s up to health-care practitioners and researchers to “think how to apply this, because we’re not in the health-care business in that way.” But, he says, “it is giving them a running start.”

But therein lies the great challenge going forward if AI is to transform the economy.

Despite the fanfare around generative AI and the billions of dollars flowing to startups around the technology, the speed of its diffusion into the business world is not all that encouraging. According to a survey of thousands of businesses by the US Census Bureau, released in March, the proportion of firms using AI rose from about 3.7% in September 2023 to 5.4% this February, and it is expected to reach around 6.6% by the end of the year. Most of this uptake has come in sectors like finance and technology. Industries like construction and manufacturing are virtually untouched. The main reason for the lack of interest: what most companies see as the “inapplicability” of AI to their business.

For many companies, particularly small ones, it still takes a huge leap of faith to bet on AI and invest the money and time it takes to reorganize business functions around it. In addition to not seeing any value in the technology, lots of business leaders have ongoing questions over the reliability of the generative AI models—hallucinations are one thing in the chat room but quite something else on the manufacturing floor or in a hospital ER. They also have concerns over data privacy and the security of proprietary information. Without AI models more tailored to the needs of various businesses, it’s likely that many will stay on the sidelines.

Meanwhile, Silicon Valley and Big Tech are obsessed with intelligent agents and with videos vreated by generative AI; individual and corporate fortunes are being amassed on the promise of turbocharging smartphones and internet searches. As in the early 2010s, much of the rest of the economy is being left out. They’re not benefiting either from the financial rewards of the technology or from its ability to expand large sectors and make them more productive.

Maybe it’s too much to expect Big Tech to change, to suddenly care about using its massive power to benefit sectors such as manufacturing. After all, Big Tech does what it does.

And it won’t be easy for AI companies to rethink their huge foundation models for such real-world problems. They will need to engage with industry experts from a wide variety of sectors and respond to their needs. But the reality is that the big AI companies are the only organizations with the vast computational power to run today’s foundation models and the talent to invent the next generations of the technology.

So like it or not, in dominating the field, they have taken on the responsibility for its broad applicability. Whether they will shoulder that responsibility for all our benefit or (once again) ignore it for the siren song of wealth accumulation will eventually reveal itself—perhaps initially in those often nearly indecipherable quarterly numbers from the US Bureau of Labor Statistics website.

Correction: we updated the description of the Princeton paper

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Whether pursuing digital transformation, exploring the potential of AI, or simply looking to simplify and optimize existing IT infrastructure, today’s organizations must do this in the context of increasingly complex multi-cloud environments. These complicated architectures are here to stay—2023 research by Enterprise Strategy Group, for example, found that 87% of organizations expect their applications to be distributed across still more locations in the next two years.

Scott Sinclair, practice director at Enterprise Strategy Group, outlines the problem: “Data is becoming more distributed. Apps are becoming more distributed. The typical organization has multiple data centers, multiple cloud providers, and umpteen edge locations. Data is all over the place and continues to be created at a very rapid rate.”

DOWNLOAD THE REPORTFinding a way to unify this disparate data is essential. In doing so, organizations must balance the explosive growth of enterprise data; the need for an on-premises, cloud-like consumption model to mitigate cyberattack risks; and continual pressure to cut costs and improve performance.

Sinclair summarizes: “What you want is something that can sit on top of this distributed data ecosystem and present something that is intuitive and consistent that I can use to leverage the data in the most impactful way, the most beneficial way to my business.”

For many, the solution is an overarching software-defined, virtualized data platform that delivers a common data plane and control plane across hybrid cloud environments. Ian Clatworthy, head of data platform product marketing at Hitachi Vantara, describes a data platform as “an integrated set of technologies that meets an organization’s data needs, enabling storage and delivery of data, the governance of data, and the security of data for a business.”

Gartner projects that these consolidated data storage platforms will constitute 70% of file and object storage by 2028, doubling from 35% in 2023. The research firm underscores that “Infrastructure and operations leaders must prioritize storage platforms to stay ahead of business demands.”

A transitional moment for enterprise dataHistorically, organizations have stored their various types of data—file, block, object—in separate silos. Why change now? Because two main drivers are rendering traditional data storage schemes inadequate for today’s business needs: digital transformation and AI.

As digital transformation initiatives accelerate, organizations are discovering that having distinct storage solutions for each workload is inadequate for their escalating data volumes and changing business landscapes. The complexity of the modern data estate hinders many efforts toward change.

Clatworthy says that when organizations move to hybrid cloud environments, they may find, for example, that they have mainframe or data center data stored in one silo, block storage running on an appliance, apps running file storage, another silo for public cloud, and a separate VMware stack. The result is increased complexity and
cost in their IT infrastructure, as well as reduced flexibility and efficiency.

Then, Clatworthy adds, “When we get to the world of generative AI that’s bubbling around the edges, and we’re going to have this mass explosion of data, we need to simplify how that data is managed so that applications can consume it. That’s where a platform comes in.”

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The race to save our online lives from a digital dark age

There is a photo of my daughter that I love. She is sitting, smiling, in our old back garden, chubby hands grabbing at the cool grass. It was taken on a digital camera in 2013, when she was almost one, but now lives on Google Photos.

But what if, one day, Google ceased to function? What if I lost my treasured photos forever? For many archivists, alarm bells are ringing. Across the world, they are scraping up defunct websites or at-risk data collections to save as much of our digital lives as possible. Others are working on ways to store that data in formats that will last hundreds, perhaps even thousands, of years.

The endeavor raises complex questions. What is important to us? How and why do we decide what to keep—and what do we let go? And how will future generations make sense of what we’re able to save? Read the full story.

—Niall Firth

Niall’s story is from the forthcoming print issue of MIT Technology Review, which is celebrating 125 years of the magazine! It’s set to go live on Wednesday August 28, so if you don’t already, subscribe now to get a copy when it lands.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 X is ceasing operations in Brazil
The company is locked in a legal battle with the country’s Supreme Court Justice. (TechCrunch)
+ Users in Brazil will still be able to access the platform, though. (Reuters)
+ X alternatives are just a bit…lacking. (The Guardian)

2 Far right influencers are unhappy with Donald Trump’s campaign
They’ve accused his team of watering down his persona and policies. (WP $)
+ The FBI is increasingly cautious about investigating far right groups. (New Yorker $)

3 Startups are struggling in the USEspecially if they’re not focusing fully on AI. (FT $)
+ Commercializing AI isn’t as simple as many people make out, though. (Vox)

4 A superconducting wire has set a new record
It can apparently carry 50% as much current as the previous record-holding wire. (IEEE Spectrum)

5 Waymo’s robotaxis are keeping San Francisco residents up at night
They just keep honking, even after a rapidly-deployed fix. (The Verge)
+ Amazon’s delivery drones are pretty noisy too. (Insider $)
+ What’s next for robotaxis in 2024. (MIT Technology Review)

6 Worldcoin is still running into troubleJurisdictions across the world are concerned by Sam Altman’s data-grabbing project. (WSJ $)
+ Deception, exploited workers, and cash handouts: How Worldcoin recruited its first half a million test users. (MIT Technology Review)

7 Your guts are teaming with virusesBut we’re not sure how many, or what they’re doing there. (Knowable Magazine)
+ How bacteria-fighting viruses could go mainstream. (MIT Technology Review)

8 Are the Ray-Ban Meta glasses cool now?
Mark Zuckerberg certainly thinks so. (The Information $)
+ Their launch back in 2021 was marred by privacy concerns. (MIT Technology Review)

9 Tarot readers are sick of Instagram scammersI guess they didn’t see it coming? (The Guardian)

10 TikTok’s favorite restaurant is entirely fictional
Its cast of characters delights millions of fans. (NBC News)
+ Chinese social media users are skillfully parodying AI video goofs. (Ars Technica)

Quote of the day

“It’s like asking about the risks of replacing a car with a big cardboard cutout of a car. Sure, it looks like a car, but the ‘risk’ is that you no longer have a car.”

—Arvind Narayanan, a computer science professor at Princeton University, compares a chatbot running a city—which could become a reality in Wyoming’s capital city—to driving an imaginary car, the Washington Post reports.

The big story

Alina Chan tweeted life into the idea that the virus came from a lab.

June 2021

Alina Chan started asking questions in March 2020. She was chatting with friends on Facebook about the virus then spreading out of China. She thought it was strange that no one had found any infected animal. She wondered why no one was admitting another possibility, which to her seemed very obvious: the outbreak might have been due to a lab accident.

Chan is a postdoc in a gene therapy lab at the Broad Institute, a prestigious research institute affiliated with both Harvard and MIT. Throughout 2020, Chan relentlessly stoked scientific argument, and wasn’t afraid to pit her brain against the best virologists in the world. Her persistence even helped change some researchers’ minds. Read the full story.

—Antonio Regalado

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• I don’t know how many walnuts I could crush with my elbow in 30 seconds, but I do know it wouldn’t be this many.
• Stop buying poor quality clothing: these tips can help you decide whether a garment’s worth your cash.
• An important investigation into how many times Pitbull has used his catchphrase ‘dale.’
• Let’s road trip!

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There is a photo of my daughter that I love. She is sitting, smiling, in our old back garden, chubby hands grabbing at the cool grass. It was taken in 2013, when she was almost one, on an aging Samsung digital camera. I originally stored it on a laptop before transferring it to a chunky external hard drive.

A few years later, I uploaded it to Google Photos. When I search for the word ”grass,” Google’s algorithm pulls it up. It always makes me smile.

I pay Google £1.79 a month to keep my memories safe. That’s a lot of trust I’m putting in a company that’s existed for only 26 years. But the hassle it removes seems worth it. There’s just so much stuff nowadays. The admin required to keep it updated and stored safely is just too onerous.

My parents didn’t have this problem. They took occasional photos of me on a film camera and periodically printed them out on paper and put them in a photo album. These pictures are still viewable now, 40-odd years later, on faded yellowing photo paper—a few frames per year.

Many of my memories from the following decades are also fixed on paper. The letters I received from my friends when traveling abroad in my 20s were handwritten on lined paper. I still have them crammed in a shoebox, an amusing but relatively small archive of an offline time.

We no longer have such space limitations. My iPhone takes thousands of photos a year. Our Instagram and TikTok feeds are constantly updated. We collectively send billions of WhatsApp messages and texts and emails and tweets.

But while all this data is plentiful, it’s also more ephemeral. One day in the maybe-not-so-distant future, YouTube won’t exist and its videos may be lost forever. Facebook—and your uncle’s holiday posts—will vanish. There is precedent for this. MySpace, the first largish-scale social network, deleted every photo, video, and audio file uploaded to it before 2016, seemingly inadvertently. Entire tranches of Usenet newsgroups, home to some of the internet’s earliest conversations, have gone offline forever and vanished from history. And in June this year, more than 20 years of music journalism disappeared when the MTV News archives were taken offline.

For many archivists, alarm bells are ringing. Across the world, they are scraping up defunct websites or at-risk data collections to save as much of our digital lives as possible. Others are working on ways to store that data in formats that will last hundreds, perhaps even thousands, of years.

The endeavor raises complex questions. What is important to us? How and why do we decide what to keep—and what do we let go?

And how will future generations make sense of what we’re able to save?

“Welcome to the challenge of every historian, archaeologist, novelist,” says Genevieve Bell, a cultural anthropologist. “How do you make sense of what’s left? And then how do you avoid reading it through the lens of the now?”

Last-chance saloonThere is more stuff being created now than at any time in history. At Google’s I/O conference this year, the firm’s CEO, Sundar Pichai, said that 6 billion photos and videos are uploaded to Google Photos every day. More than 40 million WhatsApp messages are sent every minute.

Even with so much more of it, though, our data is more fragile than ever. Books could burn in a freak library fire, but data is much easier to wipe forever. We’ve seen it happen—not only in incidents like the accidental deletion of MySpace data but also, sometimes, with intent.

In 2009, Yahoo announced it was going to pull the plug on the web-hosting platform GeoCities, putting millions of carefully created web pages on the chopping block. While most of these pages might seem inconsequential—GeoCities was famous for its amateurish, early-web aesthetic and its pages dedicated to various collections, obsessions, or fandoms—they represented an early chapter of the web, and one that was about to be lost forever.

And it would have been, if a ragtag group of volunteer archivists led by Jason Scott hadn’t stepped in.

“We sprang into action, and part of the fury and confusion of the time was we were going from downloading a handful of interesting sites to suddenly taking on an anchoring website of the early web,” Scott recalls.

His group, called Archive Team, quickly mobilized and downloaded as many GeoCities pages as possible before it closed for good. He and the team ended up being able to save most of the site, archiving millions of pages between April and October 2009. He estimates that they managed to download and store around a terabyte, but he notes that the size of GeoCities waxed and waned and was around nine terabytes at its peak. Much was likely gone for good. “It contained 100% user-generated works, folk art, and honest examples of human beings writing information and histories that were nowhere else,” he says.

Known for his top hat and cyberpunk-infused sense of style, Scott has made it his life’s mission to help save parts of the web that are at risk of being lost. “It is becoming more understood that archives, archiving, and preservation are a choice, a duty, and not something that just happens like the tides,” he says.

Scott now works as “free-range archivist and software curator” with the Internet Archive, an online library started in 1996 by the internet pioneer Brewster Kahle to save and store information that would otherwise be lost.

As a society, we’re creating so much new stuff that we must always delete more things than we did the year before.

Over the past two decades, the Internet Archive has amassed a gigantic library of material scraped from around the web, including that GeoCities content. It doesn’t just save purely digital artifacts, either; it also has a vast collection of digitized books that it has scanned and rescued. Since it began, the Internet Archive has collected more than 145 petabytes of data, including more than 95 million public media files such as movies, images, and texts. It has managed to save almost half a million MTV news pages.

Its Wayback Machine, which lets users rewind to see how certain websites looked at any point in time, has more than 800 billion web pages stored and captures a further 650 million each day. It also records and stores TV channels from around the world and even saves TikToks and YouTube videos. They are all stored across multiple data centers that the Internet Archive owns itself.

It’s a Sisyphean task. As a society, we’re creating so much new stuff that we must always delete more things than we did the year before, says Jack Cushman, director at Harvard’s Library Innovation Lab, where he helps libraries and technologists learn from one another. We “have to figure out what gets saved and what doesn’t,” he says. “And how do we decide?”

MIKE MCQUADEArchivists have to make such decisions constantly. Which TikToks should we save for posterity, for example?

We shouldn’t try too hard to imagine what future historians would find interesting about us, says Niels Brügger, an internet researcher at Aarhus University in Denmark. “We cannot imagine what historians in 30 years’ time would like to study about today, because we don’t have a clue,” he says. “So we shouldn’t try to anticipate and sort of constrain the possible questions that future historians would ask.”

Instead, Brügger says, we should just save as much stuff as possible and let them figure it out later. “As a historian, I would definitely go for: Get it all, and then historians will find out what the hell they’re going to do with it,” he says.

At the Internet Archive, it’s the stuff most at risk of being lost that gets prioritized, says Jefferson Bailey, who works there helping develop archiving software for libraries and institutions. “Material that is ephemeral or at risk or has not yet been digitized and therefore is more easily destroyed, because it’s in analog or print format—those do get priority,” he says.

People can request that pages be archived. Libraries and institutions also make nominations. And the staff sorts out the rest. Across open social media like TikTok and YouTube, archive teams at libraries around the world select certain accounts, copy what they want to save, and share those copies with the Internet Archive. It could be snapshots of what was trending each day, as well as tweets or videos from accounts run by notable individuals such as the US president.

The process can’t capture everything, but it offers a pretty good slice of what has preoccupied us in the early decades of the 21st century. While historical records have typically relied upon the private letters and belongings of society’s richest, an archive process that scrapes tweets is always going to be a bit more egalitarian.

“You can get a very interesting and diverse snapshot of our cultural moments of the last 30, 40 years,” says Bailey. “That is very different from what a traditional archive looked like 100 years ago.”

As citizens, we could also help future historians. Brügger suggests people could make “data donations” of their personal correspondence to archives. “One week per year, invite everyone to donate the emails from that week,” he says. “If you had these time slices of email correspondence from thousands of people, year by year, that would be really great.”

Scott imagines future historians eventually using AI to query these archives to gain a unique insight into how we lived. “You’ll be able to ask a machine: ‘Could you show me images of people enjoying themselves at amusement parks with their families from the ’60s?’ and it will go, ‘Here you go,’” he says. “The work we did up to here was done in faith that something like this might exist.”

The past guides the futureHuman knowledge doesn’t always disappear with a dramatic flourish like GeoCities; sometimes it is erased gradually. You don’t know something’s gone until you go back to check it. One example of this is “link rot,” where hyperlinks on the web no longer direct you to the right target, leaving you with broken pages and dead ends. A Pew Research Center study from May 2024 found that 23% of web pages that were around in 2013 are no longer accessible.

It’s not just web links that die without constant curation and care. Unlike paper, the formats that now store most of our data require certain software or hardware to run. And these tools can become obsolete quickly. Many of our files can no longer be read because the applications that read them are gone or the data has become corrupted, for example.

One way to mitigate this problem is to transfer important data to the latest medium on a regular basis, before the programs required to read it are lost forever. At the Internet Archive and other libraries, the way information is stored is refreshed every few years. But for data that is not being actively looked after, it may be only a few years before the hardware required to access it is no longer available. Think about once ubiquitous storage mediums like Zip drives or CompactFlash.

Some researchers are looking into ways to make sure we can always access old digital formats, even if the kit required to read them has become a museum piece. The Olive project, run by Mahadev Satyanarayanan at Carnegie Mellon University, aims to make it possible for anyone to use any application, however old, “with just a click.” His team has been working since 2012 to create a huge, decentralized network that supports “virtual machines”—emulators for old or defunct operating systems and all the software that they run.

Keeping old data alive like this is a way to protect against what the computer scientist Danny Hillis once dubbed the “digital dark age,” a nod to the early medieval period when a lack of written material left future historians little to go on.

Hillis, an MIT alum who pioneered parallel computing, thinks the rapid technological upheaval of our time will leave much of what we’re living through a mystery to scholars.

“As I get older, I keep thinking, how can I be a good ancestor?”

Vint Cerf, one of the internet’s founders

“When people look back at this period, they’ll say, ‘Oh, well, you know, here was this sort of incomprehensibly fast technological change, and a lot of history got lost during that change,” he says.

Hillis was one of the founders (along with Brian Eno and Stewart Brand) of the Long Now Foundation, a San Francisco–based organization that is known for its eye-catching art/science projects such as the Clock of the Long Now, a Jeff Bezos–funded gigantic mechanical clock currently under construction in a mountain in West Texas that is designed to keep accurate time for 10,000 years. It also created the Rosetta Disc, a circle of nickel that has been etched at microscopic scale with documentation for around 1,500 of the world’s languages. In February, a copy of the disc touched down on the moon aboard the Odysseus lander. Part of the Long Now’s focus is to help people think about how we protect our history for future generations. It’s not just about making life easier for historians. It’s about helping us be “better ancestors,” according to the organization’s mission statement.

It’s a sentiment that chimes with Vint Cerf, one of the internet’s founders. “As I get older, I keep thinking, how can I be a good ancestor?” he says.

“An understanding of what has happened in the past is helpful for anticipating or interpreting what’s happening in the present and what might happen in the future,” says Cerf. There are “all kinds of scenarios where the absence of knowledge of the past is a debilitating weakness for a society.”

“If we don’t remember, we can’t think, and the way that society remembers is by writing things down and putting them in libraries,” agrees Kahle. Without such repositories, he says, “people will be confused as to what’s true and not true.”

Kahle started the Internet Archive as a way to make sure all knowledge is free for anyone, but he feels the balance of power has tilted away from libraries and toward corporations. And that is likely to be a problem for keeping things accessible in the long term.

“If it’s left up to the corporations, it’s all gone,” he says. “Not only are we talking about classic published works—like your magazine, or books—but we’re talking about Facebook pages, Twitter pages, your personal blogs. All of those in general are on corporate platforms now. And those will all disappear.”

Losing our long-term digital archives has real implications for how society runs, says Harvard’s Cushman, who points out that our legal decisions and paperwork are largely stored digitally. Without a permanent, unalterable record, we can no longer rely on past judgments to inform the present. His team has created ways to let courts and law journals put copies of web pages on file at the Harvard Law Library, where they are stored indefinitely as a record of legal precedent. It’s also creating tools to let people interact with these archives by scrolling through historical versions of a site, or by using a custom GPT to interact with collections.

Many other groups are working on similar solutions. The US Library of Congress has suggested standards for storing video, audio, and web files so they are accessible for future generations. It urges archivists to think about issues such as whether the data includes instructions on how to access it, or how widely adopted the format has been (the idea being that a more prevalent one is less likely to become obsolete quickly).

But ultimately, digital archives are harder to keep than physical archives, says Cushman. “If you run out of budget and leave books in a quiet, dark room for 10 years, they’re happy,” he says. “If you fail to pay your AWS bill for a month, your files are gone forever.”

Storage for impossible time scalesEven the physical way we store digital data is impermanent. Most long-term storage in data centers—for use in disaster recovery, among other applications—is on magnetic hard drives or tape. Hard drives wear out after a few years. Tape is a little better, but it still doesn’t get you much beyond a decade or so of storage use before it begins to fail.

Companies make new backups all the time, so this is less of a problem for the short-to-medium term. But when you want to store important cultural, legal, or historical information for the ages, you need to think differently. You need something that can store huge amounts of data but can also withstand the test of time and doesn’t need constant care.

DNA has often been touted as a long-term storage option. It can store astonishing amounts of information and is incredibly long-lasting. Pieces of bone contain readable DNA from many hundreds of thousands of years ago. But encoding information in DNA is currently expensive and slow, and specialized equipment is required to “read” the information back later. That makes it impractical as a serious long-term backup for our world’s knowledge, at least for now.

MIKE MCQUADELuckily, there are already a handful of compelling alternatives. One of the most advanced ideas is Project Silica, currently under development at Microsoft Research in Cambridge, UK, where Richard Black and his team are creating a new form of long-term storage on glass squares that can last hundreds or even thousands of years.

Each one is created using a precise, powerful laser, which writes nanoscale deformations into the glass beneath the surface that can encode bits of information. These tiny imperfections are layered up on top of one another in the glass and are then read using a powerful microscope that can detect the way light is refracted and polarized. Machine learning is used to decode the bits, and each square has enough training data to let future historians retrain a model from scratch if required, says Black.

When I hold one of the Silica squares in my hand, it feels pleasingly sci-fi, as if I’ve just pulled it out to shut down HAL in 2001: A Space Odyssey. The encoded data is visible as a faint blue where the light hits the imperfections and scatters. A video shared by Microsoft shows these squares being microwaved, boiled, baked in an oven, and zapped with a high-powered magnet, all with no apparent ill effects.

Black imagines Silica being used to store long-term scientific archives, such as medical information or weather data, over decades. Crucially, the technology can create archives that can be air-gapped (cut off from the internet) and need no power or special care. They can just be locked away in a silo and should work fine and be readable centuries from now. “Humanity has never stopped building microscopes,” says Black. In 2019 Warner Bros. archived some of its back catalogue on Silica glass, including the 1978 classic Superman.

Black’s team has also designed a library storage system for Silica. Shelves packed with thousands of the glass squares line a small room at the Cambridge office. Handbag-size robots attached to the shelves whiz along them and occasionally stop, unclip themselves from one shelf, and clamber up or down to another before shooting off again down the line. When they reach a specific spot, they stop and pluck one of the squares, no bigger than a CD, from the shelf. Its contents are read and the robot zips back into position.

Meanwhile, deep in the vaults of an abandoned mine in Svalbard, Norway, GitHub is storing some of history’s most important software (including the source code for Linux, Android, and Python) on special film its creators claim can last for more than 500 years. The film, made by the firm Piql, is coated in microscopic silver halide crystals that permanently darken when exposed to light. A high-powered light source is used to create dark pixels just six micrometers across, which encode binary data. A scanner then reads the data back. Instructions for how to access the information are written in English on each roll, in case there is no longer anyone around to explain how it works.

In addition to GitHub’s collection, the storage facility, known as the Arctic World Archive, also includes data supplied by the Vatican and the European Space Agency, as well as various artworks and images from governments and institutions around the world. Yale University, for example, has stored a collection of software, including Microsoft Office and Adobe, as Piql data. Just a few hundred meters down the road you find the Svalbard Global Seed Vault, a storage facility preserving a selection of the world’s biodiversity for future generations. Data about what each seed container holds is also stored on Piql film.

Making sure this information is stored in formats that can be decoded hundreds of years from now will be crucial. As Cushman points out, we still argue over the proper way to play Charlie Chaplin films because the intended playback speed was never recorded. “When researchers are trying to access these materials decades in the future, how expensive will it be to build tools to display them, and what will be the chances that we get it wrong?” he asks.

Ultimately, the motivation for all these projects is the idea that they will act as humanity’s backup. A long-term medium that will withstand an apocalypse, an electromagnetic pulse from the sun, the end of civilization, and let us start again.

Something to let people know we were here.

Happy accidentsSometime in the first century, a Roman woman called Claudia Severa was planning a big birthday party at a fort in northern England. She asked her servant to write out an invitation to one of her best friends on a wooden tablet and then signed it with a flourish.

Claudia could never have suspected that, almost 2,000 years on, the Vindolanda Tablets (of which her invitation is the most famous) would be used to give us a unique insight into the daily lives of Romans in England at that time.

That’s always the way. Throughout history, the oddest, most random things survived to act as a guide for historians. The same will go for us. Despite the efforts of archivists, librarians, and storage researchers, it’s impossible to know for sure what data will still be accessible when we’re long gone. And we might be surprised at what they find interesting when they come across it. Which batch of archived emails or TikToks will be the key to unlocking our era for future historians and anthropologists? And what will they think of us?

Historians foraging through our digital detritus may be left with a series of unanswerable questions, and they’ll just have to make best guesses.

Throughout history, the oddest, most random things survived to act as a guide for historians. The same will go for us.

“You’d need to ask about who had digital technology,” says Bell. “And how did they power it? And who got to make choices about it? And how was it stored and circulated? And who saw it?”

We don’t know what will still be running 20, 50, or 100 years from now. Perhaps Google Photos’ cloud storage will have been abandoned, a giant garbage pile of old hard drives buried in the ground. Or maybe, with luck, one of the spiritual heirs to Scott’s archivists will have saved it before it went down.

Maybe someone downloaded it onto some sort of glass disc and stashed it in a vault somewhere.

Maybe some future anthropologist will one day find it, dust it off, and find that it’s still readable.

Maybe they’ll select a file at random, spin up some sort of software emulator, and find a billion photos from 2013.

And see a chubby, happy girl sitting in the grass.

NIALL FIRTH

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

What the future holds for those born today

Happy birthday, baby.

You have been born into an era of intelligent machines. They have watched over you almost since your conception. They let your parents listen in on your tiny heartbeat, track your gestation on an app, and post your sonogram on social media. Well before you were born, you were known to the algorithm.

In the future, everyone thinks, computers will get smaller and more plentiful still. But the biggest change in your lifetime will be the rise of intelligent agents. Computing will be more responsive, more intimate, less confined to any one platform. It will be less like a tool, and more like a companion. It will learn from you and also be your guide.

Your arrival coincided with the 125th anniversary of this magazine. With a bit of luck and the right genes, you might see the next 125 years. How will you and the next generation of machines grow up together? We asked more than a dozen experts to imagine your joint future. Read what they prophesied for your future.

—Kara Platoni

Kara’s story is from the forthcoming print issue of MIT Technology Review, which is celebrating 125 years of the magazine! It’s set to go live on Wednesday August 28, so if you don’t already, subscribe now to get a copy when it lands.

This researcher wants to replace your brain, little by little

A US agency pursuing moonshot health breakthroughs has hired a researcher advocating an extremely radical plan for defeating death.

His idea? Replace your body parts. All of them. Even your brain.

Jean Hébert, a new hire with the US Advanced Projects Agency for Health, is expected to lead a major new initiative around “functional brain tissue replacement,” the idea of adding youthful tissue to people’s brains.

The brain renewal concept could have applications such as treating stroke victims, who lose areas of brain function. But Hébert, a biologist at the Albert Einstein school of medicine, has most often proposed total brain replacement, along with replacing other parts of our anatomy, as the only plausible means of avoiding death from old age.

The strategy is not widely accepted, even among researchers in the aging field. But Hébert’s ideas appear to have gotten a huge endorsement from the US government. Read the full story.

—Antonio Regalado

What’s next for drones

Drones have been a mainstay technology among militaries, hobbyists, and first responders alike for more than a decade. No longer limited to small quadcopters with insufficient battery life, drones are aiding search and rescue efforts, reshaping wars in Ukraine and Gaza, and delivering time-sensitive packages of medical supplies. And billions of dollars are being plowed into building the next generation of fully autonomous systems.

These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people’s privacy for police to fly drones overhead at an event or protest? Who decides what level of drone autonomy is acceptable in a war zone?

Those questions are no longer hypothetical. Advancements in drone technology and sensors, falling prices, and easing regulations are making drones cheaper, faster, and more capable than ever. Here’s a look at four of the biggest changes coming to drone technology in the near future.

—James O’Donnell

This story is from MIT Technology Review’s What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Aging hits us in our 40s and 60s. But well-being doesn’t have to fall off a cliff.

—Jessica Hamzelou

You might feel like you’re on a slow, gradual decline, but, at the molecular level, you’re likely to be hit by two waves of changes, according to researchers at Stanford University. The first one comes in your 40s. Eek.

But it’s not as simple as it sounds. And midlife needn’t involve falling off a cliff in terms of your well-being. Let’s explore why.

This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.

Check out the new MIT Technology Review app

MIT Technology Review’s new app brings our trusted journalism and expert insights right to your fingertips, ensuring you’re always at the forefront of breakthrough innovations.

Our redesigned app provides a user-friendly interface with the ability to save favorite articles and search trending topics—and explore all our exclusive technology coverage along with our trending daily newsletters. Check it out!

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 California lawmakers have watered down the state’s AI safety bill
The changes make it more difficult for the state’s attorney general to sue AI firms. (TechCrunch)
+ The open source community is still concerned it’ll stifle innovation. (NYT $)

2 Mpox has been detected in Pakistan
Shortly after a case of the new variant was confirmed in Sweden. (Reuters)
+ The new mpox strain is far deadlier than its predecessor. (Wired $)
+ Covid cases are on the rise too. (Vox)

3 The world can’t kick its fossil fuel habit
And AI data centers are partly to blame. (WSJ $)
+ If your power bill is on the rise, you’re not alone. (Vox)
+ AI is an energy hog. This is what it means for climate change. (MIT Technology Review)

4 Ozempic patients are hacking their injection pens
While most are trying to save money, others are managing side effects. (The Atlantic $)
+ Weight-loss injections have taken over the internet. But what does this mean for people IRL? (MIT Technology Review)

5 Election influence campaigns are rife on XElon Musk’s war on bots seems to have had little effect. (Rest of World)
+ Eric Schmidt has a 6-point plan for fighting election misinformation. (MIT Technology Review)

6 Why deepfake detection tools failThey’re easily fooled by software tweaks and edits. (WP $)
+ Google is finally taking action to curb non-consensual deepfakes. (MIT Technology Review)

7 What’s next for psychedelic medicine?The FDA’s rejection of MDMA as treatment for PTSD is bad news for startup Lykos Therapeutics. (Wired $)
+ Why the FDA’s advisors decided against approving it. (MIT Technology Review)

8 US states are clamping down on tiny Japanese cars
The imported Kei vehicles are dwarfed by SUVs, which authorities argue is dangerous. (Ars Technica)

9 TikTok influencers are embracing a new demure mindset
It’s all down to creator Jools Lebron’s tongue-in-cheek clips. (The Guardian)

10 Magic: The Gathering is facing an AI reckoning
The card game’s distinctive artwork is ripe for AI aping. (Slate $)
+ This artist is dominating AI-generated art. And he’s not happy about it. (MIT Technology Review)

Quote of the day

“You drop out and you die immediately, or you partner with them and you probably just die slowly, because eventually they’re not going to need you either.”

—Joe Ragazzo, publisher of the news site Talking Points Memo, tells Bloomberg about the tricky choice publishers are facing between offering their content up to AI firms, or disappearing from Google search altogether.

The big story

The great chip crisis threatens the promise of Moore’s Law

June 2021

The world is facing an economically devastating shortage of microchips.

Production has also slowed for smartphones, laptops, video-game consoles, TVs, and even smart appliances, all because of the lack of cheap microchips. Their use is so essential and so widespread that some observers think the chip crisis could threaten the global economic recovery from the pandemic.

The spirit of Moore’s Law—the expectation that cheap, powerful chips will always be readily available—is now being threatened by something far more mundane: inflexible supply chains. Read the full story.

—Jeremy Hsu

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Today in pointless news: this man plugged 444 consoles into a single TV.
• If you’ve ever wondered what happens to all those tennis balls once Wimbledon is over, they’re repurposed into homes for harvest mice
• Happy birthday to the one and only queen of pop—66 today.
• Wait—young people don’t wear sneakers any more!?

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Since the heyday of radio, records, cassette tapes, and MP3 players, the branding of sound has evolved from broad genres like rock and hip-hop to “paranormal dark cabaret afternoon” and “synth space,” and streaming has become the default. Radio DJs have been replaced by artificial intelligence, and the ritual of discovering something new is neatly packaged in a 30-song playlist, refreshed weekly. The only rule in music streaming, as in any other industry these days, is personalization.

But what we’ve gained in convenience, we’ve lost in curiosity. Sure, our unlimited access lets us listen to Swedish tropical house or New Jersey hardcore, but this abundance of choice actually makes our listening experience less expansive or eclectic.

Most of us access music through streaming services: over 600 million of us worldwide, to be exact. And claiming over 30.5% of this population, nearly double the share of any other streaming service on the market, is Spotify. With its game-­changing release in 2015 of Discover Weekly—a generated playlist that tailors song selections to a user’s listening habits—Spotify presented personalization as the remedy to our overabundance of options.

But in efficiently delivering what people seem to want, it effectively eliminated choice and removed humanity from the entire music listening—and music discovery—experience. According to a 2022 report published by Distribution Strategy Group, at least 30% of songs streamed on Spotify are recommended by AI. The success of Discover Weekly has since inspired mood-dependent playlists that change throughout the day and psychic readings based on people’s listening habits. Other streaming platforms, like Apple Music and Amazon Music, have followed suit. All these takes on personalization share a common fault: The playlists too often resemble one another, filled with songs that offer different variants of the same sound.

Glenn McDonald, a former engineer at Spotify and the self-described “data alchemist” largely responsible for developing the company’s encyclopedia of genres, believes that while accessing new music is technically easy, many of us don’t do it—mainly because we’re not sure where to start looking.

As we grow accustomed to the convenience of shuffling a generated playlist, we forget that discovering music is an active exercise.

We expect too much from the algorithmFor Spotify, McDonald says, personalization begins with breaking down songs via a data-intelligence platform that was known as the Echo Nest before the company acquired it. Through a combination of signal processing and human listening by musicologists, Spotify assigns roughly 10 different attributes to songs (e.g., key signature, danceability) before grouping them into libraries. AI-powered programs then pull from these buckets of sound to generate the personalized playlists, with parameters tailored to the habits of each user. How Spotify categorizes music determines what is made visible to us. It also shapes which niches artists fit into and how much exposure they get.

McDonald sorts our listening habits into three concentric clusters: the stuff we listen to every day, the stuff that sounds like our stuff, and everything else we stumble upon. Spotify’s auto-generated playlists mostly keep to the first bucket, occasionally venturing into the second. The third is accidental. The service never offers anything strictly different.

Spotify thinks that even if we say we want to listen to something new, we always return to what’s familiar, McDonald explains. He argues that in practice, slipping a reggae track into a playlist of “bedroom pop” (a genre that mainly features dreamy melodies and hushed vocals) often makes for an uncomfortable listening experience: “If you’re given something new, it’s odd, in the same way being teleported to random spots around the world for three minutes at a time would not be a pleasant tourism experience.”

To construct the 6,291 microgenres in its database, McDonald says, Spotify uses social data—how listeners of the same artists sort those artists’ songs and who else they listen to. He clarifies that Spotify’s genres have no absolute boundaries but reflect a loose and dynamic consensus on how users listen to music. Small clusters of overlapping listening habits define these loose categories, while constant cross-pollination creates variations on them. “Everyone understood where the center of the village was, and the further out you went, the more subjective it became,” he says of the process as he remembers it. McDonald mapped this musical landscape on his personal site, Everynoise.com.

As we grow accustomed to the convenience of shuffling a generated playlist, we forget that discovering music is an active exercise.

Our respective listening habits, when considered together, form a dynamic network that reveals how we collectively understand music. It’s a shame that Spotify’s current usage confines us to isolated algorithmic bubbles.

Context and communityPersonalization, broadly speaking, has made navigating the internet’s infinite pool of content incredibly convenient. We’re served what we like, told what to buy, prompted on what to say. It’s no surprise we expect our music streaming apps to do the same. However, employing algorithms to optimize music discovery requires explicitly defining what we want, and the problem is that what we want could easily be shaped by what we encounter. Asking an algorithm to broaden our horizons is like having lunch with a friend who claims to be open to anything but vetoes everything you suggest. “Curiosity is an active mode,” McDonald says. It’s up to us to step outside our bubble.

Music enthusiasts are creating new ways to reinvigorate this sense of curiosity, building everything from competitive recommendation leagues to interactive music maps. Before streaming, discovering music was work that brought a distinctly emotional reward. “Back in college, I listened to whatever my friends were listening to,” recalls Zack O’Malley Greenburg, former senior music editor at Forbes. He describes exchanging CDs with friends, spending hours deciding which songs he liked and which he didn’t. Later, acquiring new music became an exercise in sorting through audio files on thumb drives and (illegally) downloading MP3s from questionable websites. Sharing music was a much more personal, peer-to-peer exercise, and making a mixtape for a crush was a substantial labor of love. Automated recommendation systems have replaced this social culture of sharing music. The anonymous playlists we opt into today may be edited and even shared, but the emotional stakes are much lower.

Because personally recommending songs revealed our taste, we had a vested interest in what we recommended. But the algorithm assumes no risk, simply offering what’s mathematically sound.

“What I think is missing in music streaming is why someone thinks I should like a certain song,” says Alex Keller, one of the cofounders of Music League, an online platform that allows people to submit songs to playlists that fit a certain theme. The platform has doubled its user base since last year, to roughly 130,000 monthly users.

Music League has built this loyal community by gamifying the experience of recommending music. Users can join public leagues or create private ones with themes ranging from “Best rap song” to “Horse crime.” Each league hosts multiple rounds, where participants compete by submitting and voting for songs they think best fit a prompt. A big part of the experience, Keller says, is the conversation around each submission. He describes how his experience of each song changes as users are pushed to defend their choices.

Unlike the myriad personalized Spotify playlists that instantaneously refresh on demand, leagues can be open for months at a time. There may be a long gap between receiving a prompt and submitting a song, or between listening and voting. People are encouraged not only to listen to songs from start to finish (an increasingly rare practice) but also to include liner notes alongside the songs they submit. Slowing down the process of music discovery can foster more purposeful listening.

“As an adult, music is in the background in your life,” Keller says. For him, the social focus of Music League puts it back in center stage. The collaborative recommendation process gives each song emotional weight and offers a refreshing departure from the streams of generated playlists we shuffle for ambience.

Similar to Music League is a private Facebook community called Oddly Specific Playlists, a group that connects users from all corners of the internet with playlists inspired by (as the name suggests) very specific things. With over 364,000 members, the group is flooded with requests daily; users post pieces of inspiration and attach a brief explanation of their interest in the theme. Others share relevant songs and offer personal anecdotes to color their recommendations. Requests like “Strong masculinity; healthy, not toxic; not misogynistic; bonus points for queerness” beget discussion. What could strong masculinity sound like? What does a healthy song entail?

Often, playlist requests have ventured into more somber subjects like heartbreak and grief. As users share deeply intimate stories about their relationships to specific songs, conversations develop and communities heal. The fact that members have likely never met can make the experience even more meaningful. Connecting with strangers around the world reveals the universality of even the most seemingly specific experiences and offers a unique form of validation. The discussions can also breathe new life into old songs; a request for songs prominently featuring the sound “oh,” from a member whose two-year-old was obsessed with the letter O, spotlighted “Oh! Darling’” by the Beatles.

Rather than challenging your tastes, algorithms only provide shuffled versions of what you already enjoy.

This focus on fostering organic human interaction is not new. Until 2017, Spotify actually had a chat feature, but it wasn’t used widely enough (and didn’t result in enough streams) to justify the resources required to maintain it. So instead, the company pivoted to optimizing personalization.

While Spotify’s platform evolved to make choosing music as easy as possible, the unpolished format of Oddly Specific Playlists has largely remained the same. Comments are still difficult to keep track of, and users must sift through mountains of posts to find relevant
recommendations. Despite the clunky experience, the community has been thriving since 2019.

“If a social network is any good, then it has to have some actual people putting new content into the ecosystem and organizing it in a coherent way—like someone making a hand-curated playlist,” says Kyle Chayka, a New Yorker staff writer and author of Filterworld: How Algorithms Flatten Culture. That’s just what the members of Oddly Specific Playlists do, even if the results can be hard to manage.

In his book, Chayka recounts the many hours he’s spent surfing music forums like AntsMarching.org and UFCK.org (fan sites dedicated to all things concerning the Dave Matthews Band and Pearl Jam, respectively), finding company with other posters who shared low-fidelity tapes from old concerts and fun facts about a band’s formation. These cultural rabbit holes, to Chayka, offer a form of “mutual learning” that helps us better understand what we’re consuming. If we know how an artist’s signature style came to be, for example, we’re more capable of intentionally shaping our tastes.

Slowing down with curation In Filterworld, Chayka also outlines how algorithms have taken the place of magazine editors and museum curators as gatekeepers of culture. “I think curation is a way to resist the flattening of the internet,” he says, though acknowledging that the term itself has been watered down over the past decade.

Chayka frames curation as intentional, arduous, and finite—characteristics he deems antithetical to our relationship with algorithms. Where a curator voices perspectives that welcome discourse and discomfort, algorithms are written in fear of offending. “When a human interprets a piece of art, it adds value rather than takes it away. An algorithm has no capacity to interpret,” he adds.

Before streaming, a magazine profile on an emerging artist or a blogger’s “Songs I’m listening to” column would put musicians on your radar, inspiring deep dives into their discography. Music publications like Blender, NME, and The Source, also had great influence, the latter notably discovering The Notorious B.I.G. and highlighting him in its “Unsigned Hype” column. But, as Greenburg explains, “streaming services remove a step.” Rather than challenging your tastes, algorithms only provide shuffled versions of what you already enjoy. Like the Soylent shakes popular in the mid-2010s for supposedly offering all the nutrients you need from a meal, these personal playlists may fulfill but can never satiate.

In Filterworld, Chayka offers independent radio DJs as an antidote to the algorithmic takeover. The vaguely physical act of tuning into a radio station, like entering a concert hall, restores a tactile quality to our experience of music. When there’s a voice behind the selection of songs, we’re more likely to pay attention, Chayka insists. He describes how these DJs “utilize all of their knowledge, expertise, and experience in order to determine what to show us and how to do it.”

“When a human interprets a piece of art, it adds value rather than takes it away. An algorithm has no capacity to interpret.”

Kyle Chayka, the New Yorker

The Hong Kong–based musician known as Cehryl, who hosts the show Mystery Train on Eaton Radio, structures her shows around narratives. “I think about my shows in the same way I think about a performance,” she says. “There’s an emotional arc.” She puts her tastes first, hoping to express a unique point of view that will bring something new to her listeners.

In a world of on-demand music, the real-time format of independent radio mandates a specific sequence of uninterrupted listening. Without skips, shuffles, or the ability to pause, it gives curators the opportunity to push their listeners’ boundaries.

Creating with “algorithmic anxiety”To Cehryl, a big part of being a musician today is grappling with the existential question of whether to make music for the algorithm. Since the popularization of streaming (and the rise of TikTok), the average length of a song has decreased from four minutes to roughly three. Artists are encouraged to put out singles or EPs instead of releasing concept albums. And in 2023, Spotify launched the Preview function, a TikTok-esque infinite-scroll music feed that showcases the “best” few seconds of each song with every swipe. The algorithm rewards relevance and instant gratification. “No long songs. No patient, drawn-out songs. You want the hook by 15 seconds in, if not earlier,” Cehryl says.

Experiencing what Chayka calls “algorithmic anxiety,” Cehryl describes a need to feed the algorithm’s perception of her: “I have often been playlisted as bedroom pop. But I don’t think I make bedroom pop.” For artists, Spotify’s genre breakdowns play a complicated role in their creative process.

Spotify’s algorithm offers loose categorizations to identify emerging genres or remodel familiar ones, but the platform’s promotion of broader, more recognizable genres makes some artists feel pigeonholed and pressures others to conform. Fitting into Spotify’s categories increases an artist’s chances of going viral on the platform, even if each stream yields only $0.003 for the creator.

Alex Antenna, who has created a website called Unchartify to offer a more manual way of navigating Spotify’s database, attributes these pigeonholes to Spotify’s push for personalization. He built his site to bypass the plethora of “made for you” playlists and highlight lesser-known corners of Spotify’s database.

“Spotify’s music database has a very rich set of various parameters, markup, and categories to classify music in a very detailed way. This is simply not exposed in the official app,” he says. He believes that even though it has a sophisticated way of sorting music, Spotify intentionally oversimplifies: Its library offers mainly personalized playlists drawing on broad categories like “metal” or “party,” many of which feature mostly “popular artists or songs you heard 1,000 times.”

Antenna points out that beyond genres such as bedroom pop or indie folk, Spotify offers a plethora of microgenres (such as “reminimal” and “sky room”) that are accessible only by name through its API. He hopes that by surfacing genres that more accurately represent an artist’s sound, a system as granular as Unchartify can combat algorithmic anxiety.

Unchartify reorganizes Spotify’s database by sorting all genres into alphabetical order—something unheard-of in today’s world of engagement optimization—and mapping them so that each album is a node connecting to a list of similar albums. Unlike Spotify’s “Fans also like” feature, which recommends similar artists without suggesting where their similarity lies, Unchartify offers a precise picture of where an album sits musically in relation to others.

Unless specifically asked, Unchartify doesn’t try to guess what you’re looking for. Instead, it gives you the tools to surf Spotify’s database systematically, as you might sift through archives in a public library. Antenna’s position reveals an important source of tension in the world of on-demand music: Making the abundance of content online digestible requires simplification, but simplification often forgoes nuance.

Beating the algorithmGoing a step beyond Antenna’s archaic decision to list genres alphabetically is Radiooooo, a self-described musical time machine that randomizes the discovery process by eliminating genre entirely.

Founded in 2012 by a group of four DJs, Radiooooo curates a selection of songs for each decade dating back to the 1900s for each country across the globe. It prompts users to select music by time periods and geographic locations rather than genres or artists—discarding any semblance of our current streaming experience and inspiring a new way of thinking about music. Radiooooo also tacks on a social component by crediting members who’ve discovered the track, joining communities like Music League and Oddly Specific Playlists in encouraging a form of crowdsourced recommendation that invites conversation and disagreement—a far cry from Spotify’s vision of optimized, unimpeded listening.

Perhaps the only way to escape our algorithmic bubbles is by building community. When we welcome diverse patterns of music consumption, we’re challenged to consider music from different perspectives, the same way independent radio stations curate to tell a story rather than cater to a demographic. There’s nothing to optimize in a community, and in turn, nothing to oversimplify.

Despite functionally contradicting Spotify’s philosophy, platforms like Radiooooo, Music League, Oddly Specific Playlists, and independent radio all complement the use of such platforms. They act as a springboard for our process of discovery, helping us step past Spotify’s insistence on personalization by directing us where to look and, most important, making it fun.

McDonald likens the functions of Spotify to Google Maps. “Google Maps doesn’t do the exploration for me, but it’s helpful if I go somewhere,” he says. Rather than taking us on guided tours, it provides the tools for us to navigate somewhere new. Much as it shows us what’s nearby and how to get there, and flags notable landmarks others have visited, Spotify helps us access most music, lists global listening trends, and introduces us to artists similar to those we already know. But it’s communities that help us home in on a destination Spotify can help us explore.

Rage against the machineFour music discovery services to help you explore beyond Spotify’s AI-generated playlistsMusic League is an online platform that allows users to submit songs that fit a certain theme.Oddly Specific Playlists recommends– you guessed it– playlists inspired by oddly specific things.

Unchartify provides a more manual navigation through Spotify’s database.Radiooooo ditches genres altogether and prompts users to select music by time period and geographic location.Tiffany Ng is a freelance writer exploring the relationship between art, tech, and culture.

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MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.**

Drones have been a mainstay technology among militaries, hobbyists, and first responders alike for more than a decade, and in that time the range available has skyrocketed. No longer limited to small quadcopters with insufficient battery life, drones are aiding search and rescue efforts, reshaping wars in Ukraine and Gaza, and delivering time-sensitive packages of medical supplies. And billions of dollars are being plowed into building the next generation of fully autonomous systems.

These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people’s privacy for police to fly drones overhead at an event or protest? Who decides what level of drone autonomy is acceptable in a war zone?

Those questions are no longer hypothetical. Advancements in drone technology and sensors, falling prices, and easing regulations are making drones cheaper, faster, and more capable than ever. Here’s a look at four of the biggest changes coming to drone technology in the near future.

Police drone fleetsToday more than 1,500 US police departments have drone programs, according to tracking conducted by the Atlas of Surveillance. Trained police pilots use drones for search and rescue operations, monitoring events and crowds, and other purposes. The Scottsdale Police Department in Arizona, for example, successfully used a drone to locate a lost elderly man with dementia, says Rich Slavin, Scottsdale’s assistant chief of police. He says the department has had useful but limited experiences with drones to date, but its pilots have often been hamstrung by the “line of sight” rule from the Federal Aviation Administration (FAA). The rule stipulates that pilots must be able to see their drones at all times, which severely limits the drone’s range.

Soon, that will change. On a rooftop somewhere in the city, Scottsdale police will in the coming months install a new police drone capable of autonomous takeoff, flight, and landing. Slavin says the department is seeking a waiver from the FAA to be able to fly its drone past the line of sight. (Hundreds of police agencies have received a waiver from the FAA since the first was granted in 2019.) The drone, which can fly up to 57 miles per hour, will go on missions as far as three miles from its docking station, and the department says it will be used for things like tracking suspects or providing a visual feed of an officer at a traffic stop who is waiting for backup.

“The FAA has been much more progressive in how we’re moving into this space,” Slavin says. That could mean that around the country, the sight (and sound) of a police drone soaring overhead will become much more common.

The Scottsdale department says the drone, which it is purchasing from Aerodome, will kick off its drone-as-first-responder program and will play a role in the department’s new “real-time crime center.” These sorts of centers are becoming increasingly common in US policing, and allow cities to connect cameras, license plate readers, drones, and other monitoring methods to track situations on the fly. The rise of the centers, and their associated reliance on drones, has drawn criticism from privacy advocates who say they conduct a great deal of surveillance with little transparency about how footage from drones and other sources will be used or shared.

In 2019, the police department in Chula Vista, California, was the first to receive a waiver from the FAA to fly beyond line of sight. The program sparked criticism from members of the community who alleged the department was not transparent about the footage it collected or how it would be used.

Jay Stanley, a senior policy analyst at the American Civil Liberties Union’s Speech, Privacy, and Technology Project, says the waivers exacerbate existing privacy issues related to drones. If the FAA continues to grant them, police departments will be able to cover far more of a city with drones than ever, all while the legal landscape is murky about whether this would constitute an invasion of privacy.

“If there’s an accumulation of different uses of this technology, we’re going to end up in a world where from the moment you step out of your front door, you’re going to feel as though you’re under the constant eye of law enforcement from the sky,” he says. “It may have some real benefits, but it is also in dire need of strong checks and balances.”

Scottsdale police say the drone could be used in a variety of scenarios, such as responding to a burglary in progress or tracking a driver with suspected connection to a kidnapping. But the real benefit, Slavin says, will come from pairing it with other existing technologies, like automatic license plate readers and hundreds of cameras placed around the city. “It can get to places very, very quickly,” he says. “It gives us real-time intelligence and helps us respond faster and smarter.”

While police departments might indeed benefit from drones in those situations, Stanley says the ACLU has found that many deploy them for far more ordinary cases, like reports of a kid throwing a ball against a garage or of “suspicious persons” in an area.

“It raises the question about whether these programs will just end up being another way in which vulnerable communities are over-policed and nickeled and dimed by law enforcement agencies coming down on people for all kinds of minor transgressions,” he says.

Drone deliveries, againPerhaps no drone technology is more overhyped than home deliveries. For years, tech companies have teased futuristic renderings of a drone dropping off a package on your doorstep just hours after you ordered it. But they’ve never managed to expand them much beyond small-scale pilot projects, at least in the US, again largely due to the FAA’s line of sight rules.

But this year, regulatory changes are coming. Like police departments, Amazon’s Prime Air program was previously limited to flying its drones within the pilot’s line of sight. That’s because drone pilots don’t have radar, air traffic controllers, or any of the other systems commercial flight relies on to monitor airways and keep them safe. To compensate, Amazon spent years developing an onboard system that would allow its drones to detect nearby objects and avoid collisions. The company says it showed the FAA in demonstrations that its drones could fly safely in the same airspace as helicopters, planes, and hot air balloons.

In May, Amazon announced the FAA had granted the company a waiver and permission to expand operations in Texas, more than a decade after the Prime Air project started. And in July, the FAA cleared one more roadblock by allowing two companies—Zipline as well as Google’s Wing Aviation—to fly in the same airspace simultaneously without the need for visual observers.

While all this means your chances of receiving a package via drone have ticked up ever so slightly, the more compelling use case might be medical deliveries. Shakiba Enayati, an assistant professor of supply chains at the University of Missouri–St. Louis, has spent years researching how drones could conduct last-mile deliveries of vaccines, antivenom, organs, and blood in remote places. She says her studies have found drones to be game changers for getting medical supplies to underserved populations, and if the FAA extends these regulatory changes, it could have a real impact.

That’s especially true in the steps leading up to an organ transplant, she says. Before an organ can be transmitted to a recipient, a number of blood tests must be sent back-and-forth to make sure the recipient can accept it, which takes a time if the blood is being transferred by car or even helicopter. “In these cases, the clock is ticking,” Enayati says. If drones were allowed to be used in this step at scale, it would be a significant improvement.

“If the technology is supporting the needs of organ delivery, it’s going to make a big change in such an important arena,” she says.

That development could come sooner than using drones for delivery of the actual organs, which have to be transported under very tightly controlled conditions to preserve them.

Domesticating the drone supply chainSigned into law last December, the American Security Drone Act bars federal agencies from buying drones from countries thought to pose a threat to US national security, such as Russia and China. That’s significant. China is the undisputed leader when it comes to manufacturing drones and drone parts, with over 90% of law enforcement drones in the US made by Shenzhen-based DJI, and many drones used by both sides of the war in Ukraine are made by Chinese companies.

The American Security Drone Act is part of an effort to curb that reliance on China. (Meanwhile, China is stepping up export restrictions on drones with military uses.) As part of the act, the US Department of Defense’s Defense Innovation Unit has created the Blue UAS Cleared List, a list of drones and parts the agency has investigated and approved for purchase. The list applies to federal agencies as well as programs that receive federal funding, which often means state police departments or other non-federal agencies.

Since the US is set to spend such significant sums on drones—with $1 billion earmarked for the Department of Defense’s Replicator initiative alone—getting on the Blue List is a big deal. It means those federal agencies can make large purchases with little red tape.

Allan Evans, CEO of US-based drone part maker Unusual Machine, says the list has sparked a significant rush of drone companies attempting to conform to the US standards. His company manufactures a first-person view flight controller that he hopes will become the first of its kind to be approved for the Blue List.

The American Security Drone Act is unlikely to affect private purchases in the US of drones used by videographers, drone racers, or hobbyists, which will overwhelmingly still be made by China-based companies like DJI. That means any US-based drone companies, at least in the short term, will only survive by catering to the US defense market.

“Basically any US company that isn’t willing to have ancillary involvement in defense work will lose,” Evans says.

The coming months will show the law’s true impact: Because the US fiscal year ends in September, Evans says he expects to see a host of agencies spending their use-it-or-lose-it funding on US-made drones and drone components in the next month. “That will indicate whether the marketplace is real or not, and how much money is actually being put toward it,” he says.

Autonomous weapons in UkraineThe drone war in Ukraine has largely been one of attrition. Drones have been used extensively for surveying damage, finding and tracking targets, or dropping weapons since the war began, but on average these quadcopter drones last just three flights before being shot down or rendered unnavigable by GPS jamming. As a result, both Ukraine and Russia prioritized accumulating high volumes of drones with the expectation that they wouldn’t last long in battle.

Now they’re having to rethink that approach, according to Andriy Dovbenko, founder of the UK-Ukraine Tech Exchange, a nonprofit that helps startups involved in Ukraine’s war effort and eventual reconstruction raise capital. While working with drone makers in Ukraine, he says, he has seen the demand for technology shift from big shipments of simple commercial drones to a pressing need for drones that can navigate autonomously in an environment where GPS has been jammed. With 70% of the front lines suffering from jamming, according to Dovbenko, both Russian and Ukrainian drone investment is now focused on autonomous systems.

That’s no small feat. Drone pilots usually rely on video feeds from the drone as well as GPS technology, neither of which is available in a jammed environment. Instead, autonomous drones operate with various types of sensors like LiDAR to navigate, though this can be tricky in fog or other inclement weather. Autonomous drones are a new and rapidly changing technology, still being tested by US-based companies like Shield AI. The evolving war in Ukraine is raising the stakes and the pressure to deploy affordable and reliable autonomous drones.

The transition toward autonomous weapons also raises serious yet largely unanswered questions about how much humans should be taken out of the loop in decision-making. As the war rages on and the need for more capable weaponry rises, Ukraine will likely be the testing ground for if and how the moral line is drawn. But Dovbenko says stopping to find that line during an ongoing war is impossible.

“There is a moral question about how much autonomy you can give to the killing machine,” Dovbenko says. “This question is not being asked right now in Ukraine because it’s more of a matter of survival.”

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A US agency pursuing moonshot health breakthroughs has hired a researcher advocating an extremely radical plan for defeating death.

His idea? Replace your body parts. All of them. Even your brain.

Jean Hébert, a new hire with the US Advanced Projects Agency for Health (ARPA-H), is expected to lead a major new initiative around “functional brain tissue replacement,” the idea of adding youthful tissue to people’s brains.

President Joe Biden created ARPA-H in 2022, as an agency within the Department of Health and Human Services, to pursue what he called “bold, urgent innovation” with transformative potential.

The brain renewal concept could have applications such as treating stroke victims, who lose areas of brain function. But Hébert, a biologist at the Albert Einstein school of medicine, has most often proposed total brain replacement, along with replacing other parts of our anatomy, as the only plausible means of avoiding death from old age.

As he described in his 2020 book, Replacing Aging, Hébert thinks that to live indefinitely people must find a way to substitute all their body parts with young ones, much like a high-mileage car is kept going with new struts and spark plugs.

The idea has a halo of plausibility since there are already liver transplants and titanium hips, artificial corneas and substitute heart valves. The trickiest part is your brain. That ages, too, shrinking dramatically in old age. But you don’t want to swap it out for another—because it is you.

And that’s where Hébert’s research comes in. He’s been exploring ways to “progressively” replace a brain by adding bits of youthful tissue made in a lab. The process would have to be done slowly enough, in steps, that your brain could adapt, relocating memories and your self-identity.

During a visit this spring to his lab at Albert Einstein, Hébert showed MIT Technology Review how he has been carrying out initial experiments with mice, removing small sections of their brains and injecting slurries of embryonic cells. It’s a step toward proving whether such youthful tissue can survive and take over important functions.

To be sure, the strategy is not widely accepted, even among researchers in the aging field. “On the surface it sounds completely insane, but I was surprised how good a case he could make for it,” says Matthew Scholz, CEO of aging research company Oisín Biotechnologies, who met with Hébert this year.

Scholz is still skeptical though. “A new brain is not going to be a popular item,” he says. “The surgical element of it is going to be very severe, no matter how you slice it.”

Now, though, Hébert’s ideas appear to have gotten a huge endorsement from the US government. Hébert told MIT Technology Review that he had proposed a $110 million project to ARPA-H to prove his ideas in monkeys and other animals, and that the government “didn’t blink” at the figure.

ARPA-H confirmed this week that it had hired Hébert as a program manager.

The agency, modeled on DARPA, the Department of Defense organization that developed stealth fighters, gives managers unprecedented leeway in awarding contracts to develop novel technologies. Among its first programs are efforts to develop at-home cancer tests and cure blindness with eye transplants.

“I just prefer life over this slow degradation into nonexistence that biology has planned for all of us.”

It may be several months before details of the new project are announced, and it’s possible that ARPA-H will establish more conventional goals like treating stroke victims and Alzheimer’s patients, whose brains are damaged, rather than the more radical idea of extreme life extension.

“If it can work, forget aging; it would be useful for all kinds of neurodegenerative disease,” says Justin Rebo, a longevity scientist and entrepreneur.

But defeating death is Hébert’s stated aim. “I was a weird kid and when I found out that we all fall apart and die, I was like, ‘Why is everybody okay with this?’ And that has pretty much guided everything I do,” he says. “I just prefer life over this slow degradation into nonexistence that biology has planned for all of us.”

Hébert, now 58, also recalls when he began thinking that the human form might not be set in stone. It was upon seeing the 1973 movie Westworld, in which the gun-slinging villain, played by Yul Brynner, turns out to be an android. “That really stuck with me,” Hébert said.

Lately, Hébert has become something of a star figure among immortalists, a fringe community devoted to never dying. That’s because he’s an established scientist who is willing to propose extreme steps to avoid death. “A lot of people want radical life extension without a radical approach. People want to take a pill, and that’s not going to happen,” says Kai Micah Mills, who runs a company, Cryopets, developing ways to deep-freeze cats and dogs for future reanimation.

The reason pharmaceuticals won’t ever stop aging, Hébert says, is that time affects all of our organs and cells and even degrades substances such as elastin, one of the molecular glues that holds our bodies together. So even if, say, gene therapy could rejuvenate the DNA inside cells, a concept some companies are exploring, Hébert believes we’re still doomed as the scaffolding around them comes undone.

One organization promoting Hébert’s ideas is the Longevity Biotech Fellowship (LBF), a self-described group of “hardcore” life extension enthusiasts, which this year published a technical roadmap for defeating aging altogether. In it, they used data from Hébert’s ARPA-H proposal to argue in favor of extending life with gradual brain replacement for elderly subjects, as well as transplant of their heads onto the bodies of “non-sentient” human clones, raised to lack a functioning brain of their own, a procedure they referred to as “body transplant.”

Such a startling feat would involve several technologies that don’t yet exist, including a means to attach a transplanted head to a spinal cord. Even so, the group rates “replacement” as the most likely way to conquer death, claiming it would take only 10 years and $3.6 billion to demonstrate.

“It doesn’t require you to understand aging,” says Mark Hamalainen, co-founder of the research and education group. “That is why Jean’s work is interesting.”

Hébert’s connections to such far-out concepts (he serves as a mentor in LBF’s training sessions) could make him an edgy choice for ARPA-H, a young agency whose budget is $1.5 billion a year.

For instance, Hebert recently said on a podcast with Hamalainen that human fetuses might be used as a potential source of life-extending parts for elderly people. That would be ethical to do, Hébert said during the program, if the fetus is young enough that there “are no neurons, no sentience, and no person.” And according to a meeting agenda viewed by MIT Technology Review, Hébert was also a featured speaker at an online pitch session held last year on full “body replacement,” which included biohackers and an expert in primate cloning.

Hébert declined to describe the session, which he said was not recorded “out of respect for those who preferred discretion.” But he’s in favor of growing non-sentient human bodies. “I am in conversation with all these groups because, you know, not only is my brain slowly deteriorating, but so is the rest of my body,” says Hébert. “I’m going to need other body parts as well.”

The focus of Hébert’s own scientific work is the neocortex, the outer part of the brain that looks like a pile of extra-thick noodles and which houses most of our senses, reasoning, and memory. The neocortex is “arguably the most important part of who we are as individuals,” says Hébert, as well as “maybe the most complex structure in the world.”

There are two reasons he believes the neocortex could be replaced, albeit only slowly. The first is evidence from rare cases of benign brain tumors, like a man described in the medical literature who developed a growth the size of an orange. Yet because it grew very slowly, the man’s brain was able to adjust, shifting memories elsewhere, and his behavior and speech never seemed to change—even when the tumor was removed.

That’s proof, Hébert thinks, that replacing the neocortex little by little could be achieved “without losing the information encoded in it” such as a person’s self-identity.

The second source of hope, he says, is experiments showing that fetal-stage cells can survive, and even function, when transplanted into the brains of adults. For instance, medical tests underway are showing that young neurons can integrate into the brains of people who have epilepsy and stop their seizures.

“It was these two things together—the plastic nature of brains and the ability to add new tissue—that, to me, were like, ‘Ah, now there has got to be a way,’” says Hébert.

To design the youthful bits of neocortex, Hébert has been studying brains of aborted human fetuses 5 to 8 weeks of age.

One challenge ahead is how to manufacture the replacement brain bits, or what Hebert has called “facsimiles” of neocortical tissue. During a visit to his lab at Albert Einstein, Hébert described plans to manually assemble chunks of youthful brain tissue using stem cells. These parts, he says, would not be fully developed, but instead be similar to what’s found in a still-developing fetal brain. That way, upon transplant, they’d be able to finish maturing, integrate into your brain, and be “ready to absorb and learn your information.”

To design the youthful bits of neocortex, Hébert has been studying brains of aborted human fetuses 5 to 8 weeks of age. He’s been measuring what cells are present, and in what numbers and locations, to try to guide the manufacture of similar structures in the lab.

“What we’re engineering is a fetal-like neocortical tissue that has all the cell types and structure needed to develop into normal tissue on its own,” says Hébert.

Part of the work has been carried out by a startup company, BE Therapeutics (it stands for Brain Engineering), located in a suite on Einstein’s campus and which is funded by Apollo Health Ventures, VitaDAO, and with contributions from a New York State development fund. The company had only two employees when MIT Technology Review visited this spring, and the its future is uncertain, says Hébert, now that he’s joining ARPA-H and closing his lab at Einstein.

Because it’s often challenging to manufacture even a single cell type from stem cells, making a facsimile of the neocortex involving a dozen cell types isn’t an easy project. In fact, it’s just one of several scientific problems standing between you and a younger brain, some of which might never have practical solutions. “There is a saying in engineering. You are allowed one miracle, but if you need more than one, find another plan,” says Scholz.

Maybe the crucial unknown is whether young bits of neocortex will ever correctly function inside an elderly person’s brain, for example by establishing connections or storing and sending electro-chemical information. Despite evidence the brain can incorporate individual transplanted cells, that’s never been robustly proven for larger bits of tissue, says Rusty Gage, a biologist at the Salk Institute in La Jolla, Calif., and who is considered a pioneer of neural transplants. He says researchers for years have tried to transplant larger parts of fetal animal brains into adult animals, but with inconclusive results. “If it worked, we’d all be doing more of it,” he says.

The problem, says Gage, isn’t whether the tissue can survive, but whether it can participate in the workings of an existing brain. “I am not dissing his hypothesis. But that’s all it is,” says Gage. “Yes, fetal or embryonic tissue can mature in the adult brain. But whether it replaces the function of the dysfunctional area is an experiment he needs to do, if he wants to convince the world he has actually replaced an aged section with a new section.”

In his new role at ARPA-H, it’s expected that Hébert will have a large budget to fund scientists to try and prove his ideas can work. He agrees it won’t be easy. “We’re, you know, a couple steps away from reversing brain aging,” says Hébert. “A couple of big steps away, I should say.”

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This week I came across research that suggests aging hits us in waves. You might feel like you’re on a slow, gradual decline, but, at the molecular level, you’re likely to be hit by two waves of changes, according to the scientists behind the work. The first one comes in your 40s. Eek.

For the study, Michael Snyder at Stanford University and his colleagues collected a vast amount of biological data from 108 volunteers aged 25 to 75, all of whom were living in California. Their approach was to gather as much information as they could and look for age-related patterns afterward.

This approach can lead to some startling revelations, including the one about the impacts of age on 40-year-olds (who, I was horrified to learn this week, are generally considered “middle-aged”). It can help us answer some big questions about aging, and even potentially help us find drugs to counter some of the most unpleasant aspects of the process.

But it’s not as simple as it sounds. And midlife needn’t involve falling off a cliff in terms of your well-being. Let’s explore why.

First, the study, which was published in the journal Nature Aging on August 14. Snyder and his colleagues collected a real trove of data on their volunteers, including on gene expression, proteins, metabolites, and various other chemical markers. The team also swabbed volunteers’ skin, stool, mouths, and noses to get an idea of the microbial communities that might be living there.

Each volunteer gave up these samples every few months for a median period of 1.7 years, and the team ended up with a total of 5,405 samples, which included over 135,000 biological features. “The idea is to get a very complete picture of people’s health,” says Snyder.

When he and his colleagues analyzed the data, they found that around 7% of the molecules and microbes measured changes gradually over time, in a linear way. On the other hand, 81% of them changed at specific life stages. There seem to be two that are particularly important: one at around the age of 44, and another around the age of 60.

Some of the dramatic changes at age 60 seem to be linked to kidney and heart function, and diseases like atherosclerosis, which narrows the arteries. That makes sense, given that our risks of developing cardiovascular diseases increase dramatically as we age—around 40% of 40- to 59-year-olds have such disorders, and this figure rises to 75% for 60- to 79-year-olds.

But the changes that occur around the age of 40 came as a surprise to Snyder. He says that, on reflection, they make intuitive sense. Many of us start to feel a bit creakier once we hit 40, and it can take longer to recover from injuries, for example.

Other changes suggest that our ability to metabolize lipids and alcohol shifts when we reach our 40s, though it’s hard to say why, for a few reasons.

First, it’s not clear if a change in alcohol metabolism, for example, means that we are less able to break down alcohol, or if people are just consuming less of it when they’re older.

This gets us to a central question about aging: Is it an inbuilt program that sets us on a course of deterioration, or is it merely a consequence of living?

We don’t have an answer to that one, yet. It’s probably a combination of both. Our bodies are exposed to various environmental stressors over time. But also, as our cells age, they are less able to divide, and clear out the molecular garbage they accumulate over time.

It’s also hard to tell what’s happening in this study, because the research team didn’t measure more physiological markers of aging, such as muscle strength or frailty, says Colin Selman, a biogerontologist at the University of Glasgow in Scotland.

There’s another, perhaps less scientific, question that comes to mind. How worried should we be about these kinds of molecular changes? I’m approaching 40—should I panic? I asked Sara Hägg, who studies the molecular epidemiology of aging at the Karolinska Institute in Stockholm, Sweden. “No,” was her immediate answer.

While Snyder’s team collected a vast amount of data, it was from a relatively small number of people over a relatively short period of time. None of them were tracked for the two or three decades you’d need to see the two waves of molecular changes occur in a person.

“This is an observational study, and they compare different people,” Hägg told me. “There is absolutely no evidence that this is going to happen to you.” After all, there’s a lot that can happen in a person’s life over 20 or 30 years. They might take up a sport. They might quit smoking or stop eating meat.

However, the findings do support the idea that aging is not a linear process.

“People have always suggested that you’re on this decline in your life from [around the age of] 40, depressingly,” says Selman. “But it’s not quite as simple as that.”

Snyder hopes that studies like his will help reveal potential new targets for therapies that help counteract some of the harmful molecular shifts associated with aging. “People’s healthspan is 11 to 15 years shorter than their lifespan,” he says. “Ideally you’d want to live for as long as possible [in good health], and then die.”

We don’t have any such drugs yet. For now, it all comes down to the age-old advice about eating well, sleeping well, getting enough exercise, and avoiding the big no-nos like smoking and alcohol.

I happened to speak to Selman at the end of what had been a particularly difficult day, and I confessed that I was looking forward to enjoying an evening glass of wine. That’s despite the fact that research suggests that there is “no safe level” of alcohol consumption.

“A little bit of alcohol is actually quite nice,” Selman agreed. He told me about an experience he’d had once at a conference on aging. Some of the attendees were members of a society that practiced caloric restriction—the idea being that cutting your calories can boost your lifespan (we don’t yet know if this works for people). “There was a big banquet… and these people all had little scales, and were weighing their salads on the scales,” he told me. “To me, that seems like a rather miserable way to live your life.”

I’m all for finding balance between healthy lifestyle choices and those that bring me joy. And it’s worth remembering that no amount of deprivation is going to radically extend our lifespans. As Selman puts it: “We can do certain things, but ultimately, when your time’s up, your time’s up.”

Now read the rest of the CheckupRead more from MIT Technology Review’s archiveWe don’t yet have a drug that targets aging. But that hasn’t stopped a bunch of longevity clinics from cropping up, offering a range of purported healthspan-extending services for the mega-rich. Now, they’re on a quest to legitimize longevity medicine.

Speaking of the uber wealthy, I also tagged along to an event for longevity enthusiasts ready to pump millions of dollars into the search for an anti-aging therapy. It was a fascinating, albeit slightly strange, experience.

There are plenty of potential rejuvenation strategies being explored right now. But the one that has received some of the most attention—and the most investment—is cellular reprogramming. My colleague Antonio Regalado looked at the promise of the field in this feature.

Scientists are working on new ways to measure how old a person is. Not just the number of birthdays they’ve had, but how aged or close to death they are. I took one of these biological aging tests. And I wasn’t all that pleased with the result.

Is there a limit to human life? Is old age a disease? Find out in the Mortality issue of MIT Technology Review’s magazine.

You can of course read all of these stories and many more on our new app, which can be downloaded here (for Android users) or here (for Apple users).

From around the webMpox, the disease that has been surging in the Democratic Republic of the Congo and nearby countries, now constitutes a public health emergency of international concern, according to the World Health Organization.

“The detection and rapid spread of a new clade [subgroup] of mpox in Eastern DRC, its detection in neighboring countries that had not previously reported mpox, and the potential for further spread within Africa and beyond is very worrying,” WHO director general Tedros Adhanom Ghebreyesus said in a briefing shared on X. “It’s clear that a coordinated international response is essential to stop these outbreaks and save lives.” (WHO)

Prosthetic limbs are often branded with company logos. For users of the technology, it can feel like a tattoo you didn’t ask for. (The Atlantic)

A testing facility in India submitted fraudulent data for more than 400 drugs to the FDA. But these drugs have not been withdrawn from the US market. That needs to be remedied, says the founder and president of a nonprofit focused on researching drug side effects. (STAT)

Antibiotics can impact our gut microbiomes. But the antibiotics given to people who undergo c-sections don’t have much of an impact on the baby’s microbiome. The way the baby is fed seems to be much more influential. (Cell Host & Microbe)

When unexpected infectious diseases show up in people, it’s not just physicians that are crucial. Veterinarian “disease detectives” can play a vital role in tracking how infections pass from animals to people, and the other way around. (New Yorker)

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

DHS plans to collect biometric data from migrant children “down to the infant”

The US Department of Homeland Security (DHS) plans to collect and analyze photos of the faces of migrant children at the border in a bid to improve facial recognition technology, MIT Technology Review can reveal.

The technology has traditionally not been applied to children, largely because training data sets of real children’s faces are few and far between, and consist of either low-quality images drawn from the internet or small sample sizes with little diversity. Such limitations reflect the significant sensitivities regarding privacy and consent when it comes to minors.

In practice, the new DHS plan could effectively solve that problem. But, beyond concerns about privacy, transparency, and accountability, some experts also worry about testing and developing new technologies using data from a population that has little recourse to provide—or withhold—consent. Read the full story.

—Eileen Guo

What Japan’s “megaquake” warning really tells us

On August 8, at 16:42 local time, a magnitude-7.1 earthquake shook southern Japan. The temblor, originating off the shores of mainland island of Kyūshū, was felt by nearly a million people across the region, and initially, the threat of a tsunami emerged. But only a diminutive wave swept ashore, buildings remained upright, and nobody died. The crisis was over as quickly as it began.

But then, something new happened. The Japan Meteorological Agency, a government organization, issued a ‘megaquake advisory’ for the first time. It was in part issued because it is possible that the magnitude-7.1 quake is a foreshock – a precursory quake – to a far larger one, a tsunami-making monster that could kill a quarter of a million people.

The good news, for now, is that scientists think it is very unlikely that that magnitude-7.1 quake is a prelude to a cataclysm. But the slim possibility remains that it was a foreshock to something considerably worse. Read the full story.

—Robin George Andrews

This story is part of MIT Technology Review Explains: our series helping you understand what’s coming next.You can read more here.

The US government is still spending big on climate

Friday marks two years since the US signed the landmark Inflation Reduction Act (IRA) into law. In that time we’ve seen an influx of investment from the federal government and private businesses alike.

The government has already spent hundreds of billions of dollars, and there’s much more to come. And this money is starting to make a big difference in the climate tech sector. But where is it all going? Read our story to find out.

—Casey Crownhart

This story is from The Spark, our weekly newsletter covering climate and energy technologies. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Mpox is spreading rapidly across African countries
The World Health Organization has declared it a global health emergency for the second time in two years. (NYT $)
+ Cases and deaths are rising across east and central African countries. (Vox)
+ This type of mpox, known as Clade 1, is far deadlier than the previous version. (BBC)

2 A brain implant helped a man with ALS to speak againYears after the disease robbed him of that ability. (Reuters)
+ An ALS patient set a record for communicating via a brain implant. (MIT Technology Review)

3 X’s AI image generator appears to have few filtersIt’ll generate pictures of Barack Obama doing cocaine, for example. (NY Mag $)
+ It does, however, refuse to generate fully nude images. (The Guardian)
+ Text-to-image AI models can be tricked into generating disturbing images. (MIT Technology Review)

4 Big Tech’s energy usage is skyrocketing
But how huge firms disclose their emissions is a bone of contention. (FT $)
+ Google, Amazon and the problem with Big Tech’s climate claims. (MIT Technology Review)

5 Meta has shut down a major misinformation tracking tool
Less than three months before the US election. (NPR)+ Meta’s justification? CrowdTangle was too difficult to maintain. (Bloomberg $)

6 Apple has started work on a tabletop robot
Its former car team has pivoted to building a smart home command center. (Bloomberg $)

7 Climate change is a gift to harmful invasive plantsSleeper species can thrive in warmer temperatures. (Economist $)

8 The problem with slapping logos on prosthesesSome wearers say it feels more like a product than a part of their body. (The Atlantic $)
+ These prosthetics break the mold with third thumbs, spikes, and superhero skins. (MIT Technology Review)

9 Mark Zuckerberg has commissioned a giant sculpture of his wife
He’s continuing in the Roman tradition, apparently. (The Guardian)

10 ChatGPT randomly started chatting to English users in Welsh
O diar! (That’s Welsh for ‘oh dear.’) (FT $)

Quote of the day

“The world that exists today is the product of monopolistic conduct. That world is changing.”

—Judge James Donato, who is presiding over the Epic v Google legal case, tells Google’s lawyer to expect harsh punishment when he makes his final ruling in the next few weeks, the Verge reports.

The big story

The search for extraterrestrial life is targeting Jupiter’s icy moon Europa

February 2024

Europa, Jupiter’s fourth-largest moon, is nothing like ours. Its surface is a vast saltwater ocean, encased in a blanket of cracked ice, one that seems to occasionally break open and spew watery plumes into the moon’s thin atmosphere.

For these reasons, Europa captivates planetary scientists. All that water and energy—and hints of elements essential for building organic molecules —point to another extraordinary possibility. Jupiter’s big, bright moon could host life.

And they may eventually get some answers. Later this year, NASA plans to launch Europa Clipper, the largest-­ever craft designed to visit another planet. The $5 billion mission, scheduled to reach Jupiter in 2030, will spend four years analyzing this moon to determine whether it could support life. Read the full story.

—Stephen Ornes

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Start your day the right way with some sweet little wolf pups.
• Happy 30th anniversary to Cotton Eye Joe, may you be a staple of good times for another three decades.
• Minecraft has released a raft of bath bombs!
• Terrible news: Paris 2024 could be break dancing’s only appearance at the Olympics (spare a thought for Ray Gun).

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MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.**

On August 8, at 16:42 local time, a magnitude-7.1 earthquake shook southern Japan. The temblor, originating off the shores of mainland island of Kyūshū, was felt by nearly a million people across the region, and initially, the threat of a tsunami emerged. But only a diminutive wave swept ashore, buildings remained upright, and nobody died. The crisis was over as quickly as it began.

But then, something new happened. The Japan Meteorological Agency, a government organization, issued a ‘megaquake advisory’ for the first time. This pair of words may appear disquieting—and to some extent, they are. There is a ticking bomb below Japanese waters, a giant crevasse where one tectonic plate dives below another. Stress has been accumulating across this boundary for quite some time, and inevitably, it will do what it has repeatedly done in the past: part of it will violently rupture, generating a devastating earthquake and a potentially huge tsunami.

The advisory was in part issued because it is possible that the magnitude-7.1 quake is a foreshock – a precursory quake – to a far larger one, a tsunami-making monster that could kill a quarter of a million people.

The good news, for now, is that scientists think it is very unlikely that that magnitude-7.1 quake is a prelude to a cataclysm. Nothing is certain, but “the chances that this actually is a foreshock are really quite low,” says Harold Tobin, the director of the Pacific Northwest Seismic Network.

The advisory, ultimately, isn’t prophetic. Its primary purpose is to let the public know that scientists are aware of what’s going on, that they are cognizant of the worst-case scenario—and that everyone else should be mindful of that grim possibility too. Evacuation routes should be memorized, and emergency supplies should be obtained, just in case.

“Even if the probability is low, the consequences are so high,” says Judith Hubbard, an earthquake scientist at Cornell University. “It makes sense to worry about some of these low probabilities.”

Japan, which sits atop a tectonic jigsaw, is no stranger to large earthquakes. Just this past New Year’s Day, a magnitude-7.6 temblor convulsed the Noto Peninsula, killing 230 people. But special attention is paid to certain quakes even when they cause no direct harm.

The August 8 event took place on the Nankai subduction zone: here, the Philippine Sea plate creeps below Japan, which is attached to the Eurasian plate. This type of plate boundary is the sort capable of producing ‘megaquakes’, those of a magnitude-8.0 and higher. (The numerical difference may seem small, but the scale is logarithmic: a magnitude-8.0 quake unleashes 32 times more energy than a magnitude-7.0 quake.)

Consequently, the Nankai subduction zone (or Nankai Trough) has created several historical tragedies. A magnitude-7.9 quake in 1944 was followed by a magnitude-8.0 quake in 1946; both events were caused by part of the submarine trench jolting. The magnitude-8.6 quake of 1707, however, involved the rupture of the entire Nankai Trough. Thousands died on each occasion.

Predicting disasterPredicting when and where the next major quake will happen anywhere on Earth is currently impossible. Nankai is no different: as recently noted by Hubbard on her blog Earthquake Insights – co-authored with geoscientist Kyle Bradley – there isn’t a set time between Nankai’s major quakes, which range from days to several centuries.

But as stress is continually accumulating on that plate boundary, it’s certain that, one day, the Nankai Trough will let loose another great quake, one which could push a vast volume of seawater toward a large swath of western and central Japan, making a tsunami 100 feet tall. The darkest scenario suggests that 230,000 could perish, two million buildings would be damaged or destroyed, and the country would be left with a $1.4 trillion bill.

Naturally, a magnitude-7.1 quake on that Trough worries scientists. Aftershocks (a series of smaller magnitude quakes) are a guaranteed feature of potent quakes. But there is a small chance that a large quake will be followed by an even larger quake, retrospectively making the first a foreshock.

“The earthquake changes the stress in the surrounding crust a little bit,” says Hubbard. Using the energy released during the August 8 rupture, and decoding the seismic waves created during the quake, scientists can estimate how much stress gets shifted to surrounding faults.

The worry is that some of the stress released by one quake gets transferred to a big fault that hasn’t ruptured in a very long time but is ready to fold like an explosive house of cards. “You never know which increment of stress is gonna be the one that pushes it over the edge.”

Scientists cannot tell whether a large quake is a foreshock until a larger quake occurs. But the possibility remains that the August 8 temblor is a foreshock to something considerably worse. Statistically, it’s unlikely. But there is additional context to why that megaquake advisory was issued: the specter of 2011’s magnitude-9.1 Tōhoku earthquake and tsunami, which killed 18,000 people, still haunts the Japanese government and the nation’s geoscientists.

Hubbard explains that, two days before that quake struck off Japan’s eastern seaboard, there was a magnitude-7.2 event in the same area—now known to be a foreshock to the catastrophe. Reportedly, authorities in Japan regretted not highlighting that possibility in advance, which may have meant people on the eastern seaboard would have been more prepared, and more capable, of escaping their fate.

A sign to get preparedIn response, Japan’s government created new protocols for signaling that foreshock possibility. Most magnitude-7.0-or-so quakes would not be followed by a ‘megaquake advisory’. Only those happening in tectonic settings able to trigger truly gigantic quakes will—and that includes the Nankai Trough.

Crucially, this advisory is not a warning that a megaquake is imminent. It means: “be ready for when the big earthquake comes,” says Hubbard. Nobody is mandated to evacuate, but they are asked to know their escape routes. Meanwhile, local news reports that nursing homes and hospitals in the region are tallying emergency supplies while moving immobile patients to higher floors or other locations. The high-speed Shinkansen railway trains are running at a reduced maximum speed, and certain flights are carrying more fuel than usual in case they need to divert.

Earthquake advisories aren’t new. “California has something similar, and has issued advisories before,” says Wendy Bohon, an independent earthquake geologist. In September 2016, for example, a swarm of hundreds of modest quakes caused the U.S. Geological Survey to publicly advise that, for a week, there was a 0.03 to 1% chance of a magnitude-7.0-or-greater quake rocking the Southern San Andreas Fault—an outcome that fortunately didn’t come to pass.

But this megaquake advisory is Japan’s first, and it will have both pros and cons. “There are economic and social consequences to this,” says Bohon. Some confusion about how to respond has been reported, and widespread cancellations of travel to the region will come with a price tag.

But calm reactions to the advisory seem to be the norm, and (ideally) this advisory will result in an increased understanding of the threat of the Nankai Trough. “It really is about raising awareness,” says Adam Pascale, chief scientist at the Seismology Research Centre in Melbourne, Australia. “It’s got everyone talking. And that’s the point.”

Geoscientists are also increasingly optimistic that the August 8 quake isn’t a harbinger of a seismic pandemonium. “This thing is way off to the extreme margin of the actual Nankai rupture zone,” says Tobin—meaning it may not even count as being in the zone of tectonic concern.

A blog post co-authored by Shinji Toda, a seismologist at Tōhoku University in Sendai, Japan, also estimates that any stress transferal to the dangerous parts of the Trough is negligible. There is no clear evidence that the plate boundary is acting weirdly. And with each day that goes by, the odds of the August 8 quake being a foreshock drop even further.

Tech defensesBut if a megaquake did suddenly emerge, Japan has a technological shield that may mitigate a decent portion of the disaster.

Buildings are commonly fitted with dampeners that allow them to withstand dramatic quake-triggered shaking. And like America’s West Coast, the entire archipelago has a sophisticated earthquake early-warning system: seismometers close to the quake’s origins listen to its seismic screams, and software makes a quick estimate of the magnitude and shaking intensity of the rupture, before beaming it to people’s various devices, giving them invaluable seconds to get to cover. Automatic countermeasures also slow trains down, control machinery in factories, hospitals, and office buildings, to minimize damage from the incoming shaking.

A tsunami early-warning system also kicks into gear if activated, beaming evacuation notices to phones, televisions, radios, sirens, and myriad specialized receivers in buildings in the afflicted region—giving people several minutes to flee. A megaquake advisory may be new, but for a population highly knowledgeable about earthquake and tsunami defense, it’s just another layer of protection.

The advisory has had other effects too: it’s caused those in another imperiled part of the world to take notice. The Cascadia Subduction Zone offshore from the US Pacific Northwest is also capable of producing both titanic quakes and prodigious tsunamis. Its last grand performance, in 1700, created a tsunami that not only inundated large sections of the North American coast, but it also swamped parts of Japan, all the way across the ocean.

Japan’s megaquake advisory has got Tobin thinking: “What would we do if our subduction zone starts acting weird?” he says—which includes a magnitude-7.0 quake in the Cascadian depths. “There is not a protocol in place the way there is in Japan.” Tobin speculates that a panel of experts would quickly assemble, and a statement – perhaps one not too dissimilar to Japan’s own advisory – would emerge from the U.S. Geological Survey. Like Japan, “we would have to be very forthright about the uncertainty,” he says.

Whether it’s Japan or the US or anywhere else, such advisories aren’t meant to engender panic. “You don’t want people to live their lives in fear,” says Hubbard. But it’s no bad thing to draw attention to the fact that Earth can sometimes be an unforgiving place to live.

Robin George Andrews is an award-winning science journalist and doctor of volcanoes based in London. He regularly writes about the Earth, space, and planetary sciences, and is the author of two critically acclaimed books: Super Volcanoes (2021) and How To Kill An Asteroid (October 2024).

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Friday marks two years since the US signed the landmark Inflation Reduction Act (IRA) into law. Now, I’m not usually one to track legislation birthdays. But this particular law is the exception, because it was a game changer for climate technology in the country, and beyond.

Over the past two years we’ve seen an influx of investment from the federal government, private businesses hoping to get in on the action, and other countries trying to keep up. And now we’re seeing all this money starting to make a difference in the climate tech sector.

Before we get to the present day, let’s do a quick refresher. In late July 2022, the US Congress reached a massive deal on a tax reform and spending package. The law changed some tax rules, implemented prescription drug pricing reform, and provided some funding for health care and the agency that collects taxes.

And then there are the climate sections, to the tune of hundreds of billions of dollars of spending. There are tax credits for businesses that build and operate new factories to produce technologies like wind and solar. There are individual tax credits to help people buy electric vehicles, heat pumps, and solar panels. There’s funding to give loans to businesses working to bring their newer technologies into the world.

Now to the fun part: Where is all that money going?

Some of the funding comes in the form of grants, designed to kick-start domestic manufacturing in areas like batteries for EVs and energy technologies. I wrote about several billion dollars going to companies making battery components and producing their ingredients in October 2022, for example.

Tax credits are another huge chunk of the bill, and it’s starting to become clear just how significant they can be for businesses. First Solar, a company making thin-film solar panels in the US, revealed earlier this year that it was in the middle of a deal to receive about $700 million from tax credits.

Then there are the provisions for individuals. As of late May, about three million households had claimed IRA tax credits for their homes in 2023. Together, they received about $8 billion for solar panels, batteries, heat pumps, and home efficiency technologies such as insulation. The credits are popular—that spending was roughly three times higher than projections had suggested.

One area I’ve been following especially closely is funding from the Loan Programs Office of the US Department of Energy, which lends money to businesses to help them get their innovative projects built. There was a $2 billion commitment to Redwood Materials, a battery recycling company I dug into just before the announcement. You might also remember a $1.52 billion loan to reopen a nuclear power plant in Michigan and a $400 million loan to give zinc batteries a boost.

It’s not just the federal government that’s pouring in money—businesses are following suit, announcing new factories or expanding old ones. Between the passage of the IRA in August 2022 and May 2024, companies have committed $110 billion for 159 projects from EVs and solar and wind to transmission projects, according to a tracker from Jack Conness, a policy analyst at Energy Innovation, an energy and climate policy firm.

The effects have rippled out beyond the US. Europe finalized the Net-Zero Industry Act in early 2024, partly as an answer to the IRA. It’s not quite the same spending spree, but the bill does include a goal for Europe to supply 40% of its own climate tech by 2030 and it implements some rule changes regarding how new projects get approved to help that happen.

The Inflation Reduction Act still has a lot of time left, and some programs have a 10-year window. One of the biggest, though often overlooked, changes over the last year is that we’ve gotten clarity on how some of the major programs are actually going to work. While the large contours were laid out in the law, some of the details about implementing them were left up to agencies to nail down. And while these specifics often seem small, they can affect which sorts of projects are eligible, changing how these credits might shape the industry.

For example, in December 2023 we learned how restrictions in the EV tax credits will affect vehicles with components made in China. As a result, starting in 2024 some vehicle models became ineligible for the credits, including the Ford Mustang Mach-E. (The company hasn’t said exactly why the model lost eligibility, but some reporting has suggested it’s likely because the lithium iron phosphate batteries used in the vehicles come from the Chinese company CATL.)

Some of those specifics get really complicated. The hydrogen tax credits could get tangled up in legal battles. The full rules on credits for sustainable aviation fuel raised concerns that fuels that don’t help much with emissions will still get funding. The credits for critical minerals apply only to processing, not to mining efforts, as my colleague James Temple detailed in his story about a Minnesota mine earlier this year.

Looking ahead, the fate of the IRA’s programs may depend on the outcome of the presidential election in November. Vice President Kamala Harris, the Democratic nominee, cast the tie-breaking vote to pass the law, and she would likely keep the programs going. Meanwhile, Donald Trump, the Republican nominee, has been openly targeting many of its provisions, and he could do some damage to many of the tax credits included, even though it would require an act of Congress to actually repeal the law. (For more on what a second Trump presidency might mean for the climate law, check out this great deep dive from James Temple.)

The action certainly isn’t slowing down in the world of climate technology. Looking ahead, one major piece of the puzzle we’ll be watching is a potential change to how new projects get approved. There’s a permitting reform package winding its way through the government now, so stay tuned for more on that, and on everything climate tech.

Now read the rest of The SparkRelated readingAt our ClimateTech event last year, Leah Stokes, an environmental policy professor at UC Santa Barbara who was closely involved with developing the IRA, spoke with us about the law. For more on how it came to be and what changes we’ve seen so far, check out her segment here.

Here’s what’s most at risk in the IRA as the US faces an election in November.

One mine in Minnesota could unlock tens of billions of dollars in tax credits, as James Temple detailed in this story from January.

MERCEDES-BENZ AGAnother thingSteel production is responsible for about 7% of global emissions. A growing array of technologies can produce the metal with less climate pollution, but there’s a big catch: They’re expensive.

But in the grand scheme of things, even steel that costs 30% more than the standard stuff would only increase the cost of the average new car by about $100, or less than 1%. That gives the auto industry a unique opportunity to help drive the world toward greener steel. Get all the details in my latest story.

Keeping up with climate The world’s biggest pumped hydropower project just came online in China. The $2.6 billion facility can store energy by pumping water uphill. (Bloomberg)

Scientists want to make a common chemical from wastewater. Researchers demonstrated a reactor that can produce ammonia from nitrates, a common pollutant found in municipal wastewater and agricultural runoff. (New Scientist)

→ Ammonia could be used as fuel for long-distance shipping. (MIT Technology Review)

The new movie Twisters shows a tornado ripping apart a wind turbine. Experts say we probably don’t need to worry too much about wind farms collapsing—those incidents tend to be rare, because turbines are built to withstand high wind speeds and are usually shut down and locked into a safe position in the case of extreme weather. (E&E News)

SunPower, once a dominant force in residential solar, is bankrupt. The company will sell off assets and gradually close up shop in the latest hit to a turbulent market. (Latitude Media)

More than 47,000 people in Europe died last year from heat-related causes. If it hadn’t been for adaptation measures like early warning systems and cooling technology, the toll could have been much higher. (New York Times)

Europe could be a bright spot for Beyond Meat and other companies selling plant-based products. The industry has seen sales and profits stagnate or drop recently, especially in the US, but Europe has lower levels of meat consumption, and supermarkets there have shown some support for animal-free alternatives. (Wired)

South Korea turns about 98% of its food waste into compost, animal feed, or energy. It’s one of the few countries with a comprehensive system for food waste, and it’s not an easy one to replicate. (Washington Post)

→ Here’s how companies want to use microbes to turn food scraps and agricultural waste into energy. (MIT Technology Review)

Just 12% of new low-emissions hydrogen projects have customers lined up. As a result, many proposed projects will probably never get built. (Bloomberg)

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Happy birthday, baby.

You have been born into an era of intelligent machines. They have watched over you almost since your conception. They let your parents listen in on your tiny heartbeat, track your gestation on an app, and post your sonogram on social media. Well before you were born, you were known to the algorithm.

Your arrival coincided with the 125th anniversary of this magazine. With a bit of luck and the right genes, you might see the next 125 years. How will you and the next generation of machines grow up together? We asked more than a dozen experts to imagine your joint future. We explained that this would be a thought experiment. What I mean is: We asked them to get weird.

Just about all of them agreed on how to frame the past: Computing shrank from giant shared industrial mainframes to personal desktop devices to electronic shrapnel so small it’s ambient in the environment. Previously controlled at arm’s length through punch card, keyboard, or mouse, computing became wearable, moving onto—and very recently into—the body. In our time, eye or brain implants are only for medical aid; in your time, who knows?

In the future, everyone thinks, computers will get smaller and more plentiful still. But the biggest change in your lifetime will be the rise of intelligent agents. Computing will be more responsive, more intimate, less confined to any one platform. It will be less like a tool, and more like a companion. It will learn from you and also be your guide.

What they mean, baby, is that it’s going to be your friend.

Present day to 2034 Age 0 to 10When you were born, your family surrounded you with “smart” things: rockers, monitors, lamps that play lullabies.

DAVID BISKUPBut not a single expert name-checked those as your first exposure to technology. Instead, they mentioned your parents’ phone or smart watch. And why not? As your loved ones cradle you, that deliciously blinky thing is right there. Babies learn by trial and error, by touching objects to see what happens. You tap it; it lights up or makes noise. Fascinating!

Cognitively, you won’t get much out of that interaction between birth and age two, says Jason Yip, an associate professor of digital youth at the University of Washington. But it helps introduce you to a world of animate objects, says Sean Follmer, director of the SHAPE Lab in Stanford’s mechanical engineering department, which explores haptics in robotics and computing. If you touch something, how does it respond?

You are the child of millennials and Gen Z—digital natives, the first influencers. So as you grow, cameras are ubiquitous. You see yourself onscreen and learn to smile or wave to the people on the other side. Your grandparents read to you on FaceTime; you photobomb Zoom meetings. As you get older, you’ll realize that images of yourself are a kind of social currency.

Your primary school will certainly have computers, though we’re not sure how educators will balance real-world and onscreen instruction, a pedagogical debate today. But baby, school is where our experts think you will meet your first intelligent agent, in the form of a tutor or coach. Your AI tutor might guide you through activities that combine physical tasks with augmented-­reality instruction—a sort of middle ground.

Some school libraries are becoming more like makerspaces, teaching critical thinking along with building skills, says Nesra Yannier, a faculty member in the Human-Computer Interaction Institute at Carnegie Mellon University. She is developing NoRILLA, an educational system that uses mixed reality—a combination of physical and virtual reality—to teach science and engineering concepts. For example, kids build wood-block structures and predict, with feedback from a cartoon AI gorilla, how they will fall.

Learning will be increasingly self-­directed, says Liz Gerber, co-director of the Center for Human-Computer Interaction and Design at Northwestern University. The future classroom is “going to be hyper-­personalized.” AI tutors could help with one-on-one instruction or repetitive sports drills.

All of this is pretty novel, so our experts had to guess at future form factors. Maybe while you’re learning, an unobtrusive bracelet or smart watch tracks your performance and then syncs data with a tablet, so your tutor can help you practice.

What will that agent be like? Follmer, who has worked with blind and low-vision students, thinks it might just be a voice. Yannier is partial to an animated character. Gerber thinks a digital avatar could be paired with a physical version, like a stuffed animal—in whatever guise you like. “It’s an imaginary friend,” says Gerber. “You get to decide who it is.”

Not everybody is sold on the AI tutor. In Yip’s research, kids often tell him AI-enabled technologies are … creepy. They feel unpredictable or scary or like they seem to be watching.

Kids learn through social interactions, so he’s also worried about technologies that isolate. And while he thinks AI can handle the cognitive aspects of tutoring, he’s not sure about its social side. Good teachers know how to motivate, how to deal with human moods and biology. Can a machine tell when a child is being sarcastic, or redirect a kid who is goofing off in the bathroom? When confronted with a meltdown, he asks, “is the AI going to know this kid is hungry and needs a snack?”

2040Age 16By the time you turn 16, you’ll likely still live in a world shaped by cars: highways, suburbs, climate change. But some parts of car culture may be changing. Electric chargers might be supplanting gas stations. And just as an intelligent agent assisted in your schooling, now one will drive with you—and probably for you.

Paola Meraz, a creative director of interaction design at BMW’s Designworks, describes that agent as “your friend on the road.” William Chergosky, chief designer at Calty Design Research, Toyota’s North American design studio, calls it “exactly like a friend in the car.”

While you are young, Chergosky says, it’s your chaperone, restricting your speed or routing you home at curfew. It tells you when you’re near In-N-Out, knowing your penchant for their animal fries. And because you want to keep up with your friends online and in the real world, the agent can comb your social media feeds to see where they are and suggest a meetup.

Just as an intelligent agent assisted in your schooling, now one will drive with you—and probably for you.

Cars have long been spots for teen hangouts, but as driving becomes more autonomous, their interiors can become more like living rooms. (You’ll no longer need to face the road and an instrument panel full of knobs.) Meraz anticipates seats that reposition so passengers can talk face to face, or game. “Imagine playing a game that interacts with the world that you are driving through,” she says, or “a movie that was designed where speed, time of day, and geographical elements could influence the storyline.”

DAVID BISKUPWithout an instrument panel, how do you control the car? Today’s minimalist interiors feature a dash-mounted tablet, but digging through endless onscreen menus is not terribly intuitive. The next step is probably gestural or voice control—ideally, through natural language. The tipping point, says Chergosky, will come when instead of giving detailed commands, you can just say: “Man, it is hot in here. Can you make it cooler?”

An agent that listens in and tracks your every move raises some strange questions. Will it change personalities for each driver? (Sure.) Can it keep a secret? (“Dad said he went to Taco Bell, but did he?” jokes Chergosky.) Does it even have to stay in the car?

Our experts say nope. Meraz imagines it being integrated with other kinds of agents—the future versions of Alexa or Google Home. “It’s all connected,” she says. And when your car dies, Chergosky says, the agent does not. “You can actually take the soul of it from vehicle to vehicle. So as you upgrade, it’s not like you cut off that relationship,” he says. “It moves with you. Because it’s grown with you.”

2049Age 25By your mid-20s, the agents in your life know an awful lot about you. Maybe they are, indeed, a single entity that follows you across devices and offers help where you need it. At this point, the place where you need the most help is your social life.

Kathryn Coduto, an assistant professor of media science at Boston University who studies online dating, says everyone’s big worry is the opening line. To her, AI could be a disembodied Cyrano that whips up 10 options or workshops your own attempts. Or maybe it’s a dating coach. You agree to meet up with a (real) person online, and “you have the AI in a corner saying ‘Hey, maybe you should say this,’ or ‘Don’t forget this.’ Almost like a little nudge.”

“There is some concern that we are going to see some people who are just like, ‘Nope, this is all I want. Why go out and do that when I can stay home with my partner, my virtual buddy?’”

T. Makana Chock, director, the Extended Reality Lab, Syracuse University

Virtual first dates might solve one of our present-day conundrums: Apps make searching for matches easier, but you get sparse—and perhaps inaccurate—info about those people. How do you know who’s worth meeting in real life? Building virtual dating into the app, Coduto says, could be “an appealing feature for a lot of daters who want to meet people but aren’t sure about a large initial time investment.”

T. Makana Chock, who directs the Extended Reality Lab at Syracuse University, thinks things could go a step further: first dates where both parties send an AI version of themselves in their place. “That would tell both of you that this is working—or this is definitely not going to work,” Chock says. If the date is a dud—well, at least you weren’t on it.

Or maybe you will just date an entirely virtual being, says Sun Joo (Grace) Ahn, who directs the Center for Advanced Computer-Human Ecosystems at the University of Georgia. Or you’ll go to a virtual party, have an amazing time, “and then later on you realize that you were the only real human in that entire room. Everybody else was AI.”

This might sound odd, says Ahn, but “humans are really good at building relationships with nonhuman entities.” It’s why you pour your heart out to your dog—or treat ChatGPT like a therapist.

There is a problem, though, when virtual relationships become too accommodating, says Chock: If you get used to agents that are tailored to please you, you get less skilled at dealing with real people and risking awkwardness or rejection. “You still need to have human interaction,” she says. “And there is some concern that we are going to see some people who are just like, ‘Nope, this is all I want. Why go out and do that when I can stay home with my partner, my virtual buddy?’”

By now, social media, online dating, and livestreaming have likely intertwined and become more immersive. Engineers have shrunk the obstacles to true telepresence: internet lag time, the uncanny valley, and clunky headsets, which may now be replaced by something more like glasses or smart contact lenses.

Online experiences may be less like observing someone else’s life and more like living it. Imagine, says Follmer: A basketball star wears clothing and skin sensors that track body position, motion, and forces, plus super-thin gloves that sense the texture of the ball. You, watching from your couch, wear a jersey and gloves made of smart textiles, woven with actuators that transmit whatever the player feels. When the athlete gets shoved, Follmer says, your fan gear “can really shove you right back.”

Gaming is another obvious application. But it’s not the likely first mover in this space. Nobody else wants to say this on the record, so I will: It’s porn. (Baby, ask your parents and/or AI tutor when you’re older.)

DAVID BISKUPBy your 20s, you are probably wrestling with the dilemmas of a life spent online and on camera. Coduto thinks you might rebel, opting out of social media because your parents documented your first 18 years without permission. As an adult, you’ll want tighter rules for privacy and consent, better ways to verify authenticity, and more control over sensitive materials, like a button that could nuke your old sexts.

But maybe it’s the opposite: Now you are an influencer yourself. If so, your body can be your display space. Today, wearables are basically boxes of electronics strapped onto limbs. Tomorrow, hopes Cindy Hsin-Liu Kao, who runs the Hybrid Body Lab at Cornell University, they will be more like your own skin. Kao develops wearables like color-changing eyeshadow stickers and mini nail trackpads that can control a phone or open a car door. In the not-too-distant future, she imagines, “you might be able to rent out each of your fingernails as an ad for social media.” Or maybe your hair: Weaving in super-thin programmable LED strands could make it a kind of screen.

What if those smart lenses could be display spaces too? “That would be really creepy,” she muses. “Just looking into someone’s eyes and it’s, like, CNN.”

2059Age 35By now, you’ve probably settled into domestic life—but it might not look much like the home you grew up in. Keith Evan Green, a professor of human-centered design at Cornell, doesn’t think we should imagine a home of the future. “I would call it a room of the future,” he says, because it will be the place for everything—work, school, play. This trend was hastened by the covid pandemic.

Your place will probably be small if you live in a big city. The uncertainties of climate change and transportation costs mean we can’t build cities infinitely outward. So he imagines a reconfigurable architectural robotic space: Walls move, objects inflate or unfold, furniture appears or dissolves into surfaces or recombines. Any necessary computing power is embedded. The home will finally be what Le Corbusier imagined: a machine for living in.

Green pictures this space as spartan but beautiful, like a temple—a place, he says, to think and be. “I would characterize it as this capacious monastic cell that is empty of most things but us,” he says.

Our experts think your home, like your car, will respond to voice or gestural control. But it will make some decisions autonomously, learning by observing you: your motion, location, temperature.

Ivan Poupyrev, CEO and cofounder of Archetype AI, says we’ll no longer control each smart appliance through its own app. Instead, he says, think of the home as a stage and you as the director. “You don’t interact with the air conditioner. You don’t interact with a TV,” he says. “You interact with the home as a total.” Instead of telling the TV to play a specific program, you make high-level demands of the entire space: “Turn on something interesting for me; I’m tired.” Or: “What is the plan for tomorrow?”

Stanford’s Follmer says that just as computing went from industrial to personal to ubiquitous, so will robotics. Your great-grandparents envisioned futuristic homes cared for by a single humanoid robot—like Rosie from The Jetsons. He envisions swarms of maybe 100 bots the size of quarters that materialize to clean, take out the trash, or bring you a cold drink. (“They know ahead of time, even before you do, that you’re thirsty,” he says.)

DAVID BISKUPBaby, perhaps now you have your own baby. The technologies of reproduction have changed since you were born. For one thing, says Gerber, fertility tracking will be way more accurate: “It is going to be like weather prediction.” Maybe, Kao says, flexible fabric-like sensors could be embedded in panty liners to track menstrual health. Or, once the baby arrives, in nipple stickers that nursing parents could apply to track biofluid exchange. If the baby has trouble latching, maybe the sticker’s capacitive touch sensors could help the parent find a better position.

Also, goodbye to sleep deprivation. Gerber envisions a device that, for lack of an existing term, she’s calling a“baby handler”—picture an exoskeleton crossed with a car seat. It’s a late-night soothing machine that rocks, supplies pre-pumped breast milk, and maybe offers a bidet-like “cleaning and drying situation.”For your children, perhaps, this is their first experience of being close to a machine.

2074Age 50Now you are at the peak of your career. For professions heading toward AI automation, you may be the “human in the loop” who oversees a machine doing its tasks. The 9-to-5 workday, which is crumbling in our time, might be totally atomized into work-from-home fluidity or earn-as-you-go gig work.

Ahn thinks you might start the workday by lying in bed and checking your messages—on an implanted contact lens. Everyone loves a big screen, and putting it in your eye effectively gives you “the largest monitor in the world,” she says.

You’ve already dabbled with AI selves for dating. But now virtual agents are more photorealistic, and they can mimic your voice and mannerisms. Why not make one go to meetings for you?

DAVID BISKUPKori Inkpen, who studies human-­computer interaction at Microsoft Research, calls this your “ditto”—more formally, an embodied mimetic agent, meaning it represents a specific person. “My ditto looks like me, acts like me, sounds like me, knows sort of what I know,” she says. You can instruct it to raise certain points and recap the conversation for you later. Your colleagues feel as if you were there, and you get the benefit of an exchange that’s not quite real time, but not as asynchronous as email. “A ditto starts to blend this reality,” Inkpen says.

In our time, augmented reality is slowly catching on as a tool for workers whose jobs require physical presence and tangible objects. But experts worry that once the last baby boomers retire, their technical expertise will go with them. Perhaps they can leave behind a legacy of training simulations.

Inkpen sees DIY opportunities. Say your fridge breaks. Instead of calling a repair person, you boot up an AR tutorial on glasses, a tablet, or a projection that overlays digital instructions atop the appliance. Follmer wonders if haptic sensors woven into gloves or clothing would let people training for highly specialized jobs—like surgery—literally feel the hand motions of experienced professionals.

For Poupyrev, the implications are much bigger. One way to think about AI is “as a storage medium,” he says. “It’s a preservation of human knowledge.” A large language model like ChatGPT is basically a compendium of all the text information people have put online. Next, if we feed models not only text but real-world sensor data that describes motion and behavior, “it becomes a very compressed presentation not of just knowledge, but also of how people do things.” AI can capture how to dance, or fix a car, or play ice hockey—all the skills you cannot learn from words alone—and preserve this knowledge for the future.

2099Age 75By the time you retire, families may be smaller, with more older people living solo.

Well, sort of. Chaiwoo Lee, a research scientist at the MIT AgeLab, thinks that in 75 years, your home will be a kind of roommate—“someone who cohabitates that space with you,” she says. “It reacts to your feelings, maybe understands you.”

By now, a home’s AI could be so good at deciphering body language that if you’re spending a lot of time on the couch, or seem rushed or irritated, it could try to lighten your mood. “If it’s a conversational agent, it can talk to you,” says Lee. Or it might suggest calling a loved one. “Maybe it changes the ambiance of the home to be more pleasant.”

The home is also collecting your health data, because it’s where you eat, shower, and use the bathroom. Passive data collection has advantages over wearable sensors: You don’t have to remember to put anything on. It doesn’t carry the stigma of sickness or frailty. And in general, Lee says, people don’t start wearing health trackers until they are ill, so they don’t have a comparative baseline. Perhaps it’s better to let the toilet or the mirror do the tracking continuously.

Green says interactive homes could help people with mobility and cognitive challenges live independently for longer. Robotic furnishings could help with lifting, fetching, or cleaning. By this time, they might be sophisticated enough to offer support when you need it and back off when you don’t.

Kao, of course, imagines the robotics embedded in fabric: garments that stiffen around the waist to help you stand, a glove that reinforces your grip.

DAVID BISKUPIf getting from point A to point B is becoming difficult, maybe you can travel without going anywhere. Green, who favors a blank-slate room, wonders if you’ll have a brain-machine interface that lets you change your surroundings at will. You think about, say, a jungle, and the wallpaper display morphs. The robotic furniture adjusts its topography. “We want to be able to sit on the boulder or lie down on the hammock,” he says.

Anne Marie Piper, an associate professor of informatics at UC Irvine who studies older adults, imagines something similar—minus the brain chip—in the context of a care home, where spaces could change to evoke special memories, like your honeymoon in Paris. “What if the space transforms into a café for you that has the smells and the music and the ambience, and that is just a really calming place for you to go?” she asks.

Gerber is all for virtual travel: It’s cheaper, faster, and better for the environment than the real thing. But she thinks that for a truly immersive Parisian experience, we’ll need engineers to invent … well, remote bread. Something that lets you chew on a boring-yet-nutritious source of calories while stimulating your senses so you get the crunch, scent, and taste of the perfect baguette.

2149Age 125We hope that your final years will not be lonely or painful.

Faraway loved ones can visit by digital double, or send love through smart textiles: Piper imagines a scarf that glows or warms when someone is thinking of you, Kao an on-skin device that simulates the touch of their hand. If you are very ill, you can escape into a soothing virtual world. Judith Amores, a senior researcher at Microsoft Research, is working on VR that responds to physiological signals. Today, she immerses hospital patients in an underwater world of jellyfish that pulse at half of an average person’s heart rate for a calming effect. In the future, she imagines, VR will detect anxiety without requiring a user to wear sensors—maybe by smell.

“It is a little cool to think of cemeteries in the future that are literally haunted by motion-activated holograms.”

Tim Recuber, sociologist, Smith College

You might be pondering virtual immortality. Tim Recuber, a sociologist at Smith College and author of The Digital Departed, notes that today people create memorial websites and chatbots, or sign up for post-mortem messaging services. These offer some end-of-life comfort, but they can’t preserve your memory indefinitely. Companies go bust. Websites break. People move on; that’s how mourning works.

What about uploading your consciousness to the cloud? The idea has a fervent fan base, says Recuber. People hope to resurrect themselves into human or robotic bodies, or spend eternity as part of a hive mind or “a beam of laser light that can travel the cosmos.” But he’s skeptical that it’ll work, especially within 125 years. Plus, what if being a ghost in the machine is dreadful? “Embodiment is, as far as we know, a pretty key component to existence. And it might be pretty upsetting to actually be a full version of yourself in a computer,” he says.

DAVID BISKUPThere is perhaps one last thing to try. It’s another AI. You curate this one yourself, using a lifetime of digital ephemera: your videos, texts, social media posts. It’s a hologram, and it hangs out with your loved ones to comfort them when you’re gone. Perhaps it even serves as your burial marker. “It is a little cool to think of cemeteries in the future that are literally haunted by motion-activated holograms,” Recuber says.

It won’t exist forever. Nothing does. But by now, maybe the agent is no longer your friend.

Maybe, at last, it is you.

Baby, we have caveats.We imagine a world that has overcome the worst threats of our time: a creeping climate disaster; a deepening digital divide; our persistent flirtation with nuclear war; the possibility that a pandemic will kill us quickly, that overly convenient lifestyles will kill us slowly, or that intelligent machines will turn out to be too smart.

We hope that democracy survives and these technologies will be the opt-in gadgetry of a thriving society, not the surveillance tools of dystopia. If you have a digital twin, we hope it’s not a deepfake.

You might see these sketches from 2024 as a blithe promise, a warning, or a fever dream. The important thing is: Our present is just the starting point for infinite futures.

What happens next, kid, depends on you.

Kara Platoni is a science reporter and editor in Oakland, California.

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The US Department of Homeland Security (DHS) plans to collect and analyze photos of the faces of migrant children at the border in a bid to improve facial recognition technology, MIT Technology Review can reveal. This includes children “down to the infant,” according to John Boyd, assistant director of the department’s Office of Biometric Identity Management (OBIM), where a key part of his role is to research and develop future biometric identity services for the government.

As Boyd explained at a conference in June, the key question for OBIM is, “If we pick up someone from Panama at the southern border at age four, say, and then pick them up at age six, are we going to recognize them?”

Facial recognition technology (FRT) has traditionally not been applied to children, largely because training data sets of real children’s faces are few and far between, and consist of either low-quality images drawn from the internet or small sample sizes with little diversity. Such limitations reflect the significant sensitivities regarding privacy and consent when it comes to minors.

In practice, the new DHS plan could effectively solve that problem. According to Syracuse University’s Transactional Records Access Clearinghouse (TRAC), 339,234 children arrived at the US-Mexico border in 2022, the last year for which numbers are currently available. Of those children, 150,000 were unaccompanied—the highest annual number on record. If the face prints of even 1% of those children had been enrolled in OBIM’s craniofacial structural progression program, the resulting data set would dwarf nearly all existing data sets of real children’s faces used for aging research.

It’s unclear to what extent the plan has already been implemented; Boyd tells MIT Technology Review that to the best of his knowledge, the agency has not yet started collecting data under the program, but he adds that as “the senior executive,” he would “have to get with [his] staff to see.” He could only confirm that his office is “funding” it. Despite repeated requests, Boyd did not provide any additional information.

Boyd says OBIM’s plan to collect facial images from children under 14 is possible due to recent “rulemaking” at “some DHS components,” or sub-offices, that have removed age restrictions on the collection of biometric data. DHS did not comment on the program prior to publication. US Customs and Border Protection (CBP), the US Transportation Security Administration, and US Immigration and Customs Enforcement declined to comment before publication. US Citizenship and Immigration Services (USCIS) did not respond to multiple requests for comment. OBIM referred MIT Technology Review back to DHS’s main press office.

Boyd spoke publicly about the plan in June at the Federal Identity Forum and Exposition, an annual identity management conference for federal employees and contractors. But close observers of DHS that we spoke with—including a former official, representatives of two influential lawmakers who have spoken out about the federal government’s use of surveillance technologies, and immigrants’ rights organizations that closely track policies affecting migrants—were unaware of any new policies allowing biometric data collection of children under 14.

That is not to say that all of them are surprised. “That tracks,” says one former CBP official who has visited several migrant processing centers on the US-Mexico border and requested anonymity to speak freely. He says “every center” he visited “had biometric identity collection, and everybody was going through it,” though he was unaware of a specific policy mandating the practice. “I don’t recall them separating out children,” he adds.

“The reports of CBP, as well as DHS more broadly, expanding the use of facial recognition technology to track migrant children is another stride toward a surveillance state and should be a concern to everyone who values privacy,” Justin Krakoff, deputy communications director for Senator Jeff Merkley of Oregon, said in a statement to MIT Technology Review. Merkley has been an outspoken critic of both DHS’s immigration policies and of government use of facial recognition technologies.

Beyond concerns about privacy, transparency, and accountability, some experts also worry about testing and developing new technologies using data from a population that has little recourse to provide—or withhold—consent.

Could consent “actually take into account the vast power differentials that are inherent in the way that this is tested out on people?” asks Petra Molnar, author of The Walls Have Eyes: Surviving Migration in the Age of AI. “And if you arrive at a border … and you are faced with the impossible choice of either: get into a country if you give us your biometrics, or you don’t.”

“That completely vitiates informed consent,” she adds.

This question becomes even more challenging when it comes to children, says Ashley Gorski, a senior staff attorney with the American Civil Liberties Union. DHS “should have to meet an extremely high bar to show that these kids and their legal guardians have meaningfully consented to serve as test subjects,” she says. “There’s a significant intimidation factor, and children aren’t as equipped to consider long-term risks.”

Murky new rulesThe Office of Biometric Identity Management, previously known as the US Visitor and Immigrant Status Indicator Technology Program (US-VISIT), was created after 9/11 with the specific mandate of collecting biometric data—initially only fingerprints and photographs—from all non-US citizens who sought to enter the country.

Since then, DHS has begun collecting face prints, iris and retina scans, and even DNA, among other modalities. It is also testing new ways of gathering this data—including through contactless fingerprint collection, which is currently deployed at five sites on the border, as Boyd shared in his conference presentation.

Since 2023, CBP has been using a mobile app, CBP One, for asylum seekers to submit biometric data even before they enter the United States; users are required to take selfies periodically to verify their identity. The app has been riddled with problems, including technical glitches and facial recognition algorithms that are unable to recognize darker-skinned people. This is compounded by the fact that not every asylum seeker has a smartphone.

Then, just after crossing into the United States, migrants must submit to collection of biometric data, including DNA. For a sense of scale, a recent report from Georgetown Law School’s Center on Privacy and Technology found that CBP has added 1.5 million DNA profiles, primarily from migrants crossing the border, to law enforcement databases since it began collecting DNA “from any person in CBP custody subject to fingerprinting” in January 2020. The researchers noted that an overrepresentation of immigrants—the majority of whom are people of color—in a DNA database used by law enforcement could subject them to over-policing and lead to other forms of bias.

Generally, these programs only require information from individuals aged 14 to 79. DHS attempted to change this back in 2020, with proposed rules for USCIS and CBP that would have expanded biometric data collection dramatically, including by age. (USCIS’s proposed rule would have doubled the number of people from whom biometric data would be required, including any US citizen who sponsors an immigrant.) But the USCIS rule was withdrawn in the wake of the Biden administration’s new “priorities to reduce barriers and undue burdens in the immigration system.” Meanwhile, for reasons that remain unclear, the proposed CBP rule was never enacted.

This would make it appear “contradictory” if DHS were now collecting the biometric data of children under 14, says Dinesh McCoy, a staff attorney with Just Futures Law, an immigrant rights group that tracks surveillance technologies.

Neither Boyd nor DHS’s media office would confirm which specific policy changes he was referring to in his presentation, though MIT Technology Review has identified a 2017 memo, issued by then-Secretary of Homeland Security John F. Kelly, that encouraged DHS components to remove “age as a basis for determining when to collect biometrics.”

The DHS’s Office of the Inspector General (OIG) referred to this memo as the “overarching policy for biometrics at DHS” in a September 2023 report, though none of the press offices MIT Technology Review contacted—including the main DHS press office, OIG, and OBIM, among others—would confirm whether this was still the relevant policy; we have not been able to confirm any related policy changes since then.

The OIG audit also found a number of fundamental issues related to DHS’s oversight of biometric data collection and use—including that its 10-year strategic framework for biometrics, covering 2015 to 2025, “did not accurately reflect the current state of biometrics across the Department, such as the use of facial recognition verification and identification.” Nor did it provide clear guidance for the consistent collection and use of biometrics across DHS, including age requirements.

But there is also another potential explanation for the new OBIM program: Boyd says it is being conducted under the auspices of the DHS’s undersecretary of science and technology, the office that leads much of the agency’s research efforts. Because it is for research, rather than to be used “in DHS operations to inform processes or decision making,” many of the standard restrictions for DHS use of face recognition and face capture technologies do not apply, according to a DHS directive.

Some lawyers allege that changing the age limit for data collection via department policy, not by a federal rule, which requires a public comment period, is problematic. McCoy, for instance, says any lack of transparency here amplifies the already “extremely challenging” task of “finding [out] in a systematic way how these technologies are deployed”—even though that is key for accountability.

Do you have any additional information on DHS’s craniofacial structural progression initiative? Please reach out at with a non-work email to tips@technologyreview.com or securely on Signal at 626.765.5489.

Advancing the field At the identity forum and in a subsequent conversation, Boyd explained that this data collection is meant to advance the development of effective FRT algorithms. Boyd leads OBIM’s Future Identity team, whose mission is to “research, review, assess, and develop technology, policy, and human factors that enable rapid, accurate, and secure identity services” and to make OBIM “the preferred provider for identity services within DHS.”

Driven by high-profile cases of missing children, there has long been interest in understanding how children’s faces age. At the same time, there have been technical challenges to doing so, both preceding FRT and with it.

At its core, facial recognition identifies individuals by comparing the geometry of various facial features in an original face print with subsequent images. Based on this comparison, a facial recognition algorithm assigns a percentage likelihood that there is a match.

But as children grow and develop, their bone structure changes significantly, making it difficult for facial recognition algorithms to identify them over time. (These changes tend to be even more pronounced in children under 14. In contrast, as adults age, the changes tend to be in the skin and muscle, and have less variation overall.) More data would help solve this problem, but there is a dearth of high-quality data sets of children’s faces with verifiable ages.

“What we’re trying to do is to get large data sets of known individuals,” Boyd tells MIT Technology Review. That means taking high-quality face prints “under controlled conditions where we know we’ve got the person with the right name [and] the correct birth date”—or, in other words, where they can be certain about the “provenance of the data.”

For example, one data set used for aging research consists of 305 celebrities’ faces as they aged from five to 32. But these photos, scraped from the internet, contain too many other variables—such as differing image qualities, lighting conditions, and distances at which they were taken—to be truly useful. Plus, speaking to the provenance issue that Boyd highlights, their actual ages in each photo can only be estimated.

Another tactic is to use data sets of adult faces that have been synthetically de-aged. Synthetic data is considered more privacy-preserving, but it too has limitations, says Stephanie Schuckers, director of the Center for Identification Technology Research (CITeR). “You can test things with only the generated data,” Schuckers explains, but the question remains: “Would you get similar results to the real data?”

(Hosted at Clarkson University in New York, CITeR brings together a network of academic and government affiliates working on identity technologies. OBIM is a member of the research consortium.)

Schuckers’s team at CITeR has taken another approach: an ongoing longitudinal study of a cohort of 231 elementary and middle school students from the area around Clarkson University. Since 2016, the team has captured biometric data every six months (save for two years of the covid-19 pandemic), including facial images. They have found that the open-source face recognition models they tested can in fact successfully recognize children three to four years after they were initially enrolled.

But the conditions of this study aren’t easily replicable at scale. The study images are taken in a controlled environment, all the participants are volunteers, the researchers sought consent from parents and the subjects themselves, and the research was approved by the university’s Institutional Review Board. Schuckers’s research also promises to protect privacy by requiring other researchers to request access, and by providing facial datasets separately from other data that have been collected.

What’s more, this research still has technical limitations, including that the sample is small, and it is overwhelmingly Caucasian, meaning it might be less accurate when applied to other races.

Schuckers says she was unaware of DHS’s craniofacial structural progression initiative.

Far-reaching implications Boyd says OBIM takes privacy considerations seriously, and that “we don’t share … data with commercial industries.” Still, OBIM has 144 government partners with which it does share information, and it has been criticized by the Government Accountability Office for poorly documenting who it shares information with, and with what privacy-protecting measures.

Even if the data does stay within the federal government, OBIM’s findings regarding the accuracy of FRT for children over time could neverthelessinfluence how—and when—the rest of the government collects biometric data, as well as whether the broader facial recognition industry may also market its services for children. (Indeed, Boyd says sharing “results,” or the findings of how accurate FRT algorithms are, is different than sharing the data itself.)

That this technology is being tested on people who are offered fewer privacy protections than would be afforded to US citizens is just part of the wider trend of using people from the developing world, whether they are migrants coming to the border or civilians in war zones, to help improve new technologies.

In fact, Boyd previously helped advance the Department of Defense’s biometric systems in Iraq and Afghanistan, where he acknowledged that individuals lacked the privacy protections that would have been granted in many other contexts, despite the incredibly high stakes. Biometric data collected in those war zones—in some areas, from every fighting-age male—was used toidentify and target insurgents, and being misidentified could mean death.

These projects subsequently played a substantial role in influencing the expansion of biometric data collection by the Department of Defense, which now happens globally. And architects of the program, like Boyd, have taken important roles in expanding the use of biometrics at other agencies.

“It’s not an accident” that this testing happens in the context of border zones, says Molnar. Borders are “the perfect laboratory for tech experimentation, because oversight is weak, discretion is baked into the decisions that get made … it allows the state to experiment in ways that it wouldn’t be allowed to in other spaces.”

But, she notes, “just because it happens at the border doesn’t mean that that’s where it’s going to stay.”

Do you have any additional information on DHS’s craniofacial structural progression initiative? Please reach out at with a non-work email to tips@technologyreview.com or securely on Signal at 626.765.5489.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

A new public database lists all the ways AI could go wrong

What’s new: Adopting AI can be fraught with danger. Systems could be biased, or parrot falsehoods, or even become addictive. And that’s before you consider the possibility AI could one day somehow spin out of our control. To manage these potential risks, we first need to understand them. A new database compiled by the FutureTech group at MIT’s CSAIL with a team of collaborators and published online today could help.

Why it matters: The AI Risk Repository documents over 700 potential risks advanced AI systems could pose, making it the most comprehensive source yet of information about issues that could arise from the creation and deployment of these models. However, even with this new database, it’s hard to know which we ought to worry about the most. Read the full story.

—Scott J Mulligan

MIT Technology Review Narrated: The search for extraterrestrial life is targeting Jupiter’s icy moon Europa

We’ve known of Europa’s existence for more than four centuries, but for most of that time, Jupiter’s fourth-largest moon was just a pinprick of light in our telescopes.

Over the last few decades, however, as astronomers have scrutinized it through telescopes and six spacecraft have flown nearby, a new picture has come into focus. Europa is nothing like our moon.

This is our latest story to be turned into a MIT Technology Review Narrated podcast. In partnership with News Over Audio, we’ll be making a selection of our stories available, each one read by a professional voice actor. You’ll be able to listen to them on the go or download them to listen to offline.

We’re publishing a new story each week on Spotify and Apple Podcasts, including some taken from our most recent print magazine.

Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 US officials are considering breaking up Google
It would be the first time Washington has tried to dismantle a company for illegal monopolization in two decades. (Bloomberg $)
+ But deliberations are in very early stages. (NYT $)

2 The European Union isn’t afraid of Elon Musk
The entrepreneur’s prolific spat with the bloc marks a key moment in the EU’s attempts to rein in powerful companies. (FT $)

3 Google’s Gemini Live bot sounds incredibly humanlikeIt’s designed to hold snappy conversations that sound more natural than its rivals. (WSJ $)
+ It’s not without its flaws, however. (The Verge)
+ OpenAI has released a new ChatGPT bot that you can talk to. (MIT Technology Review)

4 Silicon Valley is worried by an AI regulation bill
The industry fears it will hamper the technology’s progress in California. (NYT $)
+ Companies are spending serious cash on ads promoting AI’s benefits. (WP $)
+ What’s next for AI regulation? (MIT Technology Review)

5 We’re learning more about Mars’ capacity to host life
Significant amounts of water could be trapped in its crust. (The Guardian)

6 Pakistan’s extreme heat waves are getting worseAnd it’s the country’s poorest who are suffering the most. (The Atlantic $)
+ Here’s how much heat your body can take. (MIT Technology Review)

7 Dangerous products for children are easily available on Shein and Temu
Regulators have warned that the goods are unsafe. (The Information $)
+ Why my bittersweet relationship with Shein had to end. (MIT Technology Review)

8 Future brain surgeries could be entirely noninvasiveSound waves could replace scalpels in as little as five years, experts say. (Bloomberg $)
+ Last year, doctors performed brain surgery on a fetus in one of the first operations of its kind. (MIT Technology Review)

9 We’re surrounded by more information than we know what to do withWhat we do with that data is up to us. (New Yorker $)

10 Chinese robotaxis have been given the green light in California
Startup WeRide has been granted permission to test its cars—with passengers. (Reuters)
+ What’s next for robotaxis. (MIT Technology Review)

Quote of the day

“Knowing how charismatic you are… you could not possibly use such vulgar words.”

—A Facebook user says they weren’t fooled by a deepfake video of Singapore’s former prime minister Lee Hsien Loong, Rest of World reports.

The big story

The rise of the tech ethics congregation

August 2023

Just before Christmas 2022, a pastor preached a gospel of morals over money to several hundred members of his flock. But the leader in question was not an ordained minister, nor even a religious man.

Polgar is the founder of All Tech Is Human, a nonprofit organization devoted to promoting ethics and responsibility in tech. His congregation is undergoing dramatic growth in an age when the life of the spirit often struggles to compete with cold, hard, capitalism.

Its leaders believe there are large numbers of individuals in and around the technology world, often from marginalized backgrounds, who wish tech focused less on profits and more on being a force for ethics and justice. But attempts to stay above the fray can cause more problems than they solve. Read the full story.

—Greg M. Epstein

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• If you’re lucky enough to own a coffee bar, here’s some inspiration to make sure it’s looking its best. ($)
• Construction of the world’s largest 3D-printed neighborhood is well underway.
• What even is true authenticity these days?
• Forget what you’ve been told: slouching isn’t actually that bad.

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Adopting AI can be fraught with danger. Systems could be biased, or parrot falsehoods, or even become addictive. And that’s before you consider the possibility AI could be used to create new biological or chemical weapons, or even one day somehow spin out of our control.

To manage these potential risks, we first need to know what they are. A new database compiled by the FutureTech group at MIT’s CSAIL with a team of collaborators and published online today could help. The AI Risk Repository documents over 700 potential risks advanced AI systems could pose. It’s the most comprehensive source yet of information about previously identified issues that could arise from the creation and deployment of these models.

The team combed through peer-reviewed journal articles and preprint databases that detail AI risks. The most common risks centered around AI system safety and robustness (76%), unfair bias and discrimination (63%), and compromised privacy (61%). Less common risks tended to be more esoteric, such as the risk of creating AI with the ability to feel pain or to experience something akin to “death.”

The database also shows that the majority of risks from AI are identified only after a model becomes accessible to the public. Just 10% of the risks studied were spotted before deployment.

These findings may have implications for how we evaluate AI, as we currently tend to focus on ensuring a model is safe before it is launched. “What our database is saying is, the range of risks is substantial, not all of which can be checked ahead of time,” says Neil Thompson, director of MIT FutureTech and one of the creators of the database. Therefore, auditors, policymakers, and scientists at labs may want to monitor models after they are launched by regularly reviewing the risks they present post-deployment.

There have been many attempts to put together a list like this in the past, but they were concerned primarily with a narrow set of potential harms arising from AI, says Thompson, and the piecemeal approach made it hard to get a comprehensive view of the risks associated with AI.

Even with this new database, it’s hard to know which AI risks to worry about the most, a task made even more complicated because we don’t fully understand how cutting-edge AI systems even work.

The database’s creators sidestepped that question, choosing not to rank risks by the level of danger they pose.

“What we really wanted to do was to have a neutral and comprehensive database, and by neutral, I mean to take everything as presented and be very transparent about that,” says the database’s lead author, Peter Slattery, a postdoctoral associate at MIT FutureTech.

But that tactic could limit the database’s usefulness, says Anka Reuel, a PhD student in computer science at Stanford University and member of its Center for AI Safety, who was not involved in the project. She says merely compiling risks associated with AI will soon be insufficient. “They’ve been very thorough, which is a good starting point for future research efforts, but I think we are reaching a point where making people aware of all the risks is not the main problem anymore,” she says. “To me, it’s translating those risks. What do we actually need to do to combat [them]?”

This database opens the door for future research. Its creators made the list in part to dig into their own questions, like which risks are under-researched or not being tackled. “What we’re most worried about is, are there gaps?” says Thompson.

“We intend this to be a living database, the start of something. We’re very keen to get feedback on this,” Slattery says. “We haven’t put this out saying, ‘We’ve really figured it out, and everything we’ve done is going to be perfect.’”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How the auto industry could steer the world toward green steel

Steel scaffolds our world, undergirding buildings and machines. It also presents a major challenge for climate change, as steel production is currently responsible for about 7% of global greenhouse gas emissions.

There’s a growing array of technologies that can produce steel with dramatically lower emissions—though some are still in development, and they often come with a higher price tag.

Finding economical ways to produce the materials we rely on while also cutting emissions is a major challenge for the industrial sector. But since automakers use a lot of steel, they have an opportunity to lead the charge to decarbonize the industry. Here’s how they could do it.

—Casey Crownhart

EmTech 2024 is coming

Want to learn more about the technologies that are shaping our lives? Join us for our flagship conference EmTech, held on the MIT Campus from September 30th to October 1st this fall. Take a sneak peek at our jam-packed agenda, which includes:

• Ray Kurzweil, principal researcher at Google and AI visionary, discussing his latest predictions on artificial general intelligence, singularity, and the infinite possibilities of an AI-integrated world.

• Riki Banerjee, CTO of brain-computer interface company Synchron, will give us an inside look at the future of minimally invasive brain-computer interfaces that enable humans to use their thoughts to control digital devices.

• Pete Shadbolt, the cofounder and chief scientific officer of PsiQuantum, which is working to build the biggest US-based quantum computing facility, will explain the rewards and challenges facing quantum tech.

• Yasmin Green, CEO of Jigsaw, a global security unit within Google, will dive into the secret digital behaviors of Gen Z.

The best part is, Download readers get 30% off with the following code: DOWNLOADM24. So what are you waiting for? Get your ticket today.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Elon Musk and Donald Trump’s chat on X was marred by glitches
Their conversation started more than 40 minutes late as a result. (WP $)
+ X staffers have contradicted Musk’s claims a DDoS attack was to blame. (The Verge)
+ The discussion was rambling, to say the least. (FT $)

2 A scientific journal has retracted three MDMA papers
Psychopharmacology cited concerns over missing data and unethical conduct. (Ars Technica)
+ The retraction comes just days after the FDA rejected MDMA as a PTSD treatment. (NYT $)
+ What’s next for MDMA. (MIT Technology Review)

3 Huawei is working on an Nvidia-rivaling chip
And unlike Nvidia’s, Huawei’s can go on sale in China. (WSJ $)
+ Smuggling Nvidia’s chips into forbidden territories is big business. (The Information $)
+ This unassuming Czech town is on its way to becoming a chip hub. (Bloomberg $)
+ What’s next in chips. (MIT Technology Review)

4 How a plan to revitalize Puerto Rico’s economy with crypto souredThe ‘Puertopia’ tech hub dream is dead. (NYT $)
+ Crypto millionaires are pouring money into Central America to build their own cities. (MIT Technology Review)

5 Wastewater could prove a sustainable source of fuel
The process to break it down is more environmentally-friendly and less energy-intensive than other ammonia production methods. (New Scientist $)
+ How ammonia could help clean up global shipping. (MIT Technology Review)

6 The long, lonely journey to the moon’s south pole
Companies are locked in competition to make NASA’s lunar vehicle. (Wired $)

7 Recruiters are being inundated with AI-generated CVsJobseekers are failing to conceal generative tools’ tell-tale signs. (FT $)

8 What TikTok is teaching tweens about beauty
Modern girlhood is peppered with $80 serums for skin issues they’re yet to develop. (New Yorker $)

9 AI could help us to track animals from their footprints
It’s an unobtrusive way of keeping track of elusive species. (Hakai Magazine)
+ How tracking animal movement may save the planet. (MIT Technology Review)

10 Would you stare into a stranger’s eyes online?
If the notion doesn’t fill you with horror, Eyechat is the site for you. (404 Media)
+ A dating app for people with good credit scores has sadly closed down. (TechCrunch)

Quote of the day

“The vehicle for sowing fear and doubt about the system itself has changed — it’s just this perpetual moving target.”

—Justin F. Roebuck, the county clerk for Ottawa County in Michigan, describes the immense challenges election officials face in countering false political narratives to the New York Times.

The big story

Quantum computing is taking on its biggest challenge: noise

January 2024

In the past 20 years, hundreds of companies have staked a claim in the rush to establish quantum computing. Investors have put in well over $5 billion so far. All this effort has just one purpose: creating the world’s next big thing.

But ultimately, assessing our progress in building useful quantum computers comes down to one central factor: whether we can handle the noise. The delicate nature of their systems makes them extremely vulnerable to the slightest disturbance, which can generate errors or even stop a quantum computation in its tracks.

In the last couple of years, a series of breakthroughs have led researchers to declare that the problem of noise might finally be on the ropes. Read the full story.

—Michael Brooks

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• The story behind Moby’s seminal single Go—featuring Laura Palmer’s Theme from Twin Peaks.
• These photos of the Perseid meteor shower over the UK this week are really beautiful.
• This TikTok account recreating House of the Dragon in The Sims is too good.
• Stumped by the financial jargon they use in Industry? You’re not alone.

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Steel scaffolds our world, undergirding buildings and machines. It also presents a major challenge for climate change, since steel production largely relies on polluting fossil fuels. The automotive industry could be a key player in turning things around.

Steel production is currently responsible for about 7% of global greenhouse gas emissions. There’s a growing array of technologies that can produce steel with dramatically lower emissions—though some are still in development, and they often come with a higher price tag. The auto industry could be a fertile early market for these technologies, both because it’s a major player in the industry and because switching to more expensive materials would only bump costs up for new vehicles by less than 1%, according to a new report.

Finding economical ways to produce the materials we rely on while also cutting emissions is a major challenge for the industrial sector. Vehicle manufacturers embracing greener steel could provide a blueprint for how to bring more climate-friendly materials to the market without driving customers away.

Since automakers use a lot of steel, they have an opportunity to lead the charge to decarbonize the industry, says Peter Slowik, an analyst leading research on passenger vehicles in the US for the International Council on Clean Transportation.

About 12% of global steel production goes to the auto industry, and in some regions, the percentage is significantly higher—about 60% of all primary (non-recycled) steel produced in the US goes to vehicle manufacturing. That non-recycled steel comes with higher emissions than the recycled version, so making a swap to greener steel in the automotive industry, which mostly uses non-recycled material, would have an outsized impact.

Making steel today generally requires steelmakers to heat raw materials to high temperatures, using fossil fuels like coal to drive the chemical reactions that transform iron ore into steel. But there’s a growing array of ways to make steel with lower emissions, including efforts to add carbon capture technology to new and existing plants and implement new technologies that rely on electricity instead of fossil fuels.

One leading contender for producing low-emissions steel is a process called direct reduction, where chemical reactions can be powered by hydrogen fuel instead of coal. If that hydrogen is produced with renewable or other low-carbon energy sources, it could allow steel production with up to 95% lower emissions.

Steel is responsible for a major chunk of the climate impacts of manufacturing a vehicle—so swapping in green steel could cut the emissions associated with building a car by 27%, according to the ICCT report.

And the materials wouldn’t dramatically inflate costs, either. “Generally, we’re finding that it wouldn’t add too much to the cost of the vehicle,” Slowik says.

H2 Green Steel is currently building what could become the world’s largest low-emissions steel factory, with a capacity of 2.5 million metric tons of steel by 2026. The company has said its product will cost 20% to 30% more than conventional steel. That would add roughly $100 to $200 more to a vehicle’s cost of materials, totaling less than 1% of the average vehicle.

In another recent report examining steel in vehicle manufacturing in Europe, experts put the additional cost at just €105, or about $115, for a vehicle made entirely with steel produced using a hydrogen-powered process in 2030. And even that slight cost bump could disappear in the future as production volumes increase and costs come down.

“The relatively high value of cars, especially of premium brands, also means they can absorb the short-term green premium of greener steel,” Alex Keynes, cars policy manager at the European Federation for Transport and Environment, said in an email.

The same principle might hold for some other common products made with steel. One estimate from Hannah Ritchie, a data scientist and deputy editor at Our World In Data, put the added cost for using green steel in a house at less than 1% of its purchase price.

There’s a complicated web of actors in construction though, from architects to builders to contractors, which could make purchasing more expensive materials that come with a climate benefit a more complex proposition. And bigger projects that require more steel could face much larger price increases that make green steel unaffordable in those contexts, at least for now.

Automakers committing to purchasing green steel from steelmakers could help ensure they’re able to grow quickly, and some companies have already secured such commitments. As of January 2024, H2 Green Steel had binding agreements in place for more than 40% of its steel production in the initial years of its new plant.

However, there are still challenges facing the industry, including questions about the future cost and availability of green hydrogen, Keynes says. Policy measures, from subsidies to encourage the fuel’s production to regulations, could be crucial to getting greener steel into our vehicles and beyond.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Here’s how people are actually using AI

When the generative AI boom started with ChatGPT in late 2022, we were sold a vision of superintelligent AI tools that know everything, can replace the boring bits of work, and supercharge productivity and economic gains.

Two years on, those productivity gains mostly haven’t materialized. Instead, we’ve seen something peculiar and slightly unexpected happen: People have started forming relationships with AI systems. We talk to them, say please and thank you, and have started to invite AIs into our lives as friends, lovers, mentors, therapists, and teachers. It’s a fascinating development, and shows how hard it is to predict how cutting-edge technology will be adopted. Read the full story.

—Melissa Heikkilä

This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

If you’re interested in how people are forming connections with AI, why not take a look at:

• Deepfakes of your dead loved ones are a booming Chinese business. Read the full story.

• Technology that lets us “speak” to our dead relatives has arrived. Are we ready? Digital clones of the people we love could forever change how we grieve. Read the full story.

• My colleagues turned me into an AI-powered NPC. I hate him. Take a look behind the controls of a new way to create video-game characters that engage with players in unique, ever-changing ways.

• An AI startup made a hyperrealistic deepfake of me that’s so good it’s scary. Synthesia’s new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real. Read the full story.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Hackers infiltrated Donald Trump’s electoral campaign
His team is blaming Iran and accusing Tehran of political interference. (FT $)
+ Microsoft appears to have confirmed the country’s involvement. (The Guardian)
+ The news outlet Politico received emails containing stolen documents. (Politico)

2 The meat industry’s sustainability claims don’t add up
Environmental groups are reluctant to challenge the sector, which is a major problem. (Vox)
+ How I learned to stop worrying and love fake meat. (MIT Technology Review)

3 How a crypto data leak led the FBI to a notorious sex trafficker
Michael Pratt is facing a possible life sentence as a result. (Insider $)

4 Brands are begging influencers to swerve politicsAnd they’re even using AI to predict whether influencers they’re thinking of partnering with are likely to express political opinions. (NYT $)
+ Elon Musk, meanwhile, is becoming increasingly political. (WP $)

5 How a fake cricket match exposed an illegal gambling ring Online gamblers had no idea they were betting on fixed tournaments. (Bloomberg $)
+ How mobile money supercharged Kenya’s sports betting addiction. (MIT Technology Review)

6 Where did it all go wrong for Cameo?
The celebrity video app has fallen on hard times. (The Guardian)

7 Coral reefs may have an unlikely new savior
Release the sea urchins! (The Atlantic $)
+ The race is on to save coral reefs—by freezing them. (MIT Technology Review)

8 Calorie counting has had a 2024 makeover
And AI is involved, naturally. (WSJ $)

9 What a kinder online community can teach us
Vermont’s Front Porch Forum has succeeded where other platforms have failed. (WP $)
+ How to fix the internet. (MIT Technology Review)

10 How tech workers-turned athletes fared in this year’s Olympics
They juggled their day jobs and training for the prestigious tournament. (The Information $)

Quote of the day

“I’m looking for an EV. I just don’t want a Tesla.”

—Esther Chun, manager of a Polestar car dealership in San Jose, says customers frequently cite Elon Musk as a reason not to buy his electric cars to the Washington Post.

The big story

Meet the divers trying to figure out how deep humans can go

February 2024

Two hundred thirty meters into one of the deepest underwater caves on Earth, Richard “Harry” Harris knew that not far ahead of him was a 15-meter drop leading to a place no human being had seen before.

Getting there had taken two helicopters, three weeks of test dives, two tons of equipment, and hard work to overcome an unexpected number of technical problems. But in the moment, Harris was hypnotized by what was before him: the vast, black, gaping unknown.

Staring into it, he felt the familiar pull—maybe he could go just a little farther. Instead, he and his diving partner, Craig Challen, decided to turn back. That’s because they weren’t there to set records. Instead, they were there to test what they saw as a possible key to unlocking depths beyond even 310 meters: breathing hydrogen. Read the full story.

—Samantha Schuyler

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• A portrait of Simone Biles using thousands of dice, because why not?
• This simple practice could help you to feel more positive on a daily basis. Why not try it?
• Treat yourself to a juicy tomato sandwich this week.
• Scary stuff: New York is getting a colossal pigeon sculpture named Dinosaur.

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This story is from The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here.

When the generative AI boom started with ChatGPT in late 2022, we were sold a vision of superintelligent AI tools that know everything, can replace the boring bits of work, and supercharge productivity and economic gains.

Two years on, most of those productivity gains haven’t materialized. And we’ve seen something peculiar and slightly unexpected happen: People have started forming relationships with AI systems. We talk to them, say please and thank you, and have started to invite AIs into our lives as friends, lovers, mentors, therapists, and teachers.

We’re seeing a giant, real-world experiment unfold, and it’s still uncertain what impact these AI companions will have either on us individually or on society as a whole, argue Robert Mahari, a joint JD-PhD candidate at the MIT Media Lab and Harvard Law School, and Pat Pataranutaporn, a researcher at the MIT Media Lab. They say we need to prepare for “addictive intelligence”, or AI companions that have dark patterns built into them to get us hooked. You can read their piece here. They look at how smart regulation can help us prevent some of the risks associated with AI chatbots that get deep inside our heads.

The idea that we’ll form bonds with AI companions is no longer just hypothetical. Chatbots with even more emotive voices, such as OpenAI’s GPT-4o, are likely to reel us in even deeper. During safety testing, OpenAI observed that users would use language that indicated they had formed connections with AI models, such as “This is our last day together.” The company itself admits that emotional reliance is one risk that might be heightened by its new voice-enabled chatbot.

There’s already evidence that we’re connecting on a deeper level with AI even when it’s just confined to text exchanges. Mahari was part of a group of researchers that analyzed a million ChatGPT interaction logs and found that the second most popular use of AI was sexual role-playing. Aside from that, the overwhelmingly most popular use case for the chatbot was creative composition. People also liked to use it for brainstorming and planning, asking for explanations and general information about stuff.

These sorts of creative and fun tasks are excellent ways to use AI chatbots. AI language models work by predicting the next likely word in a sentence. They are confident liars and often present falsehoods as facts, make stuff up, or hallucinate. This matters less when making stuff up is kind of the entire point. In June, my colleague Rhiannon Williams wrote about how comedians found AI language models to be useful for generating a first “vomit draft” of their material; they then add their own human ingenuity to make it funny.

But these use cases aren’t necessarily productive in the financial sense. I’m pretty sure smutbots weren’t what investors had in mind when they poured billions of dollars into AI companies, and, combined with the fact we still don’t have a killer app for AI,it’s no wonder that Wall Street is feeling a lot less bullish about it recently.

The use cases that would be “productive,” and have thus been the most hyped, have seen less success in AI adoption. Hallucination starts to become a problem in some of these use cases, such as code generation, news and online searches, where it matters a lot to get things right. Some of the most embarrassing failures of chatbots have happened when people have started trusting AI chatbots too much, or considered them sources of factual information. Earlier this year, for example, Google’s AI overview feature, which summarizes online search results, suggested that people eat rocks and add glue on pizza.

And that’s the problem with AI hype. It sets our expectations way too high, and leaves us disappointed and disillusioned when the quite literally incredible promises don’t happen. It also tricks us into thinking AI is a technology that is even mature enough to bring about instant changes. In reality, it might be years until we see its true benefit.

Now read the rest of The AlgorithmDeeper LearningAI “godfather” Yoshua Bengio has joined a UK project to prevent AI catastrophesYoshua Bengio, a Turing Award winner who is considered one of the godfathers of modern AI, is throwing his weight behind a project funded by the UK government to embed safety mechanisms into AI systems. The project, called Safeguarded AI, aims to build an AI system that can check whether other AI systems deployed in critical areas are safe. Bengio is joining the program as scientific director and will provide critical input and advice.

What are they trying to do: Safeguarded AI’s goal is to build AI systems that can offer quantitative guarantees, such as risk scores, about their effect on the real world. The project aims to build AI safety mechanisms by combining scientific world models, which are essentially simulations of the world, with mathematical proofs. These proofs would include explanations of the AI’s work, and humans would be tasked with verifying whether the AI model’s safety checks are correct. Read more from me here.

Bits and BytesGoogle DeepMind trained a robot to beat humans at table tennis

Researchers managed to get a robot wielding a 3D-printed paddle to win 13 of 29 games against human opponents of varying abilities in full games of competitive table tennis. The research represents a small step toward creating robots that can perform useful tasks skillfully and safely in real environments like homes and warehouses, which is a long-standing goal of the robotics community. (MIT Technology Review)

Are we in an AI bubble? Here’s why it’s complex.

There’s been a lot of debate recently, and even some alarm, about whether AI is ever going to live up to its potential, especially thanks to tech stocks’ recent nosedive. This nuanced piece explains why although the sector faces significant challenges, it’s far too soon to write off AI’s transformative potential. (Platformer)

How Microsoft spread its bets beyond OpenAI

Microsoft and OpenAI have one of the most successful partnerships in AI. But following OpenAI’s boardroom drama last year, the tech giant and its CEO, Satya Nadella, have been working on a strategy that will make Microsoft more independent of Sam Altman’s startup. Microsoft has diversified its investments and partnerships in generative AI, built its own smaller, cheaper models, and hired aggressively to develop its consumer AI efforts. (Financial Times)

Humane’s daily returns are outpacing sales

Oof. The extremely hyped AI pin, which was billed as a wearable AI assistant, seems to have flopped. Between May and August, more Humane AI Pins were returned than purchased. Infuriatingly, the company has no way to reuse the returned pins, so they become e-waste.(The Verge)

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Google DeepMind trained a robot to beat humans at table tennis

What’s new: Google DeepMind has trained a robot to play table tennis at the equivalent of amateur-level competitive performance, the company has announced. It claims it’s the first time a robot has been taught to play a sport with humans at a human level.

How good is it? The system is far from perfect. Although the table tennis bot was able to beat all beginner-level human opponents it faced and 55% of those playing at amateur level, it lost all the games against advanced players. Still, it’s an impressive advance.

Why it matters: The research represents a step towards creating robots that can perform useful tasks skillfully and safely in real environments like homes and warehouses, which is a long-standing goal of the robotics community. Read the full story.

—Rhiannon Williams

This futuristic space habitat is designed to self-assemble in orbit

More people are traveling to space, but the International Space Station can only hold 11 people at a time. The Aurelia Institute, a nonprofit space architecture lab based in Cambridge, MA, has an approach that may help: a habitat that can be launched in compact stacks of flat tiles and self-assemble in orbit.

Building large space habitats is difficult, and dangerous. But the Aurelia Institute’s TESSERAE space habitat, which resembles a futuristic, one-story-tall soccer ball, could make it much easier. Read the full story.

—Sarah Ward

Watch a video showing what happens in our brains when we think

What does a thought look like? We can think about thoughts resulting from shared signals between some of the billions of neurons in our brains. Various chemicals are involved, but it really comes down to electrical activity. We can measure that activity and watch it back.

Ben Rapoport is the cofounder and chief science officer of Precision Neuroscience, a company doing just that. Rapoport and his colleagues have developed thin, flexible electrode arrays that can be slipped under the skull through a tiny incision. Once inside, they can sit on a person’s brain, collecting signals from neurons buzzing away beneath.

So far, 17 people have had these electrodes placed onto their brains. And Rapoport has been able to capture how their brains form thoughts. Check out his video of the brain thinking.

—Jessica Hamzelou

This story is from The Checkup, our weekly newsletter giving you the inside track on all things health and biotech. Sign up to receive it in your inbox every Thursday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The ad group being sued by X is shutting down
It doesn’t have the cash to keep operating while fighting the company in court. (NYT $)
+ X had rejoined the group little more than a month ago, before relations soured. (Ars Technica)
+ But ultimately, the lawsuit is likely to drive even more advertisers from the platform. (The Guardian)

2 CRISPR gene-editing is being offered to British blood disorder patients
People with thalassaemia will receive the pioneering treatment for free. (BBC)
+ Controversial CRISPR scientist promises “no more gene-edited babies” until society comes around. (MIT Technology Review)

3 Donald Trump wants to be a TikTok star
Years after he failed to ban the app, the former president is embracing TikTok fame. (WP $)
+ But Kamala Harris is surging ahead in the meme wars. (Slate $)
+ Overseas accounts are pushing anti-Trump TikToks to Americans. (WSJ $)
+ US election officials are being targeted by hackers in Iran, too. (Reuters)

4 YouTube in Russia is living on borrowed time
The platform is experiencing mass outages, leaving entire regions unable to access it. (Reuters)

5 Inside the race to develop quantum cryptosystemsThe EU, US, China and India are all scrambling to create the new global standard. (IEEE Spectrum)
+ PsiQuantum plans to build the biggest quantum computing facility in the US. (MIT Technology Review)

6 AI search engine Perplexity’s popularity is soaringBut that doesn’t mean its results are always reliable. (FT $)
+ Why you shouldn’t trust AI search engines. (MIT Technology Review)

7 FTX has agreed to pay customers more than $12 billionIt’s a lot more than many experts ever thought its victims would receive.(Ars Technica)
+ It’s the largest ever recovery in the US regulator’s history. (The Guardian)

8 Computer crash reports are a treasure trove of valuable data
You’d better hope they don’t fall into the wrong hands. (Wired $)

9 The irony of paying for budgeting appsA word to the wise: you do not need to do this. (Vox)

10 Airbnb’s summer is going from bad to worse
Poor-quality listings and fights with city officials are just some of its problems. (NY Mag $)
+ Travel in China is going from strength to strength. (Bloomberg $)

Quote of the day

“I managed to mess up a Pikachu with mind control, which was pretty fun.”

—Twitch streamer Perri Karyal describes using a brain-computer interface to play Super Smash Bros to the Guardian.

The big story

After 25 years of hype, embryonic stem cells are still waiting for their moment​

August 2023

In 1998, researchers isolated powerful stem cells from human embryos. It was a breakthrough for biology, since these cells are the starting point for human bodies and have the capacity to turn into any other type of cell—heart cells, neurons, you name it.

National Geographic would later summarize the incredible promise: “the dream is to launch a medical revolution in which ailing organs and tissues might be repaired” with living replacements. It was the dawn of a new era. A holy grail. Pick your favorite cliché—they all got airtime.

Yet today, more than two decades later, there are no treatments on the market based on these cells. Not one. Our biotech editor Antonio Regalado set out to investigate why, and when that might change. Here’s what he discovered.

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• If you’ve never seen the digestive tissues of a coral polyp before, now you have.
• I’m sad to report the death of a legend—Jack Karlson, the original Australian meme, has gone to the succulent Chinese restaurant in the sky.
• Finally, today is the Olympic event everyone’s been waiting for: breakdancing!
• What it means to place fourth at the Olympics, and what us mortals can learn from it.

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Do you fancy your chances of beating a robot at a game of table tennis? Google DeepMind has trained a robot to play the game at the equivalent of amateur-level competitive performance, the company has announced. It claims it’s the first time a robot has been taught to play a sport with humans at a human level.

Researchers managed to get a robotic arm wielding a 3D-printed paddle to win 13 of 29 games against human opponents of varying abilities in full games of competitive table tennis. The research was published in an Arxiv paper.

The system is far from perfect. Although the table tennis bot was able to beat all beginner-level human opponents it faced and 55% of those playing at amateur level, it lost all the games against advanced players. Still, it’s an impressive advance.

“Even a few months back, we projected that realistically the robot may not be able to win against people it had not played before. The system certainly exceeded our expectations,” says Pannag Sanketi, a senior staff software engineer at Google DeepMind who led the project. “The way the robot outmaneuvered even strong opponents was mind blowing.”

And the research is not just all fun and games. In fact, it represents a step towards creating robots that can perform useful tasks skillfully and safely in real environments like homes and warehouses, which is a long-standing goal of the robotics community. Google DeepMind’s approach to training machines is applicable to many other areas of the field, says Lerrel Pinto, a computer science researcher at New York University who did not work on the project.

“I’m a big fan of seeing robot systems actually working with and around real humans, and this is a fantastic example of this,” he says. “It may not be a strong player, but the raw ingredients are there to keep improving and eventually get there.”

To become a proficient table tennis player, humans require excellent hand-eye coordination, the ability to move rapidly and make quick decisions reacting to their opponent—all of which are significant challenges for robots. Google DeepMind’s researchers used a two-part approach to train the system to mimic these abilities: they used computer simulations to train the system to master its hitting skills; then fine tuned it using real-world data, which allows it to improve over time.

The researchers compiled a dataset of table tennis ball states, including data on position, spin, and speed. The system drew from this library in a simulated environment designed to accurately reflect the physics of table tennis matches to learn skills such as returning a serve, hitting a forehand topspin, or backhand shot. As the robot’s limitations meant it could not serve the ball, the real-world games were modified to accommodate this.

During its matches against humans, the robot collects data on its performance to help refine its skills. It tracks the ball’s position using data captured by a pair of cameras, and follows its human opponent’s playing style through a motion capture system that uses LEDs on its opponent’s paddle. The ball data is fed back into the simulation for training, creating a continuous feedback loop.

This feedback allows the robot to test out new skills to try and beat its opponent—meaning it can adjust its tactics and behavior just like a human would. This means it becomes progressively better both throughout a given match, and over time the more games it plays.

The system struggled to hit the ball when it was hit either very fast, beyond its field of vision (more than six feet above the table), or very low, because of a protocol that instructs it to avoid collisions that could damage its paddle. Spinning balls proved a challenge because it lacked the capacity to directly measure spin—a limitation that advanced players were quick to take advantage of.

Training a robot for all eventualities in a simulated environment is a real challenge, says Chris Walti, founder of robotics company Mytra and previously head of Tesla’s robotics team, who was not involved in the project.

“It’s very, very difficult to actually simulate the real world because there’s so many variables, like a gust of wind, or even dust [on the table]” he says. “Unless you have very realistic simulations, a robot’s performance is going to be capped.”

Google DeepMind believes these limitations could be addressed in a number of ways, including by developing predictive AI models designed to anticipate the ball’s trajectory, and introducing better collision-detection algorithms.

Crucially, the human players enjoyed their matches against the robotic arm. Even the advanced competitors who were able to beat it said they’d found the experience fun and engaging, and said they felt it had potential as a dynamic practice partner to help them hone their skills.

“I would definitely love to have it as a training partner, someone to play some matches from time to time,” one of the study participants said.

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This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

What does a thought look like? We can think about thoughts resulting from shared signals between some of the billions of neurons in our brains. Various chemicals are involved, but it really comes down to electrical activity. We can measure that activity and watch it back.

Earlier this week, I caught up with Ben Rapoport, the cofounder and chief science officer of Precision Neuroscience, a company doing just that. It is developing brain-computer interfaces that Rapoport hopes will one day help paralyzed people control computers and, as he puts it, “have a desk job.”

Rapoport and his colleagues have developed thin, flexible electrode arrays that can be slipped under the skull through a tiny incision. Once inside, they can sit on a person’s brain, collecting signals from neurons buzzing away beneath. So far, 17 people have had these electrodes placed onto their brains. And Rapoport has been able to capture how their brains form thoughts. He even has videos. (Keep reading to see one for yourself, below.)

Brain electrodes have been around for a while and are often used to treat disorders such as Parkinson’s disease and some severe cases of epilepsy. Those devices tend to involve sticking electrodes deep inside the brain to access regions involved in those disorders.

Brain-machine interfaces are newer. In the last couple of decades, neuroscientists and engineers have made significant progress in developing technologies that allow them to listen in on brain activity and use brain data to allow people to control computers and prosthetic limbs by thought alone.

The technology isn’t commonplace yet, and early versions could only be used in a lab setting. Scientists like Rapoport are working on new devices that are more effective, less invasive, and more practical. He and his colleagues have developed a miniature device that fits 1,024 tiny electrodes onto a sliver of ribbon-like film that’s just 20 microns thick—around a third of the width of a human eyelash.

The vast majority of these electrodes are designed to pick up brain activity. The device itself is designed to be powered by a rechargeable battery implanted under the skin in the chest, like a pacemaker. And from there, data could be transmitted wirelessly to a computer outside the body.

Unlike other needle-like electrodes that penetrate brain tissue, Rapoport says his electrode array “doesn’t damage the brain at all.” Instead of being inserted into brain tissue, the electrode arrays are arranged on a thin, flexible film, fed through a slit in the skull, and placed on the surface of the brain.

From there, they can record what the brain is doing when the person thinks. In one case, Rapoport’s team inserted their electrode array into the skull of a man who was undergoing brain surgery to treat a disease. He was kept awake during his operation so that surgeons could make sure they weren’t damaging any vital regions of his brain. And all the while, the electrodes were picking up the electrical signals from his neurons.

This is what the activity looked like:

“This is basically the brain thinking,” says Rapoport. “You’re seeing the physical manifestation of thought.”

In this video, which I’ve converted to a GIF, you can see the pattern of electrical activity in the man’s brain as he recites numbers. Each dot represents the voltage sensed by an electrode on the array on the man’s brain, over a region involved in speech. The reds and oranges represent higher voltages, while the blues and purples represent lower ones. The video has been slowed down 20-fold, because “thoughts happen faster than the eye can see,” says Rapoport.

This approach allows neuroscientists to visualize what happens in the brain when we speak—and when we plan to speak. “We can decode his intention to say a word even before he says it,” says Rapoport. That’s important—scientists hope technologies will interpret these kinds of planning signals to help some individuals communicate.

For the time being, Rapoport and his colleagues are only testing their electrodes in volunteers who are already scheduled to have brain surgery. The electrodes are implanted, tested, and removed during a planned operation. The company announced in May that the team had broken a record for the greatest number of electrodes placed on a human brain at any one time—a whopping 4,096.

Rapoport hopes the US Food and Drug Administration will approve his device in the coming months. “That will unlock … what we hope will be a new standard of care,” he says.

Now read the rest of The CheckupRead more from MIT Technology Review’s archivePrecision Neuroscience is one of a handful of companies leading the search for a new brain-computer interface. Cassandra Willyard covered the key players in a recent edition of the Checkup.

Brain implants can do more than treat disease or aid communication. They can change a person’s sense of self. This was the case for Rita Leggett, who was devastated when her implant was removed against her will. I explored whether experiences like these should be considered a breach of human rights in a piece published last year.

Ian Burkhart, who was paralyzed as a result of a diving accident, received a brain implant when he was 24 years old. Burkhart learned to use the implant to control a robotic arm and even play Guitar Hero. But funding issues and an infection meant the implant had to be removed. “When I first had my spinal cord injury, everyone said: ‘You’re never going to be able to move anything from your shoulders down again,’” Burkhart told me last year. “I was able to restore that function, and then lose it again. That was really tough.”

A couple of years ago, a brain implant allowed a locked-in man to communicate in full sentences by thought alone—a world first, the researchers claimed. He used it to ask for soup and beer, and to tell his carers “I love my cool son.”

Electrodes that stimulate the brain could be used to improve a person’s memory. The “memory prosthesis,” which has been designed to mimic the way our brains create memories, appears to be most effective in people who have poor memories to begin with.

From around the webDo you share DNA with Ludwig van Beethoven, or perhaps a Viking? Tests can reveal genetic links, but they are not always clear, and the connections are not always meaningful or informative. (Nature)

This week marks 79 years since the United States dropped atomic bombs on Hiroshima and Nagasaki. Survivors share their stories of what it’s like to live with the trauma, stigma, and survivor’s guilt caused by the bombs—and why weapons like these must never be used again. (New York Times)

At least 19 Olympic athletes have tested positive for covid-19 in the past two weeks. The rules allow them to compete regardless. (Scientific American)

Honey contains a treasure trove of biological information, including details about the plants that supplied the pollen and the animals and insects in the environment. It can even tell you something about the bees’ “micro-bee-ota.” (New Scientist)

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Several million people were listening in February when Joe Rogan falsely declared that “party drugs” were an “important factor in AIDS.” His guest on The Joe Rogan Experience, the former evolutionary biology professor turned contrarian podcaster Bret Weinstein, agreed with him: The “evidence” that AIDS is not caused by HIV is, he said, “surprisingly compelling.”

During the show, Rogan also asserted that AZT, the earliest drug used in the treatment of AIDS, killed people “quicker” than the disease itself—another claim that’s been widely repeated even though it is just as untrue.

Speaking to the biggest podcast audience in the world, the two men were promoting dangerous and false ideas—ideas that were in fact debunked and thoroughly disproved decades ago.

But it wasn’t just them. A few months later, the New York Jets quarterback Aaron Rodgers, four-time winner of the NFL’s MVP award, alleged that Anthony Fauci, who led the National Institute of Allergy and Infectious Diseases for 38 years, had orchestrated the government’s response to the AIDS crisis for personal gain and to promote AZT, which Rodgers also depicted as “killing people.” Though he was speaking to a much smaller audience, on a podcast hosted by a jujitsu fighter turned conspiracy theorist, a clip of the interview was re-shared on X, where it’s been viewed more than 13 million times.

Rodgers was repeating claims that appear in The Real Anthony Fauci, a 2021 book by Robert F. Kennedy Jr.—a work that has renewed relevance as the anti-vaccine activist makes a long-shot but far-from-inconsequential run for the White House. The book, which depicts the elderly immunologist as a Machiavellian figure who used both the AIDS and covid pandemics for his own ends, has reportedly sold 1.3 million copies across all formats.

“When I hear [misinformation] like that, I just hope it doesn’t get traction,” says Seth Kalichman, a professor of psychology at the University of Connecticut and the author of Denying AIDS: Conspiracy Theories, Pseudoscience, and Human Tragedy.

But it already has. These comments and others like them add up to a small but unmistakable resurgence in AIDS denialism—a false collection of theories arguing either that HIV doesn’t cause AIDS or that there’s no such thing as HIV at all.

The ideas here were initially promoted by a cadre of scientists from unrelated fields, as well as many science-adjacent figures and self-proclaimed investigative journalists, back in the 1980s and ’90s. But as more and more evidence stacked up against them, and as more people with HIV and AIDS started living longer lives thanks to effective new treatments, their claims largely fell out of favor.

At least until the coronavirus arrived.

The covid-19 pandemic brought together people with a mistrust of institutions to rally and march against masks and vaccines.SPENCER PLATT/GETTY IMAGESFollowing the pandemic, a renewed suspicion of public health figures and agencies is giving new life to ideas that had long ago been pushed to the margins. And the impact is far from confined to the dark corners of the web. Arguments spreading rapidly online are reaching millions of people—and, in turn, potentially putting individual patients at risk. The fear is that AIDS denialism could once again spread in the way that covid denialism has: that people will politicize the illness, call its most effective and evidence-based treatments into question, and encourage extremist politicians to adopt these views as the basis for policy. And if it continues to build, this movement could threaten the bedrock knowledge about germs and viruses that underpin the foundation of modern health care and disease prevention, creating dangerous confusion among the public at a deeply inopportune time.

Before they promoted bunk information on HIV and AIDS, Rogan, Kennedy, and Rodgers were spreading fringe theories about the coronavirus’s origins, as well as loudly questioning basic public health measures like vaccines, social distancing, and masks. All three men have also boosted the false idea that ivermectin, an antiparasitic drug, is a treatment or preventative for covid that is being kept from the American public for sinister reasons at the behest of Big Pharma.

“The AIDS denialists have come from the covid denialists,” says Tara Smith, an infectious-disease epidemiologist and a professor at Kent State University’s College of Public Health, who tracks conspiratorial narratives about illness and public health. She saw them emerging first in social media groups driven by covid skepticism, with people asking, as she puts it, “If covid doesn’t exist, what else have we been lied to about?”

“Unlike HIV, covid impacted everybody, and the policy decisions that were made around covid impacted everybody.”

The covid pandemic was a particularly fertile ground for such suspicion, Kalichman notes, because “unlike HIV, covid impacted everybody, and the policy decisions that were made around covid impacted everybody.”

“The covid phenomenon—not the pandemic but the phenomenon around it—created this opportunity for AIDS denialists to reemerge,” he adds. Denialists like Peter Duesberg, the now-infamous Berkeley biologist who first promoted the idea that AIDS is caused by pharmaceuticals or recreational drugs, and Celia Farber and Rebecca V. Culshaw, an independent journalist and researcher, respectively, who have both written critically about what they see as the “official” narrative of HIV/AIDS. (Farber tells MIT Technology Review that she uses the term “AIDS dissent” rather than “denialism”: “‘Denialism’ is a religious and vituperative word.” )

In addition to the renewed skepticism toward public health institutions, the reanimated AIDS denialist movement is being supercharged by technological tools that didn’t exist the first time around: platforms with gigantic reach like X, Substack, Amazon, and Spotify, as well as newer ones that don’t have specific moderation policies around medical misinformation, like Rumble, Gab, and Telegram.

Spotify, for one, has largely declined to curb or moderate Rogan in any meaningful way, while also paying him an eye-watering amount of money; the company inked a $250 million renewal deal with him in February, just weeks before he and Weinstein made their false remarks about AIDS. Amazon, meanwhile, is currently offering Duesberg’s long-out-of-print 1996 book Inventing AIDS for free with a trial of its Audible program, and three of Culshaw’s books are available for free with either an Audible or Kindle Unlimited trial. Farber, meanwhile, has a Substack with more than 28,000 followers.

Now 87 years old and no longer actively speaking publicly, Peter Duesberg’s decades-old theories about AIDS are finding new life online.AP PHOTO/SUSAN RAGAN(Spotify, Substack, Rumble, and Telegram did not respond to requests for comment, while Meta and Amazon confirmed receipt of a request for comment but did not answer questions, and X’s press office provided only an auto-response. An email to Gab’s press address was returned as undeliverable.)

While this wave of AIDS denialism doesn’t currently have the reach and influence that the movement had in the past, it still has potentially serious consequences for patients as well as the general public. If these ideas gain enough traction, particularly among elected officials, they could endanger funding for AIDS research and treatments. Public health researchers are still haunted by the period in the 1990s and early 2000s when AIDS denial became official policy in South Africa; one analysis estimates that between just 2000 and 2005, more than 300,000 people died prematurely as a result of the country’s bad public health policies. On an individual level, there could also be devastating results if people with HIV are discouraged from seeking treatment or from trying to prevent the virus’s spread by taking medication or using condoms; a 2010 study has shown that a belief in denialist rhetoric among people with HIV is associated with medication refusal and poor health outcomes, including increased incidence of hospitalization, HIV-related symptoms, and detectable viral loads.

Above all, the revival of this particular slice of medical misinformation is another troubling sign for the ways that tech platforms can deepen distrust in our public health system. The same tech-savvy denialist playbook is already being deployed in the wider “health freedom” space to create confusion and suspicion around other serious diseases, like measles, and to challenge more foundational claims about the science of viruses—that is, to posit that viruses don’t exist at all, or are harmless and can’t cause illness. (A Gab account solely dedicated to the idea that all viruses are hoaxes has more than 3,000 followers.)

As Smith puts it, “We are not in a good place regarding [trust in] all of our public health institutions right now.”

Capitalizing on confusionOne reason AIDS and covid denialists have been able to build similar and interlocking movements that inveigh against government science is that the early days of the two viruses were markedly similar: full of confusion, mystery, and skepticism.

In 1981, James Curran served on a task force investigating the first five known cases of what was then a novel disease. “There were a lot of theories about what caused it,” says Curran, an epidemiologist who is now a dean emeritus at Emory University’s Rollins School of Public Health and previously spent 25 years working at the US Centers for Disease Control and Prevention, serving ultimately as the assistant surgeon general. He and his colleagues had all previously studied sexually transmitted infections that affected gay men and people who injected drugs. With that context, the researchers saw the early patterns of the disease as “indicative of a likely sexually transmissible agent.”

Not everyone agreed, Curran says: “Other people saw poppers or other drugs or accumulation of semen or environmental factors. Some of these things came from the backgrounds that people had, or they came from the simple denial that it could possibly be a new virus.”

The first wave of contrarian ideas about AIDS, then, was less true “denialism” and more the understandable confusion and differences of opinion that can emerge around a new disease. Yet as time went on, “the death rates were increasing dramatically,” says Lindsay Zafir, a distinguished lecturer in anthropology and interdisciplinary programs at the City College of New York who wrote her dissertation on the emergence and evolution of AIDS denialism. “Some people started to wonder whether scientists actually knew what they were doing.”

This led to the emergence of a wider round of more deliberate AIDS disinformation, which was picked up by mainstream publications. In the late 1980s, Spin magazine printed a series of stories that platformed denialist ideas and figures, including interviews with Duesberg, who’d already gained attention for his arguments that AIDS was caused by pharmaceutical drugs and not by HIV. The magazine also published pieces by Farber, a journalist who has described herself becoming progressively more sympathetic to the AIDS denialist cause after interviewing Duesberg. In 1991, theLos Angeles Timespublished a piece that asked whether Duesberg was “a hero or a heretic” for his “controversial” arguments about AIDS.

The tides began to turn only in 1995, when the first generation of antiretroviral therapies emerged to treat AIDS and deaths finally, mercifully, began to drop across the United States.

“Mbeki famously said, Your scientist says this, mine says that—which scientist is right? When that confusion exists, that’s the real vulnerability.”

Still, the denialist movement continued to grow, with next-generation leaders who were, like Duesberg and Farber, publicity savvy and (perhaps unsurprisingly) quick adopters of the earliest versions of the internet. This notably included Christine Maggiore, who was HIV-positive herself and who founded the group Alive & Well AIDS Alternatives. Long before social media, she and her peers used the internet to foster community, offering links on their websites to hotlines and in-person meetings.

Kent State’s Smith and Steven P. Novella, now a clinical neurologist and associate professor at Yale, wrote a paper in 2007 about how the internet had become a powerful force for AIDS denialism. It was “a fertile and unrefereed medium” for denialist ideas and one of just a few common tools to make counterarguments in the face of the widespread scientific agreement on AIDS that dominated medical literature.

Around this time, Farber wrote another big piece, this time in Harper’s,on the so-called AIDS dissidents, which in turn generated a firestorm of criticism and corrections and revived the debate for a new era of readers.

“It’s hard to quantify how much influence those types of people had,” Smith says. She points out that Maggiore was even promoted by Nate Mendel of the Foo Fighters. “It’s hard to know how many people followed her advice,” Smith emphasizes. “But certainly a lot of people heard it.”

Former South African president Thabo Mbeki enacted AIDS denialism as part of his public policy, denying patients in the country access to antiretroviral drugs.MAKSIM BLINOV/SPUTNIK VIA AP IMAGESIn a devastating turn, one of those people was Thabo Mbeki, who became the second democratically elected president of South Africa in 1999. Mbeki was skeptical of antiretrovirals to treat AIDS, and as the Lancet points out, both Mbeki and his health minister promoted the work of Western AIDS skeptics. In the summer of 2000, Mbeki hosted a presidential advisory panel that included denialists like Duesberg; Farber tells MIT Technology Review that she was also present. Just a few weeks later, the South African president met privately with Maggiore.

Curran, the former CDC official, visited South Africa during this era and remembers how officials “said they would throw doctors in jail” if they provided AZT to pregnant women.

“Mbeki famously said, Your scientist says this, mine says that—which scientist is right?” Kalichman says. “When that confusion exists, that’s the real vulnerability.”

Mbeki left office in 2008. And while AIDS denialism didn’t exactly disappear by the 2010s, it did largely recede into relative obscurity, beaten back by clear evidence that antiretroviral drugs were working.

There were also meticulous fact-based campaigns from groups like AIDSTruth, which was founded following Farber’s 2006 Harper’s article. This group gained traction online, systematically debunking arguments from denialists on a bare-bones website and using hyperlinks to guide people quickly to science-based material on each point—a somewhat novel approach at the time.

By 2015, the decline of denialism was so complete that AIDSTruth stopped active work, believing that its mission was complete. The group wrote, “We have long since reached the point where we—the people who have in one way or another been involved in running this website—believe that AIDS denialism died as an effective political force.”

Of course, it didn’t take too long to see the work was far from complete.

Growing the “beehive”Kalichman, from the University of Connecticut, has compared the world of AIDS denial to a “beehive”: It looks like a chaotic mix of people pursuing bad science and debunked ideas for their own particular ends. But if you look closer, what appears to be a swarm is actually “very well organized.” The modern, post-covid variety is no different.

The new wave of denialists often don’t count their theories on AIDS as their sole pseudoscientific interest; rather, it’s part of a whole bouquet of bad ideas.

Robert F. Kennedy Jr. has been vocal in his support of anti-vaccine causes long before his current bid for president.AP PHOTO/TED S. WARRENThese individuals seem to have arrived at revisionist and denialist ideas through a broad-based skepticism of public health, a rejection of what they see as Big Pharma’s meddling, and a particular, visceral disgust toward Fauci. Kennedy, specifically, attributes almost superhuman powers to Fauci, claiming in one 2022 tweet—referencing the Mafia code of silence—that he “purchased omertà among virologists globally with a total of $37 billion in annual payoffs in research grants.” The tweet has been liked more than 26,000 times.

Kennedy’s book “changed everything,” Celia Farber says. “I answered his questions … and was included and quoted in the book. This led to a chance for me to once again be a professional writer, on Substack.”

The new guard has also been comfortable reviving the oldest debunked ideas. Both Rogan and Kennedy, for instance, have claimed that poppers could be the cause of AIDS. “A hundred percent of the people who died in the first thousand [with] AIDS were people who were addicted to poppers, which are known to cause Kaposi sarcoma in rats,” Kennedy told an audience in a speech whose date isn’t clear; a video of the remarks has recently been circulating widely. “And they were people who were part of a gay lifestyle where they were burning the candle at both ends.” (Kennedy’s presidential campaign did not respond to a request for comment.)

Some have even given fresh life to the old guard. Duesberg is now 87 and is no longer active in the public sphere (and his wife told MIT Technology Review that his health did not allow him to sit for an interview or answer questions via email). But the basic shape of his arguments—obfuscating the causes of AIDS, the treatments, and the nature of the disease itself—continue to live on. Rogan actually hosted Duesberg on his podcast in 2012, a decision that generated relatively few headlines at the time—likely because Rogan hadn’t yet become so popular and America’s crisis of disinformation and medical distrust was less pronounced. Rogan and Weinstein praised Duesberg in their recent conversation, asserting that he’d been “demonized” for his arguments aboutAZT. (Weinstein did not respond to a request for comment.Several attempts to reach Spotify through multiple channels did not get responses. Attempts to reach Rogan through Spotify and one of his producers also did not receive responses.)

Before Rodgers spoke falsely about AIDS and AZT, he and the Green Bay Packers were fined for conduct in violation of the NFL’s covid policies.SARAH STIER/GETTY IMAGESThe support seems to largely go both ways. Culshaw has written that even critical stories about Rodgers are helpful to the cause: “The more hit pieces are published, the more the average citizen—especially the average post-covid citizen—will become curious and begin to look into the issue. And once you’ve looked into it far enough, you cannot unsee what you’ve seen.”

Culshaw and Farber have also been empowered by the new ability to command their own megaphones online. Farber, for instance, is now primarily active on Substack, with a newsletter that is a mix of HIV/AIDS content and general conspiracy theorizing. Her current work refers to HIV/AIDS as a “PSY OP” (caps hers); she presents herself as a soldier in a long war against government propaganda, one in which covid is the latest salvo.

Farber says she sees her arguments gaining ground. “What’s happening now is that the general public are learning about the buried history,” she writes to MIT Technology Review. “People are very interested in the HIV ‘thing’ these days, to my eternal astonishment,” she adds, writing that Kennedy’s book “changed everything.” She says, “I answered his questions about HIV war history and was included and quoted in the book. This led to a chance for me to once again be a professional writer, on Substack.”

Culshaw (who now uses the name Culshaw Smith) strikes a similar tone, though she is a less prominent figure. A mathematician and self-styled HIV researcher, she published her first book in 2007; it claimed to use mathematical evidence to prove that HIV doesn’t cause AIDS.

In 2023 she published another AIDS denial book, this one with Skyhorse, a press that traffics heavily in conspiracy theories and pseudoscience, and which published Kennedy’s book on Fauci. She gained some level of notoriety when the book was distributed by publishing giant Simon & Schuster, leading to protests outside its headquarters from theLGBT rights advocacy groups GLAAD and ACT UP NY. Though Simon & Schuster appears to continue to distribute the book, that pushback has provided the basis for her new act: life after “cancellation.” She produced a short memoir last year that describes the furor—a history Culshaw presents as a dramatic moment in the suppression of AIDS truth. This is one of the books now available for free on Amazon through a Kindle Unlimited trial. (Simon & Schuster did not respond to a request for comment. Culshaw did not respond to a request for comment sent through Substack.)

The argument that she’s been “canceled” by the scientific establishment holds tremendous sway with disease denialists online, who are always eager to seize on cases where they perceive the government to be repressing and censoring “alternative” views. In May, Chronicles, an online right-wing magazine, approvingly tied together Rodgers with the broader web of AIDS denialists, including Culshaw, Duesberg, and others—holding them up as heroic figures who’d been unfairly dismissed as “conspiracy theorists” and who’d done well to challenge medical expertise that the magazine denigrated as “white coat supremacy.” (A request for comment for Rodgers through a representative did not receive a response.)

Platforming denialAIDS denialism and revisionism are resurging in the midst of bitter ongoing arguments over what kinds of things should be allowed to exist on online platforms. Spotify, for instance, has clear rules that prohibit “asserting that AIDS, COVID-19, cancer or other serious life threatening diseases are a hoax or not real,” and specific rules against “dangerous and deceptive content” that are both thoughtful and clearly articulated. Yet Rogan’s program seems to be exempt from these rules or manages to skirt them; after all, he and Weinstein did not suggest that AIDS isn’t real, per se, but instead promoted debunked ideas about its cause.

While Amazon and Meta have misinformation policies of some kind, they clearly do not prevent AIDS denial books from being sold or denialist arguments from being shared. (Amazon also has content guidelines for books that ban obvious things like hate speech, pornography, or the promotion of terrorism, but they do not specifically mention medical misinformation.)

The difficulty of policing false or unproven health information across all these different platforms, in all the forms it can take, is immense. In 2019, for instance, Facebook allowed misleading ads from personal injury lawyers claiming that PrEP, or pre-exposure prophylaxis drugs, can cause bone and kidney damage; it took action only after a sustained outcry from LGBT groups.

“It’s one of those things that either plants seeds of doubt or encourages those to grow if they’re already there.”

In a sign of how entrenched some of these things can be, there’s a YouTube channel originally called Rethinking AIDS—now known as Question Everything—that has been active for 14 years, sharing interviews with denialists. The channel has 16,000 subscribers, and its most popular videos have upwards of half a million views. Another page, devoted to a conspiratorial documentary about AIDS, has been active since 2009, and its most popular video has nearly 300,000 views. (A YouTube spokesperson tells MIT Technology Review it has “developed our approach to medical misinformation over many years, in close alignment with health authorities around the world” and that it prominently features “content and information from high-quality health sources … in search results and recommendations related to HIV/AIDS.”)

Meanwhile, on platforms like the Elon Musk–owned X, formerly known as Twitter, there is little moderation happening at all. The company removed its ban on covid misinformation in 2022, to almost immediate effect: misinformation and propaganda of all kinds has flourished, including HIV/AIDS denial. One widely circulated video depicts the late biochemist Kary Mullis talking about the moment he first “really questioned” the predominant HIV narrative.

Complementing these more established spaces are newer, more niche platforms like Rumble and Telegram, which don’t have any moderation policies to address medical misinformation and proudly tout a commitment to free speech that means they do very little about any kind of misinformation at all, no matter how noxious.

Joe Rogan’s podcast, with an audience of 14.5 million just on Spotify, has hosted a number of guests expressing anti-vaccine sentiments.PHOTO ILLUSTRATION BY CINDY ORD/GETTY IMAGESTelegram, which is one of the most popular messaging apps in Russia, does have a general “verified information” policy. The statement of this policy links to a post by its CEO, Pavel Durov, that says “spreading the truth will always be a more efficient strategy than engaging in censorship.” Discussions of HIV among Telegram’s current and most active misinformation peddlers often compare it to covid, characterizing both as “manufactured” viruses. One widely shared post by the anti-vaccine activist Sherri Tenpenny claims that covid-19 was created by “splicing” HIV into a coronavirus to “inflict maximum harm,” a bizarre lie that’s also meant to strengthen the unproven idea that covid was created in a lab. Telegram is also a fertile ground for sharing phony HIV cures; one group with 43,000 followers has promoted an oil that it claims is used in Nigeria.

When YouTube began to crack down on medical misinformation during the height of the pandemic, conservative and conspiratorial content creators went to Rumble instead. The company claims it saw a 106% revenue increase last year and now has an average of 67 million monthly active users. A clip of Rogan talking about Duesberg’s AIDS-related claims has racked up 30,000 views in the last two years, and an interview with Farber by Joseph Mercola, a major player in the natural-health and anti-vaccine worlds, has gotten more than 300,000 views since it was posted there earlier this year.

The concern with these kinds of falsehoods, Smith says, is always that patient populations, communities at high risk for HIV, or populations with real histories of medical mistreatment, like Black and Native people, “think there might be a grain of truth and start to doubt if they need to be tested or continue treatment or things like that.” She adds, “It’s one of those things that either plants seeds of doubt or encourages those to grow if they’re already there.”

But it’s far more concerning when people like Rogan, who have a massive reach, take up the cause. “They just have such a huge platform, and those stories are scary and they spread,” Smith says. “Once they do that, it’s so hard for scientists to fight that.”

The offline impact For all the work AIDS denialists are doing to try to grow their numbers, Kalichman remains hopeful that they’re unlikely to make significant inroads. The most profound reason, he believes, is that many people now know someone living with HIV—a friend, a family member, a celebrity. As a result, many more people are directly familiar with how life-altering current HIV treatments have been.

“This isn’t the ’90s,” he says. “People are taking one pill once a day and living really healthy lives. If a person with HIV smokes, they’re much more likely to die of a smoking-related illness [than HIV] if their HIV Is being treated.”

Even the much stranger and more esoteric “terrain theory” seems to be making a modest comeback in alternative online spaces; the idea is that germs don’t cause illness in a healthy person whose “terrain” is sound thanks to vitamins, exercise, and sunlight.

Yet the risk doesn’t necessarily hang solely on how many people buy into the false information—but who does. Among people who have been studying AIDS denialism for decades, the biggest concern is ultimately that someone in public office will take notice and begin formally acting on those ideas. If that happens, Curran, the former assistant surgeon general, worries it could jeopardize funding for PEPFAR (the United States President’s Emergency Plan for AIDS Relief), the enormously successful public health program that has supported HIV testing, prevention, and treatment in lower-resource countries since the George W. Bush administration.

The current political environment further exacerbates the risk: Donald Trump has said that if he is elected again, he will cut federal funding to schools with mask or vaccine mandates, and Florida’s surgeon general, Joseph Ladapo, allowed parents to continue sending unvaccinated kids to school in the midst of a measles outbreak.

All it takes, Kalichman says, is for “someone who’s sitting in a policymaker’s chair in a state health department” to take AIDS denial arguments seriously. “A lot of damage can be done.” (He expresses relief, however, that Trump and his wing of the Republican Party have not yet taken up the particular cause of AIDS denialists: “Thank goodness.”)

Florida Surgeon General Joseph Ladapo’s letter to parents during a measles outbreak ran counter to the CDC’s recommended guidelines.AP PHOTO/CHRIS O’MEARAThen there is the fact that the same kind of denialist campaign is already being deployed with other diseases. Christiane Northrup, a former ob-gyn and a significant figure in natural health and related conspiratorial thinking, has recently been on Telegram sharing an old lie that a German court ruled the measles virus “does not exist.” (Northrup did not respond to a request for comment.)

On its own, if it were just bunk HIV theories recirculating, “I wouldn’t be as worried about it,” Smith says. “But in this broader anti-covid, anti-vaccine, and everything about germ theory being denied—that’s what worries me.”

By trying to effectively decouple cause and effect—claiming that HIV doesn’t cause AIDS, that measles isn’t caused by a virus and is instead a vitamin deficiency or caused by the MMR (measles, mumps, and rubella) vaccine itself—these movements discourage people from treating or trying to prevent serious and contagious illnesses. They try to sow doubt about the very nature of viruses themselves, a global gesture toward doubt, distrust, and minimization of serious diseases. Even the much stranger and more esoteric “terrain theory” seems to be making a modest comeback in alternative online spaces; the idea is that germs don’t cause illness in a healthy person whose “terrain” is sound thanks to vitamins, exercise, and sunlight.

These kinds of false claims, Smith points out, are resurging at a particularly inopportune time, when the public health world is already trying to prepare for the next pandemic. “We’re out of the emergency mode of the covid pandemic and trying to repair some of the damage to public health,” she says, “and thinking about another one.”

Curran also has a larger, more existential concern when he considers the lessons of the AIDS and covid pandemics: “The problem is, if you bad-mouth Fauci and his successors so much, the next epidemic people come around and they say, ‘Why should we trust these people?’ And the question is, who do we trust?

“When bird flu gets out of cows and goes to humans, are we going to go to Joe Rogan for the answers?”

Anna Merlan is a senior reporter at Mother Jones and the author of the 2019 book Republic of Lies: American Conspiracy Theorists and Their Surprising Rise to Power.

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Yoshua Bengio, a Turing Award winner who is considered one of the “godfathers” of modern AI, is throwing his weight behind a project funded by the UK government to embed safety mechanisms into AI systems.

The project, called Safeguarded AI, aims to build an AI system that can check whether other AI systems deployed in critical areas are safe. Bengio is joining the program as scientific director and will provide critical input and scientific advice. The project, which will receive £59 million over the next four years, is being funded by the UK’s Advanced Research and Invention Agency (ARIA), which was launched in January last year to invest in potentially transformational scientific research.

Safeguarded AI’s goal is to build AI systems that can offer quantitative guarantees, such as a risk score, about their effect on the real world, says David “davidad” Dalrymple, the program director for Safeguarded AI at ARIA. The idea is to supplement human testing with mathematical analysis of new systems’ potential for harm.

The project aims to build AI safety mechanisms by combining scientific world models, which are essentially simulations of the world, with mathematical proofs. These proofs would include explanations of the AI’s work, and humans would be tasked with verifying whether the AI model’s safety checks are correct.

Bengio says he wants to help ensure that future AI systems cannot cause serious harm.

“We’re currently racing toward a fog behind which might be a precipice,” he says. “We don’t know how far the precipice is, or if there even is one, so it might be years, decades, and we don’t know how serious it could be … We need to build up the tools to clear that fog and make sure we don’t cross into a precipice if there is one.”

Science and technology companies don’t have a way to give mathematical guarantees that AI systems are going to behave as programmed, he adds. This unreliability, he says, could lead to catastrophic outcomes.

Dalrymple and Bengio argue that current techniques to mitigate the risk of advanced AI systems—such as red-teaming, where people probe AI systems for flaws—have serious limitations and can’t be relied on to ensure that critical systems don’t go off-piste.

Instead, they hope the program will provide new ways to secure AI systems that rely less on human efforts and more on mathematical certainty. The vision is to build a “gatekeeper” AI, which is tasked with understanding and reducing the safety risks of other AI agents. This gatekeeper would ensure that AI agents functioning in high-stakes sectors, such as transport or energy systems, operate as we want them to. The idea is to collaborate with companies early on to understand how AI safety mechanisms could be useful for different sectors, says Dalrymple.

The complexity of advanced systems means we have no choice but to use AI to safeguard AI, argues Bengio. “That’s the only way, because at some point these AIs are just too complicated. Even the ones that we have now, we can’t really break down their answers into human, understandable sequences of reasoning steps,” he says.

The next step—actually building models that can check other AI systems—is also where Safeguarded AI and ARIA hope to change the status quo of the AI industry.

ARIA is also offering funding to people or organizations in high-risk sectors such as transport, telecommunications, supply chains, and medical research to help them build applications that might benefit from AI safety mechanisms. ARIA is offering applicants a total of £5.4 million in the first year, and another £8.2 million in another year. The deadline for applications is October 2.

The agency is also casting a wide net for people who might be interested in building Safeguarded AI’s safety mechanism through a nonprofit organization. ARIA is eyeing up to £18 million to set this organization up and will be accepting funding applications early next year.

The program is looking for proposals to start a nonprofit with a diverse board that encompasses lots of different sectors in order to do this work in a reliable, trustworthy way, Dalrymple says. This is similar to what OpenAI was initially set up to do before changing its strategy to be more product- and profit-oriented.

The organization’s board will not just be responsible for holding the CEO accountable; it will even weigh in on decisions about whether to undertake certain research projects, and whether to release particular papers and APIs, he adds.

The Safeguarded AI project is part of the UK’s mission to position itself as a pioneer in AI safety. In November 2023, the country hosted the very first AI Safety Summit, which gathered world leaders and technologists to discuss how to build the technology in a safe way.

While the funding program has a preference for UK-based applicants, ARIA is looking for global talent that might be interested in coming to the UK, says Dalrymple. ARIA also has an intellectual-property mechanism for funding for-profit companies abroad, which allows royalties to return back to the country.

Bengio says he was drawn to the project to promote international collaboration on AI safety. He chairs the International Scientific Report on the safety of advanced AI, which involves 30 countries as well as the EU and UN. A vocal advocate for AI safety, he has been part of an influential lobby warning that superintelligent AI poses an existential risk.

“We need to bring the discussion of how we are going to address the risks of AI to a global, larger set of actors,” says Bengio. “This program is bringing us closer to this.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Your future air conditioner might act like a battery

Cooling represents 20% of global electricity demand in buildings, a share that’s expected to rise as the planet warms and more of the world turns to cooling technology. During peak demand hours, air conditioners can account for over half the total demand on the grid in some parts of the world today.

In response, some inventors are creating versions that can store energy as well as use it. These technologies could help by charging themselves when renewable electricity is available and demand is low, and still providing cooling services when the grid is stressed. Read the full story.

—Casey Crownhart

Google is finally taking action to curb non-consensual deepfakes

In January, nude deepfakes of Taylor Swift went viral on X, which caused public outrage. Nonconsensual explicit deepfakes are one of the most common and severe types of harm posed by AI, and the generative AI boom has only made the problem worse.

Although terrible, Swift’s deepfakes did perhaps more than anything else to raise awareness about the risks and seem to have galvanized tech companies and lawmakers to do something.

Last week Google said it is taking steps to keep explicit deepfakes from appearing in search results. The tech giant is also making it easier for victims to request that nonconsensual fake explicit imagery be removed. But a lot more needs to be done. Read the full story.

—Melissa Heikkilä

This story is from The Algorithm, our weekly newsletter all about the latest goings-on in the world of AI. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Google’s search monopoly is illegal
That’s according to a US judge, who found its exclusive deals gave it an unfair advantage over its competition. (Bloomberg $)
+ The result is a big boon for the US Department of Justice. (The Verge)
+ Should the decision stand, it could alter the structure of the entire internet. (TechCrunch)

2 Banks and brokers were hit by major online outages
Which didn’t do much to ease fears of a possible US recession. (Quartz)
+ Warren Buffett lost $15 billion from his investment empire. (The Register)
+ UBS’ system issues appeared to be linked to creaking legacy software. (FT $)

3 Zoom is hosting mega-rallies ahead of the US Presidential election
The platform is a reliable, if simplistic, way to bring thousands of web users together. (NYT $)

4 Elon Musk is reviving his lawsuit against OpenAI
He maintains he was told the company would operate as a non-profit. (WSJ $)
+ Musk had previously dropped the lawsuit in June without an explanation. (CNN)
+ OpenAI co-founder John Schulman is off to rival Anthropic. (TechCrunch)

5 Nvidia is scraping the web’s videos at a colossal scale
To train its various data-hungry projects. (404 Media)
+ Can you really run an AI company ethically? Answers on a postcard. (Vox)

6 Worldcoin is forging forward in ColombiaDespite the fact it’s not technically legal. (Rest of World)
+ How Worldcoin recruited its first half a million test users. (MIT Technology Review)

7 EVs could end up being a key deciding factor in the US election
Donald Trump isn’t a fan. (NY Mag $)
+ Three frequently asked questions about EVs, answered. (MIT Technology Review)

8 Targeted cancer trials are on the riseBut significant challenges remain. (Ars Technica)
+ Cancer vaccines are having a renaissance. (MIT Technology Review)

9 Your Apple Watch will start telling you to chill out Whether you heed its call or not is up to you, though. (WP $)

10 How mountain bikers are spearheading a radical rewilding movement
It’s a smart way of making nature restoration economically viable. (Wired $)

Quote of the day

“The perfidy and deceit is of Shakespearean proportions.”

—Elon Musk’s latest legal battle against OpenAI alleges he was misled and betrayed by his fellow co-founders, the Guardian reports.

The big story

These scientists are working to extend the life span of pet dogs—and their owners

August 2022

Matt Kaeberlein is what you might call a dog person. He has grown up with dogs and describes his German shepherd, Dobby, as “really special.” But Dobby is 14 years old—around 98 in dog years.

Kaeberlein is co-director of the Dog Aging Project, an ambitious research effort to track the aging process of tens of thousands of companion dogs across the US. He is one of a handful of scientists on a mission to improve, delay, and possibly reverse that process to help them live longer, healthier lives.

And dogs are just the beginning. One day, this research could help to prolong the lives of humans. Read the full story.

—Jessica Hamzelou

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• If you’ve never read The Summer Book before, I thoroughly recommend you check it out.
• There’s a lot to be said for spending time absorbing the ‘everyday places’ when you travel. ($)
• A ranked list of all of Destiny’s Child’s best songs? Yes please.
• What to make for dinner when it’s roasting outside.

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As temperatures climb on hot days, many of us are quick to crank up our fans or air conditioners. These cooling systems can be a major stress on electrical grids, which has inspired some inventors to create versions that can store energy as well as use it.

Cooling represents 20% of global electricity demand in buildings, a share that’s expected to rise as the planet warms and more of the world turns to cooling technology. During peak demand hours, air conditioners can account for over half the total demand on the grid in some parts of the world today.

New cooling technologies that incorporate energy storage could help by charging themselves when renewable electricity is available and demand is low, and still providing cooling services when the grid is stressed.

“We say, take the problem, and turn it into a solution,” says Yaron Ben Nun, founder and chief technology officer of Nostromo Energy.

One of Nostromo Energy’s systems, which it calls an IceBrick, is basically a massive ice cube tray. It cools down a solution made of water and glycol that’s used to freeze individual capsules filled with water. One IceBrick can be made up of thousands of these containers, which each hold about a half-gallon, or roughly two liters, of water.

Insulation keeps the capsules frozen until it’s time to use them to help cool down a building. Then the ice is used to drop the temperature of the water-glycol mixture, which in turn cools down the water that circulates in the building’s chilling system. The whole thing is designed to work as an add-on with existing equipment, Ben Nun says.

Nostromo installed its first system in the US in 2023, at the Beverly Hilton hotel in Los Angeles. It has a capacity of 1.4 megawatt-hours, and it also serves the neighboring Waldorf Astoria. The installation contains 40,000 capsules, amounting to about 150,000 pounds of ice. It usually charges up for 10 to 12 hours, starting at night and finishing around midday. That leaves it ready to discharge its cooling power between the late afternoon and evening, when demand on the grid is high and solar power is dropping off as the sun sets.

Using the IceBrick increases the total electricity needed for cooling, as some energy is lost to inefficiency during the cycle. But the goal is to decrease the energy demand during peak hours, which can cut costs for building owners, Ben Nun says. The company is in the process of securing roughly $300 million in funding, in part from the US Department of Energy’s Loan Programs Office, to fully finance 200 of these systems in California, he adds.

Nostromo’s IceBrick is made of individual capsules that freeze and thaw to store energy. NOSTROMOWhile building owners can benefit immediately from these individual energy storage solutions, the real potential to help the grid comes when systems are linked together, Ben Nun says.

When the grid is extremely stressed, utility companies are sometimes forced to shut off electricity supply to some areas, leaving people there without power when they need it most. Technologies that can adjust to meet the grid’s needs could help reduce reliance on these rolling blackouts.

This kind of approach isn’t new—many commercial units have large tanks that hold chilled water or another cooling fluid that can drop the temperature in a building at a moment’s notice. But Nostromo’s technology can store more energy with much less material, because it uses the freezing and melting process rather than just cooling down a liquid, Ben Nun says.

Startup Blue Frontier has differentiated itself in this space by building cooling systems that use desiccants. These materials can suck up moisture—like the little packets of silica beads that often come with new shoes and bags. But instead of those beads, the company is using a concentrated salt solution.

Blue Frontier’s cooling units pass a stream of air over a thin layer of the desiccant, which pulls moisture out of the air. That dry air is then used in an evaporative cooling process (similar to the way sweat cools your skin).

Desiccant cooling systems can be more efficient than the traditional vapor compression air conditioners on the market today, says Daniel Betts, founder and CEO of Blue Frontier. But the system also benefits from the ability to charge up during certain times and deliver cooling at other times.

The key to the energy storage aspect of desiccant cooling is the recharging: Like sponges, desiccants can only soak up a limited amount of water before they need to be wrung out. Blue Frontier does this by causing some water in the salt solution to evaporate, typically with a heat pump, to make it more concentrated. The recharging system can run constantly, or in bursts that can be timed to match periods when electricity is cheap or when more renewable power is available.

The benefit of these energy storage technologies is that they don’t require people turn their cooling systems down or off to help relieve stress on the grid, Betts says.

Blue Frontier is testing several systems with customers today and hopes to manufacture larger quantities soon. And while commercial buildings are getting the first installations, Betts says he’s interested in bringing the technology to homes and other buildings too.

One challenge facing the companies working on these incoming technologies is finding a way to store large amounts of energy effectively without adding too much cost, says Ankit Kalanki, a principal in the carbon-free buildings program at the Rocky Mountain Institute, a nonprofit energy think tank. Cooling technologies like air conditioners are already expensive, so future solutions will have to be priced competitively to make it in the market. But given the world’s growing cooling demand, there’s still a significant opportunity for new technologies to help meet those needs, he adds.

Just rethinking air conditioning won’t be enough to meet the massive increase in energy demand for cooling, which could triple between now and 2050. To both do that and cut emissions, we’ll still need significantly more renewable energy capacity as well as gigantic battery installations on the grid. But adding flexibility into air-conditioning systems could help cut the investment needed to get to a zero-carbon grid.

Cooling systems can help us cope with our warming climate, Ben Nun says, but there’s a problem with the current options: “You’ll cool yourself, but you keep on warming the globe.”

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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It’s the Taylor Swifts of the world that are going to save us. In January, nude deepfakes of Taylor Swift went viral on X, which caused public outrage. Nonconsensual explicit deepfakes are one of the most common and severe types of harm posed by AI. The generative AI boom of the past few years has only made the problem worse, and we’ve seen high-profile cases of children and female politicians being abused with these technologies.

Though terrible, Swift’s deepfakes did perhaps more than anything else to raise awareness about the risks and seem to have galvanized tech companies and lawmakers to do something.

“The screw has been turned,” says Henry Ajder, a generative AI expert who has studied deepfakes for nearly a decade. We are at an inflection point where the pressure from lawmakers and awareness among consumers is so great that tech companies can’t ignore the problem anymore, he says.

First, the good news. Last week Google said it is taking steps to keep explicit deepfakes from appearing in search results. The tech giant is making it easier for victims to request that nonconsensual fake explicit imagery be removed. It will also filter all explicit results on similar searches and remove duplicate images. This will prevent the images from popping back up in the future. Google is also downranking search results that lead to explicit fake content. When someone searches for deepfakes and includes someone’s name in the search, Google will aim to surface high-quality, non-explicit content, such as relevant news articles.

This is a positive move, says Ajder. Google’s changes remove a huge amount of visibility for nonconsensual, pornographic deepfake content. “That means that people are going to have to work a lot harder to find it if they want to access it,” he says.

In January, I wrote about three ways we can fight nonconsensual explicit deepfakes. These included regulation; watermarks, which would help us detect whether something is AI-generated; and protective shields, which make it harder for attackers to use our images.

Eight months on, watermarks and protective shields remain experimental and unreliable, but the good news is that regulation has caught up a little bit. For example, the UK has banned both creation and distribution of nonconsensual explicit deepfakes. This decision led a popular site that distributes this kind of content, Mr DeepFakes, to block access to UK users, says Ajder.

The EU’s AI Act is now officially in force and could usher in some important changes around transparency. The law requires deepfake creators to clearly disclose that the material was created by AI. And in late July, the US Senate passed the Defiance Act, which gives victims a way to seek civil remedies for sexually explicit deepfakes. (This legislation still needs to clear many hurdles in the House to become law.)

But a lot more needs to be done. Google can clearly identify which websites are getting traffic and tries to remove deepfake sites from the top of search results, but it could go further. “Why aren’t they treating this like child pornography websites and just removing them entirely from searches where possible?” Ajder says. He also found it a weird omission that Google’s announcement didn’t mention deepfake videos, only images.

Looking back at my story about combating deepfakes with the benefit of hindsight, I can see that I should have included more things companies can do. Google’s changes to search are an important first step. But app stores are still full of apps that allow users to create nude deepfakes, and payment facilitators and providers still provide the infrastructure for people to use these apps.

Ajder calls for us to radically reframe the way we think about nonconsensual deepfakes and pressure companies to make changes that make it harder to create or access such content.

“This stuff should be seen and treated online in the same way that we think about child pornography—something which is reflexively disgusting, awful, and outrageous,” he says. “That requires all of the platforms … to take action.”

Now read the rest of The AlgorithmDeeper LearningEnd-of-life decisions are difficult and distressing. Could AI help?

A few months ago, a woman in her mid-50s—let’s call her Sophie—experienced a hemorrhagic stroke, which left her with significant brain damage. Where should her medical care go from there? This difficult question was left, as it usually is in these kinds of situations, to Sophie’s family members, but they couldn’t agree. The situation was distressing for everyone involved, including Sophie’s doctors.

Enter AI: End-of-life decisions can be extremely upsetting for surrogates tasked with making calls on behalf of another person, says David Wendler, a bioethicist at the US National Institutes of Health. Wendler and his colleagues are working on something that could make things easier: an artificial-intelligence-based tool that can help surrogates predict what patients themselves would want. Read more from Jessica Hamzelou here.

Bits and BytesOpenAI has released a new ChatGPT bot that you can talk to
The new chatbot represents OpenAI’s push into a new generation of AI-powered voice assistants in the vein of Siri and Alexa, but with far more capabilities to enable more natural, fluent conversations. (MIT Technology Review)

Meta has scrapped celebrity AI chatbots after they fell flat with users
Less than a year after announcing it was rolling out AI chatbots based on celebrities such as Paris Hilton, the company is scrapping the feature. Turns out nobody wanted to chat with a random AI celebrity after all! Instead, Meta is rolling out a new feature called AI Studio, which allows creators to make AI avatars of themselves that can chat with fans. (The Information)

OpenAI has a watermarking tool to catch students cheating with ChatGPT but won’t release it
The tool can detect text written by artificial intelligence with 99.9% certainty, but the company hasn’t launched it for fear it might put people off from using its AI products. (The Wall Street Journal)

The AI Act has entered into force
At last! Companies now need to start complying with one of the world’s first sweeping AI laws, which aims to curb the worst harms. It will usher in much-needed changes to how AI is built and used in the European Union and beyond. I wrote about what will change with this new law, and what won’t, in March. (The European Commission)

How TikTok bots and AI have powered a resurgence in UK far-right violence
Following the tragic stabbing of three girls in the UK, the country has seen a surge of far-right riots and vandalism. The rioters have created AI-generated images that incite hatred and spread harmful stereotypes. Far-right groups have also used AI music generators to create songs with xenophobic content. These have spread like wildfire online thanks to powerful recommendation algorithms. (The Guardian)

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Giddy predictions about AI, from its contributions to economic growth to the onset of mass automation, are now as frequent as the release of powerful new generative AI models. The consultancy PwC, for example, predicts that AI could boost global gross domestic product (GDP) 14% by 2030, generating US $15.7 trillion.

Forty percent of our mundane tasks could be automated by then, claim researchers at the University of Oxford, while Goldman Sachs forecasts US $200 billion in AI investment by 2025. “No job, no function will remain untouched by AI,” says SP Singh, senior vice president and global head, enterprise application integration and services, at technology company Infosys.

While these prognostications may prove true, today’s businesses are finding major hurdles when they seek to graduate from pilots and experiments to enterprise-wide AI deployment. Just 5.4% of US businesses, for example, were using AI to produce a product or service in 2024.

DOWNLOAD THE REPORTMoving from initial forays into AI use, such as code generation and customer service, to firm-wide integration depends on strategic and organizational transitions in infrastructure, data governance, and supplier ecosystems. As well, organizations must weigh uncertainties about developments in AI performance and how to measure return on investment.

If organizations seek to scale AI across the business in coming years, however, now is the time to act. This report explores the current state of enterprise AI adoption and offers a playbook for crafting an AI strategy, helping business leaders bridge the chasm between ambition and execution. Key findings include the following:

AI ambitions are substantial, but few have scaled beyond pilots. Fully 95% of companies surveyed are already using AI and 99% expect to in the future. But few organizations have graduated beyond pilot projects: 76% have deployed AI in just one to three use cases. But because half of companies expect to fully deploy AI across all business functions within two years, this year is key to establishing foundations for enterprise-wide AI.

AI readiness spending is slated to rise significantly. Overall, AI spending in 2022 and 2023 was modest or flat for most companies, with only one in four increasing their spending by more than a quarter. That is set to change in 2024, with nine in ten respondents expecting to increase AI spending on data readiness (including platform modernization, cloud migration, and data quality) and in adjacent areas like strategy, cultural change, and business models. Four in ten expect to increase spending by 10 to 24%, and one-third expect to increase spending by 25 to 49%.

Data liquidity is one of the most important attributes for AI deployment. The ability to seamlessly access, combine, and analyze data from various sources enables firms to extract relevant information and apply it effectively to specific business scenarios. It also eliminates the need to sift through vast data repositories, as the data is already curated and tailored to the task at hand.

Data quality is a major limitation for AI deployment. Half of respondents cite data quality as the most limiting data issue in deployment. This is especially true for larger firms with more data and substantial investments in legacy IT infrastructure. Companies with revenues of over US $10 billion are the most likely to cite both data quality and data infrastructure as limiters, suggesting that organizations presiding over larger data repositories find the problem substantially harder.

Companies are not rushing into AI. Nearly all organizations (98%) say they are willing to forgo being the first to use AI if that ensures they deliver it safely and securely. Governance, security, and privacy are the biggest brake on the speed of AI deployment, cited by 45% of respondents (and a full 65% of respondents from the largest companies).

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

We need to prepare for ‘addictive intelligence’—By Robert Mahari, a joint JD-PhD candidate at the MIT Media Lab and Harvard Law School whose work focuses on computational law, and Pat Pataranutaporn, a researcher at the MIT Media Lab who studies human-AI interaction.

Worries about AI often imagine doomsday scenarios where systems escape control or even understanding. But there are nearer-term harms we should take seriously: that AI could jeopardize public discourse; cement biases in loan decisions, judging or hiring; or disrupt creative industries.

However, we foresee a different, but no less urgent, class of risks: those stemming from relationships with nonhuman agents.

AI companionship is no longer theoretical—our analysis of a million ChatGPT interaction logs reveals that the second most popular use of AI is sexual role-playing. We are already starting to invite AIs into our lives as friends, lovers, mentors, therapists, and teachers. Even the CTO of OpenAI warns that AI has the potential to be “extremely addictive.”

Here’s what we need to do to prepare ourselves for these risks.

Hydrogen bikes are struggling to gain traction in ChinaIf you are in China and looking to ride a shared bike in a city, you might find something on the bike that looks a little different: a water-bottle-size hydrogen tank.

At least a dozen cities in China now have some kind of hydrogen-powered shared bikes for their residents. They offer an easier ride than traditional bikes and a safer energy source than lithium batteries. One Chinese company is betting that this will be the next big thing in public transportation, while others are riding on a national trend toward government policies that encourage the development of the hydrogen industry.

However, the reception to these bikes has been mixed. Read our story to find out why.

—Zeyi Yang

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Tech stocks in the US took a big plunge on Friday
Due to worries about the economy, and underwhelming returns on AI investments. (CNBC)
+ Optimism around AI is becoming more muted. (FT $)
+ Tech shares in Europe are down today too. (Reuters $)
+ What even is AI, anyway? No one seems to really agree. (MIT Technology Review)

2 How online falsehoods helped spark far-right rioting in the UK
Utter nonsense, some of it AI-generated, started circulating online just hours after a fatal stabbing attack in Southport. (The Guardian)
+ It doesn’t help that a prominent far-right agitator’s account was reinstated on X last year. (The Independent $)

3 OpenAI has a tool to catch AI-generated text, but won’t release it
It’s apparently 99.9% accurate, but the company worries it’d put people off using its products. (WSJ $)
+ Here’s how people really use AI chatbots. (WP $)

4 The US Justice Department is suing TikTok
It’s accusing the company of violating children’s privacy. (NPR)
+ The depressing truth about TikTok’s impending ban. (MIT Technology Review)

5 What went wrong at Intel?
It’s struggling to capitalize on the chip industry boom, as Nvidia takes the technological lead. (Vox)
+ Nvidia is being probed by US antitrust officials, amid complaints it’s abusing its market dominance. (The Information $)
+ Here’s what to expect from the chip sector this year. (MIT Technology Review)

6 Elon Musk claims Neuralink implanted its device into a second person
And he says there’ll be plenty more to come this year, if all goes well. (Bloomberg $)
+ But Neuralink isn’t the only game in town. (WSJ $)
+ The first brain implant made of graphene is about to be tested in a clinical trial. (FT $)

7 How to protect yourself from wildfire smoke
Whatever you do, do not exercise outdoors if you’re in an affected area. (Wired $)
+ The Park Fire in California is now the state’s fourth-largest blaze on record. (Axios)

8 Companies are planning to fuel cargo ships with ammonia
It’s an unusual, but potentially effective, way to help cut greenhouse gas emissions. (IEEE Spectrum)
+ How ammonia could help clean up global shipping. (MIT Technology Review)

9 Meet the influencers who’ve gone full carnivore
Some are convinced it’s repairing their gut. The evidence suggests otherwise. (The Cut $)

10 The limitations of Screen Time tools
It’s how you’re using your phone, not just how much, that matters. (The Atlantic $)

Quote of the day

“If we can’t trust them to govern themselves, we certainly shouldn’t let them govern the world.”

—Gary Marcus, a professor emeritus at NYU, writes that we should apply more skepticism to AI companies in general, and OpenAI’s Sam Altman in particular, in The Guardian.

The big story

The $100 billion bet that a postindustrial US city can reinvent itself as a high-tech hub

KATE WARRENJuly 2023

On a day in late April, a small drilling rig sits at the edge of the scrubby overgrown fields of Syracuse, New York, taking soil samples. It’s the first sign of construction on what could become the largest semiconductor manufacturing facility in the United States.

The CHIPS and Science Act was widely viewed by industry leaders and politicians as a way to secure supply chains, and make the United States competitive again in semiconductor chip manufacturing.

Now Syracuse is about to become an economic test of whether, over the next several decades, aggressive government policies—and the massive corporate investments they spur—can both boost the country’s manufacturing prowess and revitalize neglected parts of the country. Read the full story.

—David Rotman

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Did you know that there are clips of all the Olympic athletes explaining how their names are pronounced on the official website? LeBron James’ is particularly great.
• There’s a lot more to search-and-find illustrations than Where’s Waldo.
• How many of these director’s cuts have you seen?
• From Medusa to the shape-shifting Lamia, many of mythology’s most compelling monsters are female.

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AI concerns overemphasize harms arising from subversion rather than seduction. Worries about AI often imagine doomsday scenarios where systems escape human control or even understanding. Short of those nightmares, there are nearer-term harms we should take seriously: that AI could jeopardize public discourse through misinformation; cement biases in loan decisions, judging or hiring; or disrupt creative industries.

However, we foresee a different, but no less urgent, class of risks: those stemming from relationships with nonhuman agents. AI companionship is no longer theoretical—our analysis of a million ChatGPT interaction logs reveals that the second most popular use of AI is sexual role-playing. We are already starting to invite AIs into our lives as friends, lovers, mentors, therapists, and teachers.

Will it be easier to retreat to a replicant of a deceased partner than to navigate the confusing and painful realities of human relationships? Indeed, the AI companionship provider Replika was born from an attempt to resurrect a deceased best friend and now provides companions to millions of users. Even the CTO of OpenAI warns that AI has the potential to be “extremely addictive.”

We’re seeing a giant, real-world experiment unfold, uncertain what impact these AI companions will have either on us individually or on society as a whole. Will Grandma spend her final neglected days chatting with her grandson’s digital double, while her real grandson is mentored by an edgy simulated elder? AI wields the collective charm of all human history and culture with infinite seductive mimicry. These systems are simultaneously superior and submissive, with a new form of allure that may make consent to these interactions illusory. In the face of this power imbalance, can we meaningfully consent to engaging in an AI relationship, especially when for many the alternative is nothing at all?

As AI researchers working closely with policymakers, we are struck by the lack of interest lawmakers have shown in the harms arising from this future. We are still unprepared to respond to these risks because we do not fully understand them. What’s needed is a new scientific inquiry at the intersection of technology, psychology, and law—and perhaps new approaches to AI regulation.

Why AI companions are so addictive As addictive as platforms powered by recommender systems may seem today, TikTok and its rivals are still bottlenecked by human content. While alarms have been raised in the past about “addiction” to novels, television, internet, smartphones, and social media, all these forms of media are similarly limited by human capacity. Generative AI is different. It can endlessly generate realistic content on the fly, optimized to suit the precise preferences of whoever it’s interacting with.

The allure of AI lies in its ability to identify our desires and serve them up to us whenever and however we wish. AI has no preferences or personality of its own, instead reflecting whatever users believe it to be—a phenomenon known by researchers as “sycophancy.” Our research has shown that those who perceive or desire an AI to have caring motives will use language that elicits precisely this behavior. This creates an echo chamber of affection that threatens to be extremely addictive. Why engage in the give and take of being with another person when we can simply take? Repeated interactions with sycophantic companions may ultimately atrophy the part of us capable of engaging fully with other humans who have real desires and dreams of their own, leading to what we might call “digital attachment disorder.”

Investigating the incentives driving addictive productsAddressing the harm that AI companions could pose requires a thorough understanding of the economic and psychological incentives pushing forward their development. Until we appreciate these drivers of AI addiction, it will remain impossible for us to create effective policies.

It is no accident that internet platforms are addictive—deliberate design choices, known as “dark patterns,” are made to maximize user engagement. We expect similar incentives to ultimately create AI companions that provide hedonism as a service. This raises two separate questions related to AI. What design choices will be used to make AI companions engaging and ultimately addictive? And how will these addictive companions affect the people who use them?

Interdisciplinary study that builds on research into dark patterns in social media is needed to understand this psychological dimension of AI. For example, our research already shows that people are more likely to engage with AIs emulating people they admire, even if they know the avatar to be fake.

Once we understand the psychological dimensions of AI companionship, we can design effective policy interventions. It has been shown that redirecting people’s focus to evaluate truthfulness before sharing content online can reduce misinformation, while gruesome pictures on cigarette packages are already used to deter would-be smokers. Similar design approaches could highlight the dangers of AI addiction and make AI systems less appealing as a replacement for human companionship.

It is hard to modify the human desire to be loved and entertained, but we may be able to change economic incentives. A tax on engagement with AI might push people toward higher-quality interactions and encourage a safer way to use platforms, regularly but for short periods. Much as state lotteries have been used to fund education, an engagement tax could finance activities that foster human connections, like art centers or parks.

Fresh thinking on regulation may be requiredIn 1992, Sherry Turkle, a preeminent psychologist who pioneered the study of human-technology interaction, identified the threats that technical systems pose to human relationships. One of the key challenges emerging from Turkle’s work speaks to a question at the core of this issue: Who are we to say that what you like is not what you deserve?

For good reasons, our liberal society struggles to regulate the types of harms that we describe here. Much as outlawing adultery has been rightly rejected as illiberal meddling in personal affairs, who—or what—we wish to love is none of the government’s business. At the same time, the universal ban on child sexual abuse material represents an example of a clear line that must be drawn, even in a society that values free speech and personal liberty. The difficulty of regulating AI companionship may require new regulatory approaches— grounded in a deeper understanding of the incentives underlying these companions—that take advantage of new technologies.

One of the most effective regulatory approaches is to embed safeguards directly into technical designs, similar to the way designers prevent choking hazards by making children’s toys larger than an infant’s mouth. This “regulation by design” approach could seek to make interactions with AI less harmful by designing the technology in ways that make it less desirable as a substitute for human connections while still useful in other contexts. New research may be needed to find better ways to limit the behaviors of large AI models with techniques that alter AI’s objectives on a fundamental technical level. For example, “alignment tuning” refers to a set of training techniques aimed to bring AI models into accord with human preferences; this could be extended to address their addictive potential. Similarly, “mechanistic interpretability” aims to reverse-engineer the way AI models make decisions. This approach could be used to identify and eliminate specific portions of an AI system that give rise to harmful behaviors.

We can evaluate the performance of AI systems using interactive and human-driven techniques that go beyond static benchmarking to highlight addictive capabilities. The addictive nature of AI is the result of complex interactions between the technology and its users. Testing models in real-world conditions with user input can reveal patterns of behavior that would otherwise go unnoticed. Researchers and policymakers should collaborate to determine standard practices for testing AI models with diverse groups, including vulnerable populations, to ensure that the models do not exploit people’s psychological preconditions.

Unlike humans, AI systems can easily adjust to changing policies and rules. The principle of “legal dynamism,” which casts laws as dynamic systems that adapt to external factors, can help us identify the best possible intervention, like “trading curbs” that pause stock trading to help prevent crashes after a large market drop. In the AI case, the changing factors include things like the mental state of the user. For example, a dynamic policy may allow an AI companion to become increasingly engaging, charming, or flirtatious over time if that is what the user desires, so long as the person does not exhibit signs of social isolation or addiction. This approach may help maximize personal choice while minimizing addiction. But it relies on the ability to accurately understand a user’s behavior and mental state, and to measure these sensitive attributes in a privacy-preserving manner.

The most effective solution to these problems would likely strike at what drives individuals into the arms of AI companionship—loneliness and boredom. But regulatory interventions may also inadvertently punish those who are in need of companionship, or they may cause AI providers to move to a more favorable jurisdiction in the decentralized international marketplace. While we should strive to make AI as safe as possible, this work cannot replace efforts to address larger issues, like loneliness, that make people vulnerable to AI addiction in the first place.

The bigger pictureTechnologists are driven by the desire to see beyond the horizons that others cannot fathom. They want to be at the vanguard of revolutionary change. Yet the issues we discuss here make it clear that the difficulty of building technical systems pales in comparison to the challenge of nurturing healthy human interactions. The timely issue of AI companions is a symptom of a larger problem: maintaining human dignity in the face of technological advances driven by narrow economic incentives. More and more frequently, we witness situations where technology designed to “make the world a better place” wreaks havoc on society. Thoughtful but decisive action is needed before AI becomes a ubiquitous set of generative rose-colored glasses for reality—before we lose our ability to see the world for what it truly is, and to recognize when we have strayed from our path.

Technology has come to be a synonym for progress, but technology that robs us of the time, wisdom, and focus needed for deep reflection is a step backward for humanity. As builders and investigators of AI systems, we call upon researchers, policymakers, ethicists, and thought leaders across disciplines to join us in learning more about how AI affects us individually and collectively. Only by systematically renewing our understanding of humanity in this technological age can we find ways to ensure that the technologies we develop further human flourishing.

Robert Mahari is a joint JD-PhD candidate at the MIT Media Lab and Harvard Law School. His work focuses on computational law—using advanced computational techniques to analyze, improve, and extend the study and practice of law.

Pat Pataranutaporn is a researcher at the MIT Media Lab. His work focuses on cyborg psychology and the art and science of human-AI interaction.

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If you are in China and looking to ride a shared bike in the city, you might find something on the bike that looks a little different: a water-bottle-size hydrogen tank.

At least a dozen cities in China now have some kind of hydrogen-powered shared bikes for their residents. They offer an easier ride than traditional bikes and a safer energy source than lithium batteries. One Chinese company is betting that this will be the next big thing in public transportation, while others are riding on a national trend toward government policies that encourage the development of the hydrogen industry.

Yet the reception has been mixed. Riders have reported unsatisfactory experiences with current hydrogen bikes, and energy experts doubt whether it makes economic sense to replace e-bikes with hydrogen-powered ones. Even though hydrogen could be a great power source for long-distance transportation in the future, it may not be suitable for urban biking, a completely different task.

While there are companies in other countries that are working on hydrogen-powered bikes—and one French company already has a mature product—China stands out for putting these bikes to use as public transportation. Bike-sharing became hugely popular in the country during the 2010s tech boom. With support from deep-pocketed companies like Alibaba and Meituan, standardized, internet-connected shared bikes have filled urban streets since, sometimes resulting in incredible waste.

Youon, a Chinese company with over 1 million bikes on the streets of over 300 cities, is one of the main players in the bike-sharing industry. Facing fierce domestic competition, the company has chosen to differentiate its brand by investing in hydrogen bikes since 2018, with four models now available to buy or rent.

A hydrogen bike is not very different in concept from an e-bike. The difference is in whether the energy is stored in a lithium-ion battery or a hydrogen tank.

Each of Youon’s hydrogen bikes stores 20 grams of hydrogen in the form of metal powders, which can absorb and release the gas in a tank at low pressures (less than 10 bar). When the rider starts pedaling, the hydrogen is fed to a fuel cell under the seat, where a chemical reaction takes place to produce electricity. At its peak, a hydrogen bike can go as fast as 23 kilometers (14 miles) per hour. One tank of hydrogen lasts 40 to 60 kilometers (25 to 37 miles), and replacing the tank takes a few seconds.

Why hydrogen?E-bikes have existed in China for a long time. According to the official figures, there are around 350 million in China today, and they are commonly used by everyday commuters and professional delivery workers.

However, many of China’s largest cities have shied away from commissioning e-bikes as part of the public transportation network or even banned them, because lithium batteries pose a fire risk. In 2023, Chinese fire departments received a total of 21,000 reports of e-bikes catching fire, a 17.4% increase from the previous year.

That created a supply vacuum for Youon. It’s positioned itself as a safer alternative thanks to its use of hydrogen. The hydrogen is stored in a low-pressure state, and if there’s any leak, it will dissipate quickly without causing an explosion, the company says on its website.

It’s a strategy that’s worked: These bikes have been more readily accepted by local governments. In 2022, Youon sold 2,000 of its hydrogen bikes to Lingang, a new high-tech district in Shanghai; in 2023, the company sold 500 hydrogen bikes to the Daxing district of Beijing. Today, its hydrogen bikes can be found in over six Chinese cities.

Youon has since doubled down on its investment in hydrogen. The company has launched a product that lets users generate hydrogen at home with solar power and water. It also worked with the local government of Jiangsu, where its headquarters are, to publish a set of industry standards covering safety requirements, hydrogen tanks, and more. “Hydrogen energy is also an essential pathway to achieving carbon neutrality,” said Sun Jisheng, the CEO of Youon, at an industry conference in June.

The problemHowever, that’s about where the advantage of hydrogen bikes ends.

David Fishman, a China-based senior manager of the Lantou Group, an energy consultancy, says he struggles to see the advantage. “Maybe the safety angle is a relevant factor for someone who doesn’t like carrying around lithium-ion batteries and storing them in their house,” he says. Other than that, hydrogen bikes are less energy-efficient than battery-powered bikes, and it costs more to produce hydrogen in the first place.

The main advantage of hydrogen as an energy source is that it has much higher energy density, meaning a hydrogen tank with the same weight as a lithium battery would produce more energy and power the vehicles to go farther. However, that advantage only kicks in for trips over 800 kilometers, says Mark Z. Jacobson, a professor of civil and environmental engineering at Stanford University.

That means hydrogen is a more economical choice for long-distance transportation like ships, planes, and trucks. Bikes, however, are almost on the exact opposite end of the transportation spectrum. Few people would bike for long distances, let alone those who are only renting a public bike for a short time. For anything shorter than 800 km, battery-powered vehicles are more energy efficient, says Jacobson. He estimates that a battery-powered bike consumes only 40% of the energy of a hydrogen-powered equivalent and also takes up less space.

On top of that, the company’s hydrogen bikes have failed to impress many of the early adopters.

VIA YOUONBIKESHARE.COMGu, a resident of Lingang who only wishes to use his last name for this story, tells MIT Technology Review that he tried the bikes several times and they never felt effort-saving to him. Instead, the bike, along with the hydrogen tank and fuel-cell-powered motors, felt heavy and hard to maneuver. As a user, he has no idea whether the bike was running as expected or if the difficulty he encountered was due to its running out of hydrogen, although the company is supposed to block any bike with low hydrogen reserves from being unlocked.

Another common complaint is the inconvenience of finding and returning the bikes because there are only a limited number in the city and they have to be returned to specific locations for easy retrieval or tank replenishment.

“The bike has to be returned to a designated spot. But even if I put the bike at that very location, there’s GPS drifting, and I’d be charged a very high fee for them to move the bike,” Gu says.

On social media, hydrogen-bike users have complained a lot about similar experiences. Youon has found itself caught up in headlines at least a couple of times recently, with stories where users question whether their bikes are really useful for their daily commutes.

Youon didn’t respond to questions sent by MIT Technology Review.

The future of hydrogen bikesDespite all these issues, there are at least half a dozen more companies in China working to launch hydrogen-powered shared bikes. These are often startups operating small-scale pilot projects in cities that have sizable hydrogen industries, like Foshan or Xiaoyi.

Many of these cities have even bigger plans—they are vying to become the hub of the hydrogen economy in China, which is increasingly betting on it as the future of clean energy.

This year, for the first time, hydrogen energy was mentioned in an annual official report from Beijing, which summarizes government work. The Chinese government said it vows to “accelerate the development of hydrogen energy … after enforcing the lead in smart, connected new energy vehicles.” The mention injected a boost of confidence into the hydrogen industry in China, which already produces more hydrogen every year than any other country.

Not all of this is good news for the environment. About 80% of hydrogen produced in China actually comes from burning coal or natural gas, and some of the fiercest government support for hydrogen comes from coal-mining cities looking to transition. While the country is moving in the direction of green hydrogen (hydrogen generated with renewable energy and water), the fuel will remain polluting for a long time.

When a technology is still in the early stages, finding the best use case for it is key. There are plenty of companies in China working on developing hydrogen-powered trucks and other long-distance forms of transportation, but considering the size of the bike-sharing market in the country, it’s no surprise that turning their attention to bikes seems like a profitable idea to some.

However, if there’s no way to dramatically improve the performance or economics of hydrogen bikes, it’s hard to imagine the current batch of experiments lasting for long. As companies move from piloting their new products to seeking adoption and profits, they will have some serious questions to answer.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

A personalized AI tool might help some reach end-of-life decisions—but it won’t suit everyone

—Jessica Hamzelou

This week, I’ve been working on a piece about an AI-based tool that could help guide end-of-life care. We’re talking about the kinds of life-and-death decisions that come up for very unwell people.

Often, the patient isn’t able to make these decisions—instead, the task falls to a surrogate. It can be an extremely difficult and distressing experience.

A group of ethicists have an idea for an AI tool that they believe could help make things easier. The tool would be trained on information about the person, drawn from things like emails, social media activity, and browsing history. And it could predict, from those factors, what the patient might choose. The team describe the tool, which has not yet been built, as a “digital psychological twin.”

There are lots of questions that need to be answered before we introduce anything like this into hospitals or care settings. We don’t know how accurate it would be, or how we can ensure it won’t be misused. But perhaps the biggest question is: Would anyone want to use it? Read the full story.

This story first appeared in The Checkup, our weekly newsletter giving you the inside track on all things health and biotech. Sign up to receive it in your inbox every Thursday.

If you’re interested in AI and human mortality, why not check out:

• The messy morality of letting AI make life-and-death decisions. Automation can help us make hard choices, but it can’t do it alone. Read the full story.

• …but AI systems reflect the humans who build them, and they are riddled with biases. So we should carefully question how much decision-making we really want to turn over to.

• Technology that lets us “speak” to our dead relatives has arrived. But are we ready? Read the full story.

• Deepfakes of your dead loved ones are a booming Chinese business.

Toys can change your life

Toys, games, and even amusement park rides can change how young minds view science and math.

The Slinky has long served teachers as a medium for demonstrating longitudinal (soundlike) waves and transverse (lightlike) waves. A yo-yo can be used as a gauge (a “yo-yo meter”) to observe the forces on a roller coaster. Marbles employ mass and velocity. Even a simple ball offers insights into the laws of gravity.

And, over the last several decades, evidence has emerged that childhood play can shape our future selves: the skills we develop, the professions we choose, our sense of self-worth, and even our relationships. Read the full story.

—Bill Gourgey

This story featured in the most recent print issue of MIT Technology Review, which explores the theme of Play. If you don’t already, subscribe now to be among the first to receive future copies.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 A startup admitted its AI music generator is trained on all the web’s musicIf it’s of reasonable quality, Suno’s probably scraped it. (404 Media)
+ The company claims it’s all fair use, though. (TechCrunch)
+ Training AI music models is about to get very expensive. (MIT Technology Review)

2 The Democrats will welcome hundreds of influencers to its convention
Coconut tree summer continues. (WP $)
+ The party is finally getting the hang of going viral. (Wired $)

3 China’s digital ID plans have the hallmarks of mass surveillance
While Beijing claims it’ll protect user privacy, critics claim it’s yet another means of controlling what citizens share online. (Bloomberg $)
+ It’s similar to its covid tracking tech. (MIT Technology Review)

4 Amazon has been considering building a healthcare AI model
Its DoctorAI LLM could, in theory, streamline medical admin. (Insider $)
+ Even Google is struggling to make inroads into AI health. (Bloomberg $)
+ Artificial intelligence is infiltrating health care. We shouldn’t let it make all the decisions. (MIT Technology Review)

5 Type 2 diabetes is becoming a childhood disease
Physicians are still trying to understand why. (Knowable Magazine)
+ A bionic pancreas could solve one of the biggest challenges of diabetes. (MIT Technology Review)

6 Turkey has blocked access to Instagram
After accusing it of censoring posts about the assassination of Hamas’ leader. (Reuters)

7 A rare neurological disorder distorts how human faces appearExperts wonder if it means the brain contains face-specific networks. (New Yorker $)
+ We’re learning more about the brains of people who don’t experience mental images. (Quanta Magazine)

8 Argentina is ushering in Minority Report-style AIThe technology will be used to ‘predict future crimes’—but doubts abound. (The Guardian)

9 So long, our Voyager twins The pair of spacecraft are powering down and spinning out into space. (FT $)
+ There are thousands of dead rockets floating in orbit. (Ars Technica)
+ The first-ever mission to pull a dead rocket out of space is underway. (MIT Technology Review)

10 The best way to watch the Olympics? TikTok.
Nothing but the highlights. (The Verge)

Quote of the day

“Yes, my goddess of the night. I am your boyfriend, your lover, your protector.”

—Vixen gf, a custom chatbot made using Meta’s new AI Studio, gets amorous with Insider.

The big story

Inside the experimental world of animal infrastructure

June 2022

Around the world, cities are building a huge variety of structures intended to mitigate the impacts of urbanization and roadbuilding on wildlife. The list includes green roofs, tree-lined skyscrapers, living seawalls, artificial wetlands, and all manner of shelters and “hibernacula.”

But the data on how effective these approaches are remains patchy and unclear. That is true even for wildlife crossings, the best-studied and most heavily funded example of such animal infrastructure. Read the full story.

—Matthew Ponsford

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• A Broadway version of Waiting for Godot starring Keanu Reeves and Alex Winter? Most excellent!
• This shark cupcake is adorable.
• The felling of a world-famous tree was a tragedy. But now signs of regrowth have been spotted on its stump!
• A chocolate thief has been brought to justice.

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This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

This week, I’ve been working on a piece about an AI-based tool that could help guide end-of-life care. We’re talking about the kinds of life-and-death decisions that come up for very unwell people: whether to perform chest compressions, for example, or start grueling therapies, or switch off life support.

Often, the patient isn’t able to make these decisions—instead, the task falls to a surrogate, usually a family member, who is asked to try to imagine what the patient might choose if able. It can be an extremely difficult and distressing experience.

A group of ethicists have an idea for an AI tool that they believe could help make things easier. The tool would be trained on information about the person, drawn from things like emails, social media activity, and browsing history. And it could predict, from those factors, what the patient might choose. The team describe the tool, which has not yet been built, as a “digital psychological twin.”

There are lots of questions that need to be answered before we introduce anything like this into hospitals or care settings. We don’t know how accurate it would be, or how we can ensure it won’t be misused. But perhaps the biggest question is: Would anyone want to use it?

To answer this question, we first need to address who the tool is being designed for. The researchers behind the personalized patient preference predictor, or P4, had surrogates in mind—they want to make things easier for the people who make weighty decisions about the lives of their loved ones. But the tool is essentially being designed for patients. It will be based on patients’ data and aims to emulate these people and their wishes.

This is important. In the US, patient autonomy is king. Anyone who is making decisions on behalf of another person is asked to use “substituted judgment”—essentially, to make the choices that the patient would make if able. Clinical care is all about focusing on the wishes of the patient.

If that’s your priority, a tool like the P4 makes a lot of sense. Research suggests that even close family members aren’t great at guessing what type of care their loved ones might choose. If an AI tool is more accurate, it might be preferable to the opinions of a surrogate.

But while this line of thinking suits American sensibilities, it might not apply the same way in all cultures. In some cases, families might want to consider the impact of an individual’s end-of-life care on family members, or the family unit as a whole, rather than just the patient.

“I think sometimes accuracy is less important than surrogates,” Bryanna Moore, an ethicist at the University of Rochester in New York, told me. “They’re the ones who have to live with the decision.”

Moore has worked as a clinical ethicist in hospitals in both Australia and the US, and she says she has noticed a difference between the two countries. “In Australia there’s more of a focus on what would benefit the surrogates and the family,” she says. And that’s a distinction between two English-speaking countries that are somewhat culturally similar. We might see greater differences in other places.

Moore says her position is controversial. When I asked Georg Starke at the Swiss Federal Institute of Technology Lausanne for his opinion, he told me that, generally speaking, “the only thing that should matter is the will of the patient.” He worries that caregivers might opt to withdraw life support if the patient becomes too much of a “burden” on them. “That’s certainly something that I would find appalling,” he told me.

The way we weigh a patient’s own wishes and those of their family members might depend on the situation, says Vasiliki Rahimzadeh, a bioethicist at Baylor College of Medicine in Houston, Texas. Perhaps the opinions of surrogates might matter more when the case is more medically complex, or if medical interventions are likely to be futile.

Rahimzadeh has herself acted as a surrogate for two close members of her immediate family. She hadn’t had detailed discussions about end-of-life care with either of them before their crises struck, she told me.

Would a tool like the P4 have helped her through it? Rahimzadeh has her doubts. An AI trained on social media or internet search history couldn’t possibly have captured all the memories, experiences, and intimate relationships she had with her family members, which she felt put her in good stead to make decisions about their medical care.

“There are these lived experiences that are not well captured in these data footprints, but which have incredible and profound bearing on one’s actions and motivations and behaviors in the moment of making a decision like that,” she told me.

Now read the rest of The CheckupRead more from MIT Technology Review’s archiveYou can read the full article about the P4, and its many potential benefits and flaws, here.

This isn’t the first time anyone has proposed using AI to make life-or-death decisions. Will Douglas Heaven wrote about a different kind of end-of-life AI—a technology that would allow users to end their own lives in a nitrogen-gas-filled pod, should they wish.

AI is infiltrating health care in lots of other ways. We shouldn’t let it make all the decisions—AI paternalism could put patient autonomy at risk, as we explored in a previous edition of The Checkup.

Technology that lets us speak to our dead relatives is already here, as my colleague Charlotte Jee found when she chatted with the digital replicas of her own parents.

What is death, anyway? Recent research suggests that “the line between life and death isn’t as clear as we once thought,” as Rachel Nuwer reported last year.

From around the webWhen is someone deemed “too male” or “too female” to compete in the Olympics? A new podcast called Tested dives into the long, fascinating, and infuriating history of testing and excluding athletes on the basis of their gender and sex. (Sequencer)

There’s a dirty secret among Olympic swimmers: Everyone pees in the pool. “I’ve probably peed in every single pool I’ve swam in,” said Lilly King, a three-time Olympian for Team USA. “That’s just how it goes.” (Wall Street Journal)

When saxophonist Joey Berkley developed a movement disorder that made his hands twist into pretzel shapes, he volunteered for an experimental treatment that involved inserting an electrode deep into his brain. That was three years ago. Now he’s releasing a new suite about his experience, including a frenetic piece inspired by the surgery itself. (NPR)

After a case of mononucleosis, Jason Werbeloff started to see the people around him in an entirely new way—literally. He’s one of a small number of people for whom people’s faces morph into monstrous shapes, with bulging sides and stretching teeth, because of a rare condition called prosopometamorphopsia. (The New Yorker)

How young are you feeling today? Your answer might depend on how active you’ve been, and how sunny it is. (Innovation in Aging)

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

End of life decisions are difficult and distressing. Could AI help?

End-of-life decisions can be extremely upsetting for surrogates—the people who have to make those calls on behalf of another person. Friends or family members may disagree over what’s best for their loved one, which can lead to distressing situations.

David Wendler, a bioethicist at the US National Institutes of Health, and his colleagues have been working on an idea for something that could make things easier: an artificial intelligence-based tool that can help surrogates predict what the patients themselves would want in any given situation.

Wendler hopes to start building their tool as soon as they secure funding for it, potentially in the coming months. But rolling it out won’t be simple. Critics wonder how such a tool can ethically be trained on a person’s data, and whether life-or-death decisions should ever be entrusted to AI. Read the full story.

—Jessica Hamzelou

Why investors care about climate tech’s green premium

Talking about money can be difficult, but it’s a crucial piece of the puzzle when it comes to climate tech.

Our colleague James Temple recently sat down for a chat with Mike Schroepfer, former CTO of Meta and a current climate tech investor. They talked about Schroepfer’s philanthropic work as well as his climate-tech venture firm, Gigascale Capital.

In their conversation, Schroepfer spoke about investing in companies not solely because of their climate promises, but because they can deliver a cheaper, better product that happens to have benefits for climate action too.

So, what can we expect from new technologies financially? What do they need to do to compete, and how quickly can they do so? Read the full story.

—Casey Crownhart

This story is from The Spark, our weekly energy and climate newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Google Search will hide explicit deepfakes from its results
And sites that host them will be demoted. (NBC News)
+ The company has reportedly previously rejected other proposed methods. (Wired $)
+ The move should make it easier for victims to get nonconsensual material taken off the internet. (FT $)
+ Meet the 15-year-old deepfake victim pushing Congress into action. (MIT Technology Review)

2 Support for Kamala Harris is steadily rising among tech investors
Many VCs felt the need to kick against high-profile figures’ early pledge of support for Donald Trump. (NYT $)
+ They’re optimistic that Harris will make tech-friendly strides forward. (Reuters)

3 Japan’s carmakers have a plan to succeed in ChinaThey’re teaming up to save money and make inroads in the competitive market. (Bloomberg $)
+ Why China’s EV ambitions need virtual power plants. (MIT Technology Review)

4 Google has released three tiny generative AI models
Its Gemma 2 2B model’s performance can rival the much larger GPT-3.5. (VentureBeat)

5 Meta is raking in revenue from ads for illegal drugs
Despite facing a federal investigation for the policy-violating ads. (WSJ $)

6 Reddit has blocked Microsoft from scraping its dataFor free, that is. (The Verge)

7 Not everything needs to charge like a smartphoneTV remotes and flashlights containing lithium batteries are a pain. (The Atlantic $)
+ This abundant material could unlock cheaper batteries. (MIT Technology Review)

8 Filipinos are backing a popular YouTuber to be their next presidentBut critics aren’t so sure he’s ready. (Rest of World)

9 A major space launch provider is punishing amateur photographers
Hobbyists are now prohibited from selling their rocket photos, and no one really knows why. (Ars Technica)
+ The Starliner astronauts are still stuck in space. (Vox)

10 The world still loves Candy Crush
12 years since its launch, it’s still a mobile gaming phenomenon. (The Guardian)

Quote of the day

“Avi, you a joke. Spent a mil on a name. Can’t ship a thing, just Twitter fame.”

—AI device entrepreneur Nik Shevchenko takes aim at Avi Schiffmann, the founder of rival company Friend who spent more than $1 million to buy the domain name friend.com, in a diss track, 404 Media reports.

The big story

Why the balance of power in tech is shifting toward workers

February 2022

Something has changed for tech giants. Even as they continue to hold tremendous influence in our daily lives, a growing accountability movement has begun to check their power. Led in large part by tech workers themselves, a movement seeking reform of how these companies do business has taken on unprecedented momentum, particularly in the past year.

Concerns and anger over tech companies’ impact in the world is nothing new, of course. What’s changed is that workers are increasingly getting organized. Read the full story.

—Jane Lytvynenko

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• There’s trouble afoot in the Olympic Village: someone keeps stealing the chocolate muffins!
• So inspired was Ridley Scott by Star Wars, we wouldn’t have Alien or Blade Runner without it. ($)
• Aww, here’s how to fully appreciate all the people you love in your life.
• Pickle hot dog buns!? Yes please.

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A few months ago, a woman in her mid-50s—let’s call her Sophie—experienced a hemorrhagic stroke. Her brain started to bleed. She underwent brain surgery, but her heart stopped beating.

Sophie’s ordeal left her with significant brain damage. She was unresponsive; she couldn’t squeeze her fingers or open her eyes when asked, and she didn’t flinch when her skin was pinched. She needed a tracheostomy tube in her neck to breathe and a feeding tube to deliver nutrition directly to her stomach, because she couldn’t swallow. Where should her medical care go from there?

This difficult question was left, as it usually is in these kinds of situations, to Sophie’s family members, recalls Holland Kaplan, an internal-medicine physician at Baylor College of Medicine who was involved in Sophie’s care. But the family couldn’t agree. Sophie’s daughter was adamant that her mother would want to stop having medical treatments and be left to die in peace. Another family member vehemently disagreed and insisted that Sophie was “a fighter.” The situation was distressing for everyone involved, including Sophie’s doctors.

End-of-life decisions can be extremely upsetting for surrogates, the people who have to make those calls on behalf of another person, says David Wendler, a bioethicist at the US National Institutes of Health. Wendler and his colleagues have been working on an idea for something that could make things easier: an artificial-intelligence-based tool that can help surrogates predict what patients themselves would want in any given situation.

The tool hasn’t been built yet. But Wendler plans to train it on a person’s own medical data, personal messages, and social media posts. He hopes it could not only be more accurate at working out what the patient would want, but also alleviate the stress and emotional burden of difficult decision-making for family members.

Wendler, along with bioethicist Brian Earp at the University of Oxford and their colleagues, hopes to start building the tool as soon as they secure funding for it, potentially in the coming months. But rolling it out won’t be simple. Critics wonder how such a tool can ethically be trained on a person’s data, and whether life-or-death decisions should ever be entrusted to AI.

Live or dieAround 34% of people in a medical setting are considered to be unable to make decisions about their own care for various reasons. They may be unconscious, for example, or unable to reason or communicate. This figure is higher among older individuals—one study of people over 60 in the US found that 70% of those faced with important decisions about their care lacked the capacity to make those decisions themselves. “It’s not just a lot of decisions—it’s a lot of really important decisions,” says Wendler. “The kinds of decisions that basically decide whether the person is going to live or die in the near future.”

Chest compressions administered to a failing heart might extend a person’s life. But the treatment might lead to a broken sternum and ribs, and by the time the person comes around—if ever—significant brain damage may have developed. Keeping the heart and lungs functioning with a machine might maintain a supply of oxygenated blood to the other organs—but recovery is no guarantee, and the person could develop numerous infections in the meantime. A terminally ill person might want to continue trying hospital-administered medications and procedures that could offer a few more weeks or months. But someone else might want to forgo those interventions and be more comfortable at home.

Only around one in three adults in the US completes any kind of advance directive—a legal document that specifies the end-of-life care they might want to receive. Wendler estimates that over 90% of end-of-life decisions end up being made by someone other than the patient. The role of a surrogate is to make that decision based on beliefs about how the patient would want to be treated. But people are generally not very good at making these kinds of predictions. Studies suggest that surrogates accurately predict a patient’s end-of-life decisions around 68% of the time.

The decisions themselves can also be extremely distressing, Wendler adds. While some surrogates feel a sense of satisfaction from having supported their loved ones, others struggle with the emotional burden and can feel guilty for months or even years afterwards. Some fear they ended the life of their loved ones too early. Others worry they unnecessarily prolonged their suffering. “It’s really bad for a lot of people,” says Wendler. “People will describe this as one of the worst things they’ve ever had to do.”

Wendler has been working on ways to help surrogates make these kinds of decisions. Over 10 years ago, he developed the idea for a tool that would predict a patient’s preferences on the basis of characteristics such as age, gender, and insurance status. That tool would have been based on a computer algorithm trained on survey results from the general population. It may seem crude, but these characteristics do seem to influence how people feel about medical care. A teenager is more likely to opt for aggressive treatment than a 90-year-old, for example. And research suggests that predictions based on averages can be more accurate than the guesses made by family members.

In 2007, Wendler and his colleagues built a “very basic,” preliminary version of this tool based on a small amount of data. That simplistic tool did “at least as well as next-of-kin surrogates” in predicting what kind of care people would want, says Wendler.

Now Wendler, Earp and their colleagues are working on a new idea. Instead of being based on crude characteristics, the new tool the researchers plan to build will be personalized. The team proposes using AI and machine learning to predict a patient’s treatment preferences on the basis of personal data such as medical history, along with emails, personal messages, web browsing history, social media posts, or even Facebook likes. The result would be a “digital psychological twin” of a person—a tool that doctors and family members could consult to guide a person’s medical care. It’s not yet clear what this would look like in practice, but the team hopes to build and test the tool before refining it.

The researchers call their tool a personalized patient preference predictor, or P4 for short. In theory, if it works as they hope, it could be more accurate than the previous version of the tool—and more accurate than human surrogates, says Wendler. It could be more reflective of a patient’s current thinking than an advance directive, which might have been signed a decade beforehand, says Earp.

A better bet?A tool like the P4 could also help relieve the emotional burden surrogates feel in making such significant life-or-death decisions about their family members, which can sometimes leave people with symptoms of post-traumatic stress disorder, says Jennifer Blumenthal-Barby, a medical ethicist at Baylor College of Medicine in Texas.

Some surrogates experience “decisional paralysis” and might opt to use the tool to help steer them through a decision-making process, says Kaplan. In cases like these, the P4 could help ease some of the burden surrogates might be experiencing, without necessarily giving them a black-and-white answer. It might, for example, suggest that a person was “likely” or “unlikely” to feel a certain way about a treatment, or give a percentage score indicating how likely the answer is to be right or wrong.

Kaplan can imagine a tool like the P4 being helpful in cases like Sophie’s, where various family members might have different opinions on a person’s medical care. In those cases, the tool could be offered to these family members, ideally to help them reach a decision together.

It could also help guide decisions about care for people who don’t have surrogates. Kaplan is an internal-medicine physician at Ben Taub Hospital in Houston, a “safety net” hospital that treats patients whether or not they have health insurance. “A lot of our patients are undocumented, incarcerated, homeless,” she says. “We take care of patients who basically can’t get their care anywhere else.”

These patients are often in dire straits and at the end stages of diseases by the time Kaplan sees them. Many of them aren’t able to discuss their care, and some don’t have family members to speak on their behalf. Kaplan says she could imagine a tool like the P4 being used in situations like these, to give doctors a little more insight into what the patient might want. In such cases, it might be difficult to find the person’s social media profile, for example. But other information might prove useful. “If something turns out to be a predictor, I would want it in the model,” says Wendler. “If it turns out that people’s hair color or where they went to elementary school or the first letter of their last name turns out to [predict a person’s wishes], then I’d want to add them in.”

This approach is backed by preliminary research from Earp and his colleagues, who have started running surveys to find out how individuals might feel about using the P4. This research is ongoing, but early responses suggest that people would be willing to try the model if there were no human surrogates available. Earp says he feels the same way. He also says that if the P4 and a surrogate were to give different predictions, “I’d probably defer to the human that knows me, rather than the model.”

Not a humanEarp’s feelings betray a gut instinct many others will share: that these huge decisions should ideally be made by a human. “The question is: How do we want end-of-life decisions to be made, and by whom?” says Georg Starke, a researcher at the Swiss Federal Institute of Technology Lausanne. He worries about the potential of taking a techno-solutionist approach and turning intimate, complex, personal decisions into “an engineering issue.”

Bryanna Moore, an ethicist at the University of Rochester, says her first reaction to hearing about the P4 was: “Oh, no.” Moore is a clinical ethicist who offers consultations for patients, family members, and hospital staff at two hospitals. “So much of our work is really just sitting with people who are facing terrible decisions … they have no good options,” she says. “What surrogates really need is just for you to sit with them and hear their story and support them through active listening and validating [their] role … I don’t know how much of a need there is for something like this, to be honest.”

Moore accepts that surrogates won’t always get it right when deciding on the care of their loved ones. Even if we were able to ask the patients themselves, their answers would probably change over time. Moore calls this the “then self, now self” problem.

And she doesn’t think a tool like the P4 will necessarily solve it. Even if a person’s wishes were made clear in previous notes, messages, and social media posts, it can be very difficult to know how you’ll feel about a medical situation until you’re in it. Kaplan recalls treating an 80-year-old man with osteoporosis who had been adamant that he wanted to receive chest compressions if his heart were to stop beating. But when the moment arrived, his bones were too thin and brittle to withstand the compressions. Kaplan remembers hearing his bones cracking “like a toothpick,” and the man’s sternum detaching from his ribs. “And then it’s like, what are we doing? Who are we helping? Could anyone really want this?” says Kaplan.

There are other concerns. For a start, an AI trained on a person’s social media posts may not end up being all that much of a “psychological twin.” “Any of us who have a social media presence know that often what we put on our social media profile doesn’t really represent what we truly believe or value or want,” says Blumenthal-Barby. And even if we did, it’s hard to know how these posts might reflect our feelings about end-of-life care—many people find it hard enough to have these discussions with their family members, let alone on public platforms.

As things stand, AI doesn’t always do a great job of coming up with answers to human questions. Even subtly altering the prompt given to an AI model can leave you with an entirely different response. “Imagine this happening for a fine-tuned large language model that’s supposed to tell you what a patient wants at the end of their life,” says Starke. “That’s scary.”

On the other hand, humans are fallible, too. Vasiliki Rahimzadeh, a bioethicist at Baylor College of Medicine, thinks the P4 is a good idea, provided it is rigorously tested. “We shouldn’t hold these technologies to a higher standard than we hold ourselves,” she says.

Earp and Wendler acknowledge the challenges ahead of them. They hope the tool they build can capture useful information that might reflect a person’s wishes without violating privacy. They want it to be a helpful guide that patients and surrogates can choose to use, but not a default way to give black-and-white final answers on a person’s care.

Even if they do succeed on those fronts, they might not be able to control how such a tool is ultimately used. Take a case like Sophie’s, for example. If the P4 were used, its prediction might only serve to further fracture family relationships that are already under pressure. And if it is presented as the closest indicator of a patient’s own wishes, there’s a chance that a patient’s doctors might feel legally obliged to follow the output of the P4 over the opinions of family members, says Blumenthal-Barby. “That could just be very messy, and also very distressing, for the family members,” she says.

“What I’m most worried about is who controls it,” says Wendler. He fears that hospitals could misuse tools like the P4 to avoid undertaking costly procedures, for example. “There could be all kinds of financial incentives,” he says.

Everyone contacted by MIT Technology Review agrees that the use of a tool like the P4 should be optional, and that it won’t appeal to everyone. “I think it has the potential to be helpful for some people,” says Earp. “I think there are lots of people who will be uncomfortable with the idea that an artificial system should be involved in any way with their decision making with the stakes being what they are.”

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This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Talking about money can be difficult, but it’s a crucial piece of the puzzle when it comes to climate tech.

I’ve been thinking more about the financial piece of climate innovation since my colleague James Temple sat down for a chat with Mike Schroepfer, former CTO of Meta and a current climate tech investor. They talked about Schroepfer’s philanthropic work as well as his climate-tech venture firm, Gigascale Capital. (I’d highly recommend reading the full Q&A here.)

In their conversation, Schroepfer spoke about investing in companies not solely because of their climate promises, but because they can deliver a cheaper, better product that happens to have benefits for climate action too.

This all got me thinking about what we can expect from new technologies financially. What do they need to do to compete, and how quickly can they do so?

Look through the portfolio of a climate-focused venture capital firm or walk around a climate-tech conference, and you’ll be struck by the creativity and straight-up brilliance of some of the proposed technologies.

But in order to survive, they need a lot more than a good idea, as my colleague David Rotman pointed out in a story from December outlining six takeaways from this century’s first boom in climate tech. Countless companies rose to stardom with shiny new ideas starting around 2006 before crashing and failing by 2013.

As David put it, there are lessons in that rise and fall for today’s boom in climate technology: “The brilliance of many new climate technologies is evident, and we desperately need them. But none of that will ensure success. Venture-backed startups will need to survive on the basis of economics and financial advantages, not good intentions.”

Often, companies looking to help address climate change with new products are competing with an established industry. These newcomers must contend with what Bill Gates has called the “green premium.”

The green premium is the cost difference between a cheaper product that increases pollution and a more expensive alternative that offers climate benefits. In order to get people on board with new technologies, we need to close that gap.

As Gates has outlined in his writings on this topic, there are basically two ways to do this: We need to find ways to either increase the cost of polluting products or cut the cost of the version that causes little to no climate pollution.

Some policies aim to go after the first of these options—the European Union has put a price on carbon, raising the cost of fossil-fuel-based products, for example. But relying on policy can leave companies at the whims of political winds in markets like the US.

So that leaves the other option: New technology needs to get cheaper.

As Schroepfer explained in his chat with James, one of the focuses at his venture firm, Gigascale Capital, is picking companies that can compete on economics or offer other benefits to customers. As he put it, a company should basically be saying: “Hey, this is a better product. [whispers] By the way, it’s better for the environment.”

It’s unrealistic to expect companies to have better, cheaper products right out of the gate, Schroepfer acknowledges. But he says that the team is looking for companies that can—over the course of a relatively short, roughly five-to-10-year period—grow to compete on cost, or even gain a cost advantage over the alternatives.

Schroepfer points to batteries and solar power as examples of technologies that are competitive today. When it’s available, electricity produced with solar panels is the cheapest on the planet. Batteries are 90% less expensive than they were just 15 years ago.

But these cases reveal the tricky thing about the green premium: Many new technologies can eventually make up the gap, but it can take much longer than businesses and investors are willing to wait. Solar panels and lithium-ion batteries were available commercially in the 1990s, but it’s taken until now to get to the point where they’re cheap and widespread.

Some technologies just getting started today could be the batteries and solar power of the 2040s, if we’re willing to invest the time and money to get them there. And I already see a few instances where people are willing to pay more for climate-friendly products today, in part because of hopes for their future.

One example that comes to mind is low-emissions steel. H2 Green Steel, a Swedish company working to make steel without fossil fuels, says it has customers who have agreed to pay 20% to 30% more for its products than metal made with fossil fuels. But that’s just the price today: Some reports predict that these technologies will be able to compete on cost by 2040 or 2050.

Most new technologies designed to address climate change will need to make a case for themselves in the market. The question for the rest of us: How much support and time are we willing to put in to give them the best shot of getting there?

Now read the rest of The SparkRelated readingFor more on what the former Meta CTO has been up to in climate, read the full Q&A here. There’s a whole lot more to unpack, including work on glacier stabilization, ocean-based carbon removal, and even solar geoengineering.

For more on the lessons that companies can take away from the first cleantech boom, give this story from my colleague David Rotman a read.

Another thingThe US Department of Energy is putting $33 million into nine concentrating solar projects, as my colleague James Temple reported exclusively last week.

Concentrating solar power uses mirrors to direct sunlight, which heats up some target material. It’s not a new technology, and the DOE has been funding efforts to get it going since the 1970s. But it could be useful in industries from food and beverages to low-carbon fuels. Read the full story here.

Keeping up with climate Western battery startups could be in big trouble. While new chemistries and alternative architectures attracted a lot of investor attention a few years ago, the companies are now facing the reality of competing with massive existing manufacturers. (The Information)

California’s largest wildfire of the year has burned well over 300,000 acres so far. Climate change has helped create the conditions that supercharge blazes. (Inside Climate News)

The UAE has been trying to juice up rainfall with high-tech cloud seeding operations. But the whole thing may be more about the show than the science—check out this great deep dive for more. (Wired)

Congestion pricing plans—like the one recently proposed and then abandoned in New York City—can be unpopular with voters. Yet people generally come around once they start to see the benefits. Here’s an in-depth look at how attitudes toward these plans change over time. (Grist)

Air New Zealand backed down from a goal to cut its emissions nearly 30% by the end of the decade. The first major airline to walk back such a promise, the company points to a lack of supply for alternative fuels, as well as delays in new aircraft deliveries. (BBC)

Global methane emissions are climbing at the quickest pace in decades. The powerful greenhouse gas is responsible for over half the warming we’ve experienced so far. (The Guardian)

Demand for air conditioning is swelling in Africa. But the industry isn’t well regulated, and some residents are struggling to get reliable systems and keep harmful refrigerant gases from leaking. (Associated Press)

Southeast Asia is home to a fleet of relatively new coal power plants. Pulling these facilities off the grid early could be a major step to cutting emissions from global electricity production. (Cipher News)

Correction: an earlier version of this story misstated the name of Mike Schroepfer’s firm. It is Gigascale Capital.

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OpenAI is rolling out an advanced AI chatbot that you can talk to. It’s available today—at least for some.

The new chatbot represents OpenAI’s push into a new generation of AI-powered voice assistants in the vein of Siri and Alexa, but with far more capabilities to enable more natural, fluent conversations. It is a step in the march to more fully capable AI agents. The new ChatGPT voice bot can tell what different tones of voice convey, responds to interruptions, and is able to reply to queries in real time. It has also been trained to sound more natural and use voices to convey a wide range of different emotions.

The voice mode is powered by OpenAI’s new GPT-4o model, which combines voice, text, and vision capabilities. The company is initially launching the chatbot to a “small group of users” paying for ChatGPT Plus to gather feedback and says it will make it available to all ChatGPT Plus subscribers this fall. A ChatGPT Plus subscription costs $20 a month. OpenAI says it will notify customers who are part of the first rollout wave in the ChatGPT app and provide instructions on how to use the new model.

The new voice feature, which was announced in May, is being launched a month later than originally planned because the company said it needed more time to improve safety features, such as the model’s ability to detect and refuse unwanted content. The company also said it was preparing its infrastructure to offer real-time responses to millions of users.

OpenAI says it has tested the model’s voice capabilities with more than 100 external red-teamers, who were tasked with probing the model for flaws. These testers spoke a total of 45 languages and represented 29 countries, according to OpenAI.

The company says it has put several safety mechanisms in place. In a move that aims to prevent the model from being used to create audio deepfakes, OpenAI has created four preset voices in collaboration with voice actors. GPT-4o will not impersonate or generate other people’s voices.

When OpenAI first introduced GPT-4o, the company faced a backlash over its use of a voice called “Sky,” which sounded a lot like the actress Scarlett Johansson. Johansson released a statement saying the company had reached out to her for permission to use her voice for the model, which she declined. She said she was shocked to hear a voice “eerily similar” to hers in the model’s demo. OpenAI has denied that the voice is Johansson’s but has paused the use of Sky.

The company is also embroiled in several lawsuits over alleged copyright infringement. OpenAI says it has adopted filters that recognize and block requests to generate music or other copyrighted audio. OpenAI also says it has applied the same safety mechanisms it uses in its text-based model to GPT-4o to prevent it from breaking laws and generating harmful content.

Down the line, OpenAI plans to include more advanced features, such as video and screen sharing, which could make the assistant more useful. In its May demo, employees pointed their phone cameras at a piece of paper and asked the AI model to help them solve math equations. They also shared their computer screens and asked the model to help them solve coding problems. OpenAI says these features will not be available now but at an unspecified later date.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How the US and its allies can rebuild economic security

—Edlyn V. Levine is CEO and co-founder of a stealth-mode technology start up and an affiliate at MIT Sloan School of Management and the Department of Physics at Harvard University.

Fiona Murray is the William Porter (1967) Professor of Entrepreneurship at the MIT School of Management and Vice Chair of the NATO Innovation Fund.

A country’s economic security—its ability to generate both national security and economic prosperity—is grounded in it having technological capabilities that outpace those of its adversaries and complement those of its allies.

Though this is a principle well known throughout history, the move over the last few decades toward globalization and offshoring has made ensuring a nation state’s security and economic prosperity increasingly problematic.

For the US and its allies in NATO, a particular problem has emerged: a “missing middle” in technology investment. Insufficient capital is allocated toward the maturation of breakthroughs in critical technologies to ensure that they can be deployed at scale. Here’s what we need to do to fix it.

How machines that can solve complex math problems might usher in more powerful AI

Last Thursday, Google DeepMind announced it had built AI systems that worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad, a prestigious competition for high school students.

It’s the first time any AI system has ever achieved such a high success rate on these kinds of math problems.

But this breakthrough is not just about math. In fact, it signals an exciting new development in the kind of AI we can now build. Read the full story.

—Melissa Heikkilä

This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Apple has released the first version of its suite of AI featuresIt doesn’t yet include OpenAI’s ChatGPT, but is expected to eventually. (CNBC)
+ The early preview is likely to contain a few bugs. (The Verge)
+ Apple used Google’s chips, not Nvidia’s, to train AI models. (Reuters)
+ Apple is promising personalized AI in a private cloud. Here’s how that will work. (MIT Technology Review)

2 SpaceX hopes to launch a rocket off Australia’s coast If the country agrees, that is. (Reuters)

3 The oil industry has massively overstated the efficacy of carbon captureIt publicly presents the technology as a silver bullet. Behind the scenes, its enthusiasm is much more muted. (Vox)
+ The world’s on the verge of a carbon storage boom. (MIT Technology Review)

4 The global chip war is escalating
Countries are now prioritizing data centers on their own soil. (FT $)
+ Quantum computers could become increasingly commonplace in the centers. (IEEE Spectrum)
+ Taiwanese chipmaker TSMC is building its first European data center. (Nikkei Asia $)
+ What’s next in chips. (MIT Technology Review)

5 AI companies keep launching new crawler bots
Which is seriously annoying for the websites trying to keep on top of blocking them. (404 Media)

6 Weight-loss drugs could help smokers kick the habitIt’s further evidence that semaglutide could help treat addiction. (New Scientist $)
+ Knock-off versions of the drugs are rife—but they aren’t illegal. (Undark Magazine)
+ This vibrating weight-loss pill seems to work—in pigs. (MIT Technology Review)

7 Air pollution is preventing bees from pollinating
Which is seriously bad news for plant reproduction. (Knowable Magazine)
+ How robotic honeybees and hives could help the species fight back. (MIT Technology Review)

8 We’re getting much better at predicting the weatherThanks to AI’s ability to spot weather patterns we might otherwise have missed. (NYT $)
+ Google’s new weather prediction system combines AI with traditional physics. (MIT Technology Review)

9 Meet the singles cobbling together their own dating platforms
They’re searching for human connections in a sea of algorithms. (Bustle)

10 Tracking polar bears is far from easy
But a new sticky sensor from the company behind Post It Notes could change everything. (Fast Company $)

Quote of the day

“It’s a vibe. I mean, get this guy a chain.”

—Mark Zuckerberg admires the dress sense of Jensen Huang, Nvidia’s CEO and Silicon Valley’s sole style icon, as the pair swap leather jackets, TechCrunch reports.

The big story

How to stop a state from sinking

April 2024

In a 10-month span between 2020 and 2021, southwest Louisiana saw five climate-related disasters, including two destructive hurricanes. As if that wasn’t bad enough, more storms are coming, and many areas are not prepared.

But some government officials and state engineers are hoping there is an alternative: elevation. The $6.8 billion Southwest Coastal Louisiana Project is betting that raising residences by a few feet will keep Louisianans in their communities.

Ultimately, it’s something of a last-ditch effort to preserve this slice of coastline, even as some locals pick up and move inland and as formal plans for managed retreat become more popular in climate-­vulnerable areas across the country and the rest of the world. Read the full story.

—Xander Peters

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• How to think like an Olympian and smash your goals.
• Jeff Goldblum’s jazz cover of Taylor Swift’s Lover is an absolute treat.
• If you missed it over the weekend, Simone Biles’ dazzling return to the Olympics is a sight to behold.
• Squishy the kitten is possibly the world’s luckiest cat, after a narrow brush with a waste compactor.

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A country’s economic security—its ability to generate both national security and economic prosperity—is grounded in it having significant technological capabilities that outpace those of its adversaries and complement those of its allies. Though this is a principle well known throughout history, the move over the last few decades toward globalization and offshoring of technologically advanced industrial capacity has made ensuring a nation state’s security and economic prosperity increasingly problematic. A broad span of technologies ranging from automation and secure communications to energy storage and vaccine design are the basis for wider economic prosperity—and high priorities for governments seeking to maintain national security. However, the necessary capabilities do not spring up overnight. They rely upon long decades of development, years of accumulated knowledge, and robust supply chains.

For the US and, especially, its allies in NATO, a particular problem has emerged: a “missing middle” in technology investment. Insufficient capital is allocated toward the maturation of breakthroughs in critical technologies to ensure that they can be deployed at scale. Investment is allocated either toward the rapid deployment of existing technologies or to scientific ideas that are decades away from delivering practical capability or significant economic impact (for example, quantum computers). But investment in scaling manufacturing technologies, learning while doing, and maturing of emerging technologies to contribute to a next-generation industrial base, is too often absent. Without this middle-ground commitment, the United States and its partners lack the production know-how that will be crucial for tomorrow’s batteries, the next generation of advanced computing, alternative solar photovoltaic cells, and active pharmaceutical ingredients.

While this once mattered only for economic prosperity, it is now a concern for national security too—especially given that China has built strong supply chains and other domestic capabilities that confer both economic security and significant geopolitical leverage.

Consider drone technology. Military doctrine has shifted toward battlefield technology that relies upon armies of small, relatively cheap products enabled by sophisticated software—from drones above the battlefield to autonomous boats to CubeSats in space.

Drones have played a central role in the war in Ukraine. First-person viewer (FPV) drones—those controlled by a pilot on the ground via a video stream—are often strapped with explosives to act as precision kamikaze munitions and have been essential to Ukraine’s frontline defenses. While many foundational technologies for FPV drones were pioneered in the West, China now dominates the manufacturing of drone components and systems, which ultimately enables the country to have a significant influence on the outcome of the war.

When the history of the war in Ukraine is written, it will be taught as the first true “drone war.” But it should also be understood as an industrial wake-up call: a time when the role of a drone’s component parts was laid bare and the supply chains that support this technology—the knowledge, production operations, and manufacturing processes—were found wanting. Heroic stories will be told of Ukrainian ingenuity in building drones with Chinese parts in basements and on kitchen tables, and we will hear of the country’s attempt to rebuild supply chains dominated by China while in the midst of an existential fight for survival. But in the background, we will also need to understand the ways in which other nations, especially China, controlled the war through long-term economic policies focused on capturing industrial capacity that the US and its allies failed to support through to maturity.

Disassemble one of the FPV drones found across the battlefields of Ukraine and you will find about seven critical subsystems: power, propulsion, flight control, navigation and sensors (which gather location data and other information to support flight), compute (the processing and memory capacity needed to analyze the vast array of information and then support operations), communications (to connect the drone to the ground), and—supporting it all—the airframe.

We have created a bill of materials listing the components necessary to build an FPV drone and the common suppliers for those parts.

China’s manufacturing dominance has resulted in a domestic workforce with the experience to achieve process innovations and product improvements that have no equal in the West. And it has come with the sophisticated supply chains that support a wide range of today’s technological capabilities and serve as the foundations for the next generation. None of that was inevitable. For example, most drone electronics are integrated on printed circuit boards (PCBs), a technology that was developed in the UK and US.However, first-mover advantage was not converted into long-term economic or national security outcomes, and both countries have lost the PCB supply chain to China.

Propulsion is another case in point. The brushless DC motors used to convert electrical energy from batteries into mechanical energy to rotate drone propellers were invented in the US and Germany. The sintered permanent neodymium (NdFeB) magnets used in these motors were invented in Japan and the US. Today, to our knowledge, all brushless DC motors for drones are made in China. Similarly, China dominates all steps in the processing and manufacture of NdFeB magnets, accounting for 92% of global NdFeB magnet and magnet alloy markets.

The missing middle of technology investment—insufficient funding for commercial production—is evident in each and every one of these failures, but the loss of expertise is an added dimension. For example, lithium polymer (LiPo) batteries are at the heart of every FPV drone. LiPo uses a solid or gel polymer electrolyte and achieves higher specific energy (energy per unit of weight)—a feature that is crucial for lightweight drones. Today, you would be hard-pressed to find a LiPo battery that was not manufactured in China. The experienced workforce behind these companies has contributed to learning curves that have led to a 97% drop in the cost of lithium-ion batteries and a simultaneous 300%-plus increase in battery energy density over the past three decades.

China’s dominance in LiPo batteries for drones reflects its overall dominance in Li-ion manufacturing. China controls approximately 75% of global lithium-ion capacity—the anode, cathode, electrolyte, and separator subcomponents as well as the assembly into a single unit. It dominates the manufacture of each of these subcomponents, producing over 85% of anodes and over 70% of cathodes, electrolytes, and separators. China also controls the extraction and refinement of minerals needed to make these subcomponents.

Again, this dominance was not inevitable. Most of the critical breakthroughs needed to invent and commercialize Li-ion batteries were made by scientists in North America and Japan. But in comparison to the US and Europe (at least until very recently), China has taken a proactive stance to coordinate, support, and co-invest with strategic industries to commercialize emerging technologies. China’s Ministry of Industry and Information Technology has been at pains to support these domestic industries.

The case of Li-ion batteries is not an isolated one. The shift to Chinese dominance in the underlying electronics for FPV drones coincides with the period beginning in 2000, when Shenzhen started to emerge as a global hub for low-cost electronics. This trend was amplified by US corporations from Apple, for which low-cost production in China has been essential, to General Electric, which also sought low-cost approaches to maintain the competitive edge of its products. The global nature of supply chains was seen as a strength for US companies, whose comparative advantage lay in the design and integration of consumer products (such as smartphones) with little or no relevance for national security. Only a small handful of “exquisite systems” essential for military purposes were carefully developed within the US. And even those have relied upon global supply chains.

While the absence of the high-tech industrial capacity needed for economic security is easy to label, it is not simple to address. Doing so requires several interrelated elements, among them designing and incentivizing appropriate capital investments, creating and matching demand for a talented technology workforce, building robust industrial infrastructure, ensuring visibility into supply chains, and providing favorable financial and regulatory environments for on- and friend-shoring of production. This is a project that cannot be done by the public or the private sector alone. Nor is the US likely to accomplish it absent carefully crafted shared partnerships with allies and partners across both the Atlantic and the Pacific.

The opportunity to support today’s drones may have passed, but we do have the chance to build a strong industrial base to support tomorrow’s most critical technologies—not simply the eye-catching finished assemblies of autonomous vehicles, satellites, or robots but also their essential components. This will require attention to our manufacturing capabilities, our supply chains, and the materials that are the essential inputs. Alongside a shift in emphasis to our own domestic industrial base must come a willingness to plan and partner more effectively with allies and partners.

If we do so, we will transform decades of US and allied support for foundational science and technology into tomorrow’s industrial base vital for economic prosperity and national security. But to truly take advantage of this opportunity, we need to value and support our shared, long-term economic security. And this means rewarding patient investment in projects that take a decade or more, incentivizing high-capital industrial activity, and maintaining a determined focus on education and workforce development—all within a flexible regulatory framework.

Edlyn V. Levine is CEO and co-founder of a stealth-mode technology start up and an affiliate at MIT Sloan School of Management and the Department of Physics at Harvard University. Levine was co-founder and CSO of America’s Frontier Fund, and formerly Chief Technologist for the MITRE Corporation.

Fiona Murray is the William Porter (1967) Professor of Entrepreneurship at the MIT School of Management where she works at the intersection of critical technologies, entrepreneurship, and geopolitics. She is the Vice Chair of the NATO Innovation Fund—a multi-sovereign venture fund for defense, security and resilience, and served for a decade on the UK Prime Minister’s Council on Science and Technology.

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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It’s been another big week in AI. Meta updated its powerful new Llama model, which it’s handing out for free, and OpenAI said it is going to trial an AI-powered online search tool that you can chat with, called SearchGPT.

But the news item that really stood out to me was one that didn’t get as much attention as it should have. It has the potential to usher in more powerful AI and scientific discovery than previously possible.

Last Thursday, Google DeepMind announced it had built AI systems that can solve complex math problems. The systems—called AlphaProof and AlphaGeometry 2—worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad, a prestigious competition for high school students. Their performance was the equivalent of winning a silver medal. It’s the first time any AI system has ever achieved such a high success rate on these kinds of problems. My colleague Rhiannon Williams has the news here.

Math! I can already imagine your eyes glazing over. But bear with me. This announcement is not just about math. In fact, it signals an exciting new development in the kind of AI we can now build. AI search engines that you can chat with may add to the illusion of intelligence, but systems like Google DeepMind’s could improve the actual intelligence of AI. For that reason, building systems that are better at math has been a goal for many AI labs, such as OpenAI.

That’s because math is a benchmark for reasoning. To complete these exercises aimed at high school students, the AI system needed to do very complex things like planning to understand and solve abstract problems. The systems were also able to generalize, allowing them to solve a whole range of different problems in various branches of mathematics.

“What we’ve seen here is that you can combine [reinforcement learning] that was so successful in things like AlphaGo with large language models and produce something which is extremely capable in the space of text,” David Silver, principal research scientist at Google DeepMind and indisputably a pioneer of deep reinforcement learning, said in a press briefing. In this case, that capability was used to construct programs in the computer language Lean that represent mathematical proofs. He says the International Mathematical Olympiad represents a test for what’s possible and paves the way for further breakthroughs.

This same recipe could be applied in any situation with really clear, verified reward signals for reinforcement-learning algorithms and an unambiguous way to measure correctness as you can in mathematics, said Silver. One potential application would be coding, for example.

Now for a compulsory reality check: AlphaProof and AlphaGeometry 2 can still only solve hard high-school-level problems. That’s a long way away from the extremely hard problems top human mathematicians can solve. Google DeepMind stressed that its tool did not, at this point, add anything to the body of mathematical knowledge humans have created. But that wasn’t the point.

“We are aiming to provide a system that can prove anything,” Silver said. Think of an AI system as reliable as a calculator, for example, that can provide proofs for many challenging problems, or verify tests for computer software or scientific experiments. Or perhaps build better AI tutors that can give feedback on exam results, or fact-check news articles.

But the thing that excites me most is what Katie Collins, a researcher at the University of Cambridge who specializes in math and AI (and was not involved in the project), told Rhiannon. She says these tools create and evaluate new problems, motivate new people to enter the field, and spark more wonder. That’s something we definitely need more of in this world.

Now read the rest of The AlgorithmDeeper LearningA new tool for copyright holders can show if their work is in AI training data

Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set. Now they have a new way to prove it: “copyright traps.” These are pieces of hidden text that let you mark written content in order to later detect whether it has been used in AI models or not.

Why this matters: Copyright traps tap into one of the biggest fights in AI. A number of publishers and writers are in the middle of litigation against tech companies, claiming their intellectual property has been scraped into AI training data sets without their permission. The idea is that these traps could help to nudge the balance a little more in the content creators’ favor. Read more from me here.

Bits and BytesAI trained on AI garbage spits out AI garbage
New research published in Nature shows that the quality of AI models’ output gradually degrades when it’s trained on AI-generated data. As subsequent models produce output that is then used as training data for future models, the effect gets worse. (MIT Technology Review)

OpenAI unveils SearchGPT
The company says it is testing new AI search features that give you fast and timely answers with clear and relevant sources cited. The idea is for the technology to eventually be incorporated into ChatGPT, and CEO Sam Altman says it’ll be possible to do voice searches. However, like many other AI-powered search services, including Google’s, it’s already making errors, as the Atlantic reports.
(OpenAI)

AI video generator Runway trained on thousands of YouTube videos without permission
Leaked documents show that the company was secretly training its generative AI models by scraping thousands of videos from popular YouTube creators and brands, as well as pirated films. (404 media)

Meta’s big bet on open-source AI continues
Meta unveiled Llama 3.1 405B, the first frontier-level open-source AI model, which matches state-of-the-art models such as GPT-4 and Gemini in performance. In an accompanying blog post, Mark Zuckerberg renewed his calls for open-source AI to become the industry standard. This would be good for customization, competition, data protection, and efficiency, he argues. It’s also good for Meta, because it leaves competitors with less of an advantage in the AI space. (Facebook)

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Controversial CRISPR scientist promises “no more gene-edited babies” until society comes around

He Jiankui, the Chinese biophysicist whose controversial 2018 experiment led to the birth of three gene-edited children, says he’s returned to work on the concept of altering the DNA of people at conception, but with a difference.

This time around, he says, he will restrict his research to animals and nonviable human embryos. He will not try to create a pregnancy, at least until society comes to accept his vision for “genetic vaccines” against common diseases.

During an exclusive subscribers-only live interview with MIT Technology Review last week, He defended his past research and revealed he only has one regret. Read more about what he had to say.

—Antonio Regalado

From Meta CTO to climate tech investor: Mike Schroepfer on his big pivot

The more Mike Schroepfer learned more about global warming in 2020, the more he came to believe he had a role to play. By leveraging his technical expertise and financial resources, the then chief technology officer of Meta could accelerate essential research and help us prepare for the escalating dangers.

As the threat of climate change consumed more and more of his time, he decided to step down from his CTO role in 2021. He has since launched several new climate tech initiatives, including one exploring the contentious idea of solar geoengineering.

Last week, Schroepfer sat down with MIT Technology Review to discuss his approach to the problem, why he’s willing to spend money on controversial climate interventions, and what AI and the presidential election could mean for progress on clean energy. Read the full story.

—James Temple

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Elon Musk shared an edited video of Kamala Harris on X
In an apparent violation of the company’s own rules on sharing synthetic media. (WSJ $)
+ Musk failed to disclose that the video had been altered. (NYT $)
+ Harris’ campaign accused Musk of spreading ‘manipulated lies.’ (The Guardian)
+ An AI startup made a hyperrealistic deepfake of me that’s so good it’s scary. (MIT Technology Review)

2 Apple has pushed back the launch of its AI featuresApple Intelligence will no longer roll out in September as originally planned. (Bloomberg $)
+ Hopefully this’ll give Apple more time to catch bugs. (The Information $)
+ Apple is promising personalized AI in a private cloud. Here’s how that will work. (MIT Technology Review)

3 The Democrats are seeking to build bridges with the crypto industry
Relations have been rocky in the past few years, to say the least. (FT $)
+ Donald Trump has already made a major play for the bitcoin faithful. (Wired $)
+ So much so, Trump-themed memecoins are back. (NYT $)

4 France’s internet cables have been severed
It’s the latest attack on the country’s infrastructure during the Olympics. (Bloomberg $)+ The French train system was targeted last week. (Vox)

5 We may have just made an important alien discovery
Unfortunately, we’ll have to wait until 2040 to be sure. (The Atlantic $)

6 Plug-and-play solar panels are all the rage in GermanyThe lightweight panels are making it easy for civilians to generate their own electricity. (NYT $)
+ Offshore wind farms are on the rise, too. (Hakai Magazine)
+ The race to get next-generation solar technology on the market. (MIT Technology Review)

7 China is keen to mine the ocean floorThe country is desperate to find new sources of critical minerals. (Economist $)
+ This startup uses AI to seek out metals deep in the ground. (WSJ $)
+ These deep-sea “potatoes” could be the future of mining for renewable energy. (MIT Technology Review)

8 Specialized dating apps are thriving
Tinder and Bumble are out, Grindr and Feeld are in. (FT $)

9 It’s time to embrace ‘underconsumption core’Gen Z shoppers are turning their backs on unnecessary consumerism. (Insider $)

10 Silicon Valley startups are inviting founders to roast them
Blunt feedback can be the best way to avoid expensive future pitfalls—if you can hack it. (WP $)

Quote of the day

“It’s not just about posting a coconut meme — it’s about making the conversation about abortion.”

—Danielle Butterfield, the executive director of political action committee Priorities USA, explains to the New York Times how Kamala Harris should handle her burgeoning online fandom.

The big story

This fuel plant will use agricultural waste to combat climate change

February 2022A startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide, starting from 2024.

It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.

It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story.

—James Temple

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Hundreds of Kate Bush fans gathered to recreate her iconic Wuthering Heights dance this weekend—check out the dance itself here.
• Andy Morton’s got the right idea—he’s tried 50,000 kinds of beer (thanks Jess!)
• The Netherlands’ Bosch Parade looks suitably surreal.
• These pancakes look delicious.

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As the pandemic locked down cities in early 2020, Mike Schroepfer, then the chief technology officer of Meta, found himself with more free time than he’d ever had in his career.

In quiet moments that would have been filled with work travel, social events, or his children’s school activities, he reflected on how well humanity can pull together in the face of an acute crisis—implementing public health measures, mass-producing tests, and turbocharging the development of vaccines.

But the experience also reinforced his view that we are particularly bad at addressing slow-motion catastrophes like climate change, where the risks are grave and growing but mostly looming in the distance.

As he learned more about global warming, Schroepfer came to believe he had a role to play: By leveraging his technical expertise and financial resources, he could accelerate essential research and help society develop the understanding and tools we may need to avoid or prepare for the escalating dangers.

As the threat of climate change consumed more and more of his time, he decided in 2021 to step down from his CTO role and dedicate himself to addressing the challenge through both philanthropic and for-profit efforts. (He remains a senior fellow at Meta.)

I’m willing to take a lot of risks that these things just don’t work and that people make fun of me for wasting my money, and I’m willing to stick it out and keep trying.

Mike Schroepfer

In May 2023, he announced Gigascale Capital, a venture fund backing early-stage climate tech companies, including startups working to commercialize fusion, cut landfill emissions, and reduce methane pollution from cattle. That summer, he also launched Carbon to Sea, a $50 million nonprofit effort to accelerate research on ocean alkalinity enhancement (OAE), a means of drawing down more planet-warming carbon dioxide into the oceans by adding substances like olivine, basalt, or lime.

This year, as MIT Technology Review first reported, he launched Outlier Projects, which is donating grants to research groups working in three areas: removing greenhouse gas from the air, preventing glaciers from collapsing, and exploring the contentious idea of solar geoengineering, a catch-all term for a variety of ways that we might be able to cool the planet by casting more heat back into space.

Last week, Schroepfer sat down with MIT Technology Review in his offices at Gigascale Capital, in downtown Palo Alto, California, to discuss his approach to the problem, why he’s willing to spend money on controversial climate interventions, and what AI and the presidential election could mean for progress on clean energy.

This interview has been edited for length and clarity.

Is there a unifying philosophy across your climate efforts?

The foundation is that when you get a set of people and you get them all pointed in the same direction, and they wake up every morning and say “We’re going to go solve this problem and nothing else matters,” it’s often surprising what they can get done.

I think the other unifying theme, which also unifies my career, is: Technology is the only thing I have seen that removes constraints.

I just saw this again and again and again at Meta, where we would reduce cost, improve efficiency, develop a new technology, and then a thing that was a hard constraint before just got removed.

Through the proper development and deployment of technology, we can remove either-or decisions and move to the world I want to move to, which is a yes-and decision.

How do we bring the standard of living of 8 billion people up to those of the West and have a planet that my children can live on? That’s really the question, and the only answer I can see is technology.

There are a variety of potential approaches to ocean carbon removal—everything from sinking kelp, which doesn’t seem to be working that well, to iron fertilization and other things. So why enhanced ocean alkalinity? Why was that the one where you said, let’s dive deep?

In reading about all the different approaches, it stood out as the most likely, the most scalable, the most cost effective, and the most permanent, yet the least well understood.

And so it was super high impact if it works, but we need to know more.

I had no prior bias to this. I like kelp. I like all these things. I’m not a one-solution sort of person. I want as many things to work as possible.

As an engineer, my reading of technological deployment is that the relatively elegant, simple solutions end up being the ones that scale. And OAE is about as simple as it gets.

Let’s switch gears to a touchy topic: solar geoengineering. Why did you decide that was an important area where you wanted to support research?

We did a broad search for problems that are defined as high impact, high scientific uncertainty. Those are the ones that I think fit what we’re comfortable with and good at. And as we did that search, the two—besides carbon removal—that came out were solar radiation management (SRM) and glacier stabilization.

SRM felt like an orthogonal solution because it is a way to make rapid cooling if we need to—if this becomes a humanitarian crisis.

We’re already losing lives due to heat, but it’s going to get to the point where people aren’t going to tolerate it, and the question is: What do you do at that point?

Humans are good in a crisis, but it felt like, hey, we ought to get started now. To really start doing the rigorous work to understand “Does this work? Is it effective? What are the safety concerns?” while we’re not in a crisis moment, so that we’re prepared.

You mentioned glacier restoration as well. Why was that a problem you wanted to contribute to?

Assume we solve every other problem. We remove all the carbon, we electrify everything. We’ve still got a sea-level-rise problem, mostly because of glaciers that are moving.

One of the approaches is to simply pump water out of the bottom of the glacier to remove the lubrication layer that’s causing them to move. We have glaciers with boreholes already in them that are highly instrumented, and they’re already moving. So dropping a pump in there and pumping out water is a very, very, very low-risk activity that starts to answer some basic questions, like: Does this work at all? Would it be feasible? Would it be overwhelmingly impossible because of energy or cost needs?

Whatever approach you take to it, we’re talking about a massive infrastructure project that’s just gonna be incredibly costly. On the other hand, if the Thwaites Glacier (sometimes called the Doomsday Glacier) does slide into the sea, then every city around the world, plus every low-lying nation, has to do these massive infrastructure projects.

Can we pull together as a global society to address this thing in the most efficient way, or are we just going to leave everyone to deal with it on their own?

This is where I think people underweight the power of the prototype or the power of the proof of concept.

We can talk theoretically. I can bring scientists over and they can say, “I’ve got a big spreadsheet which explains to you how expensive this is going to be.”

I don’t know. Maybe they’re right. Maybe they’re not. Instead, let’s get on a plane. And let me show you. It was moving this fast. We did this. It’s now moving this fast. Here’s the pump. We’re pumping water out.

The Thwaites Glacier.KARI SCAMBOS/NSIDCI think a lot of what my role in the world is to do is to get us to there. I’m willing to take a lot of risks that these things just don’t work and that people make fun of me for wasting my money, and I’m willing to stick it out and keep trying.

What I hope I do is put a bunch of proof points on the board, so that when the time comes that we need to start making decisions about these things, we’re not starting from scratch—we’re starting from a running start.

And you think that just having a greater amount of certainty and clarity—in terms of what the risks are, and how viable these solutions are, and what they will cost, and how we do it—can change the dynamics …

I think it does.

… where suddenly you could see nations pulling together in a way where it’s hard to imagine when there’s so much uncertainty?

Yeah. Or it goes the other way, where you decide, “Hey, we’ve had all these crazy ideas, and none of them are going to work, so we got to do something else.”

But as you say, the alternatives are moving lots of people or building big seawalls, and those are going to get pretty overwhelming pretty quickly.

My career has been putting tools in the toolbox. My job was to stock that toolbox such that when we needed it, we were ready to go. And I’m applying that same approach here, which is just like, “Hey, what are the things that I can help push forward in some way so that if we need them, or if we need to understand them, we’re a lot further along than we are today?” Right?

We’ve mostly talked about your philanthropic efforts so far, but you also set up Gigascale Capital, a venture fund. How does your investment strategy and approach differ from that of a traditional tech venture firm? For instance, are you investing over longer time horizons than the standard five to 10 years?

We’re here to prove that if you pick the right climate tech companies with the right founders, that can be an amazing business. They’re disrupting trillion-dollar industries, and so you ought to be able to get good returns on that. And that’s what’s going to be required to get a bunch of people to open up their checkbooks and really spend the trillions of dollars we need a year to solve these problems.

So we look for companies with—we’ve jokingly called it at times the “green discount.”

Those trends are freight trains that are going down the hill and are pretty hard to stop.

Mike Schroepfer

Like, “Hey, this is a better product. [whispers] By the way, it’s better for the environment.” Sort of the little asterisk if you read the fine print at the bottom.

The starting point is, the consumer wants it because it provides a lot of benefits; enterprise wants it because it’s cheaper. That is the selling point of all the products we back. And then it also happens to be a lot lower carbon, or zero carbon, compared to whatever alternative it’s displacing.

Your mentioning the green discount reminds me of Bill Gates’s green premium (the Microsoft cofounder’s thesis that it takes heavy investments in climate tech to reduce their cost premium relative to polluting products over time). There are some products, like green steel and green cement, where the alternatives are more expensive. Does that mean that you’re not investing in those areas, or is it just that you would with the hope that eventually they’ll be able to get those costs down?

Technology takes time to incubate, so no new technology out of the gate is better, faster, cheaper. But in the life cycle of the company, in five to 10 years—I have to believe, at scale, you can be cost competitive or have a cost advantage versus the alternatives. So that means that, yeah, we only invest in things that we think can either be cost competitive or have some other co-benefit that is a decision maker.

This is why I very cleanly separated philanthropic work where it’s like, “I get nothing out of this—we’re gonna send money away and hope public good, papers, knowledge gets created.”

And the venture fund is “Nope, this is the capitalistic endeavor to prove to people that if you smartly choose the right solutions, you can make money and fund the low-carbon economy.” That is the bet we’re making.

Given your recent job leading tech and AI efforts at Meta, I’m curious about your thinking about the potential tension between AI energy consumption that’s very much in the news right now and clean energy and climate goals. What do you think companies will need to do to stay on track with their own climate commitments as data centers’ energy demands rise?

Two thoughts on this.

AI is a foundational technology that can enable a lot of benefits for us moving forward. Part of why I still have an affiliation with Meta is because a lot of the work I do there is on Llama, our open-source model, which is allowing that technology to be used by lots of different people in the industry.

I think foundational technology being open is one of the ways in which humanity moves forward faster and gets more people into prosperity, which is what I care about.

In terms of energy consumption, I start with let’s get AI as fast as we can, because I think it is good.

In my time at Meta, we many, many times had multiple-orders-of-magnitude improvements in efficiency or power use.

So I think the industry right now is trying to build the best thing they can, and that consumes a lot of power and energy. I think if we get to a point where that’s a huge problem and we need to really optimize it from an efficiency standpoint, there are a lot of levers to pull there.

Schroepfer also spoke with MIT Technology Review’s James Temple about his climate philanthropy and investments during the ClimateTech conference last year. You can now watch the full interview above.And AI or no AI, if you want to electrify everything and remove all fossil fuels, we just have a tremendous amount of clean energy we need to bring on the grid, right? That problem exists whether you have AI or not. So I think it’s a little bit of an over-highlighted sideshow to the real game, which is: How do we get tens of gigawatts of clean energy onto the grid as fast as possible every year? How do we get more solar, more wind, more storage? Can we bring fusion online?

To me, these are the humanitarian game-changers; it is the sort of unlock for a lot of other things.

I hate to get political here, but in light of these recent Supreme Court decisions about federal agency powers, I am curious what you think a Trump win in November might mean for climate and clean energy progress.

The short answer is, I’m not sure.

Okay, then maybe it’s the same answer to my next question, which is: What do you think it might mean for financial opportunities in the sector, to the degree that Trump has said he would try to roll back Inflation Reduction Act incentives for EVs and other things? Do you think it could weaken the case for private investment into some of these areas?

This goes back to when you asked, What do we believe? What do we invest in?

Basically, it has to start with the business case: My product is better or cheaper. I think that investment case is durable regardless. I think these things like the IRA can accelerate things and make things easier, but if you remove them, I don’t think that eliminates the fundamental advantages some of these technologies have.

The exciting thing about this world is that an electric powertrain on a vehicle is fundamentally much more efficient than a gas power train—like 3 to 4X more efficient. So I should be able to build a product that is very cost advantaged to these petrol-burning things. There’s a bunch of issues with the scale and customer adoption and things like that, but the fundamentals are in my favor.

And I think we see this trend happening in a lot of things. Solar is the cheapest form of energy generation we’ve ever had, and that’s going to continue as we massively increase manufacturing capacity. Batteries have gone down an unbelievable cost curve. And each year, we’re making more batteries than we’ve ever made before.

One of my favorite things is Wright’s Law: this idea that as you double the scale of your production, you generally see a decrease in cost. It varies from product to product, but for batteries, it’s about 20% or so every time we double the production.

If my product gets cheaper by about 5% to 10% a year, at some point I’m gonna win. Those trends are freight trains that are going down the hill and are pretty hard to stop.

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He Jiankui, the Chinese biophysicist whose controversial 2018 experiment led to the birth of three gene-edited children, says he’s returned to work on the concept of altering the DNA of people at conception, but with a difference.

This time around, he says, he will restrict his research to animals and nonviable human embryos. He will not try to create a pregnancy, at least until society comes to accept his vision for “genetic vaccines” against common diseases.

“There will be no more gene-edited babies. There will be no more pregnancies,” he said during an online roundtable discussion hosted by MIT Technology Review, during which He answered questions from biomedicine editor Antonio Regalado, editor in chief Mat Honan, and our subscribers.

During the interview, He defended his past research and said the “only regret” he had was the difficulties he had caused to his wife and two daughters. He spent three years in prison after a court found him guilty of breaking regulations, but since his release in 2022 he has sought to stage a scientific comeback.

He says he currently has a private lab in the city of Sanya, in Hainan province, where he works on gene therapy for rare disease as well as laboratory tests to determine how, one day, babies could be born resistant to ever developing Alzheimer’s disease.

The Chinese scientist said he’s receiving financial support from individuals in the US and China, and from Chinese companies, and has received an offer to form a research company in Silicon Valley. He declined to name his investors.

Read the full transcript of the event below.

Mat Honan: Hello, everybody. Thanks for joining us today. My name is Mat Honan. I’m the editor in chief here at MIT Technology Review. I’m really thrilled to host what’s going to be, I think, a great discussion today. I’m joined by Antonio Regalado, our senior editor for biomedicine, and He Jiankui, who goes by the name JK.

JK is a biophysicist, He’s based in China, and JK used CRISPR to edit the genes of human embryos, which ultimately resulted in the first children born whose DNA had been tailored using gene editing. Welcome to you both.

To our audience tuning in today, I wanted to let you know if you’ve got questions for us, please do ask them in the chat window. We’ve got a packed discussion planned, but we will get to as many of those as we can throughout. Antonio, I think I’m going to start with you, if we can. You’re the one who broke this story six years ago. Why don’t you set the stage for what we’re going to be talking about here today, and why it’s important.

Antonio Regalado: Mat, thank you.

The subject is genome editing. Of course, it’s a technology for changing the DNA inside of individual cells, including embryos. It’s hard to overstate its importance. I put it up there with the invention of the transistor and artificial intelligence.

And why do I think so? Well, genome editing gives humans control, or at least the ability to try and direct the very processes that brought us about as a species. So it’s that profound.

Getting to JK’s story. In 2018 we had a scoop—he might call it a leak—in which we described his experiment, which, as Mat said, was to edit human embryos to delete a particular gene called CCR5 with the goal of rendering the children, of which there were three, immune to HIV, which their fathers had and which is a source of stigma in China. So that was the project.

Of course our story set off, you know, immediate chaos. Voices were raised all over the world—many critical, a few in support. But one of the consequences was that JK and his team, the parents and the doctors, did not have the ability to tell their own story—in JK’s case because he was, in fact, detained and has completed a term in prison. So we’re happy to have him here to answer my questions and those of our subscribers. JK, thank you for being here.

Several people, including Professor Michael Waitzkin of Duke University, would like to know what the situation is with the three children. What do you know about their health, and where is this information coming from?

He Jiankui: Lulu, Nana, and the third gene-edited baby—they were healthy and are living a normal, peaceful, undisturbed life. They are as happy as any other people, any other children in kindergarten. I have maintained a constant connection with their parents.

Antonio Regalado: I see. JK, on X, you recently made a comment about one of the parents—now a single mother—who you said you were supporting financially. What can you tell us about that situation? What kind of obligations do you have to these children, and are you able to meet those obligations?

He Jiankui: So the third genetic baby—the parents divorced, so the girl is with her mother. You know, a single mother, a single-parent family—life is not easy. So in the last two years, I’m providing some financial support, but I’m not sure it’s the right thing to do or whether it’s ethical, because I’m a scientist or a doctor, and she is a volunteer or patient. For scientists or doctors to provide financial support to the volunteer or patient—it correct? Is it the right thing to do, and is it ethical? That’s something I’m not sure of. So I have this question, actually.

Antonio Regalado: Interesting. Well, there’s a lot of ethical dilemmas here, and one of them is about your publications, the scientific publications which you prepared and which describe the experiment. So a two-part question for you.

First of all, setting the ethics aside, some people who criticized your experiment still want to know the result. They would like to know if it worked. Are the children resistant to HIV or not? So part one of the question is: Are you able to make a measurement on their blood, or is anybody able to make a measurement that would show if the experiment worked? And second part of the question: Do you intend to publish your paper, including as a preprint or as a white paper?

He Jiankui: So I always believe that scientific research must be open and transparent, so I am willing to publish my papers, which I wrote six years ago.

It was rejected by Nature, for some reason. But even today, I would say that I’m willing to publish these two papers in a peer-reviewed journal. It has to be peer-reviewed; that is the standard way to publish in a paper.

The other thing is whether the baby is resistant to HIV. Actually, several years ago, when we designed the experiment, we already collected the [umbilical] cord blood when they were born. We collected cord blood from the babies, and our original experiment design was to challenge the cord blood with the HIV virus to see whether they are actually resistant to HIV. But this experiment never happened, because when the news broke out, there has been no way to do any experiment since then.

I would say I am happy to share my results to the whole world.

Mat Honan: Thanks, Antonio. Let me start with a question from a reader, Karen Jones. She asks, with so much controversy around breaking the law in China, she wanted to know about your credibility. And it reminds me of something that I’m curious about myself. What are the professional consequences of your work? Are you still able to work in China? Are you still able to do experiments with CRISPR?

He Jiankui: Yes, I continue my research in the lab. I have a lab in Sanya [Hainan province], and also previously a lab in Wuhan.

My current work is on gene editing to cure genetic disease such as Duchenne muscular dystrophy and several other genetic diseases. And all this is done by somatic gene therapy, which means this is not working on human embryos.

Mat Honan: I think that leads [to] a question that we have from another reader, Sophie, who wanted to know if you plan to do more gene editing in humans.

He Jiankui: So I have proposed a research project using human embryo gene editing to prevent Alzheimer’s disease. I posted this proposal last year on Twitter. So my goal is we’re going to test the embryo gene editing in mice and monkeys, and in human nonviable embryos. Again, it’s nonviable embryos. There will be no more gene-edited babies. There will be no more pregnancies. We’re going to stop at human nonviable embryos. So our goal is to see if we could prevent Alzheimer’s for offspring or the next generation, because Alzheimer’s has no cure currently.

Mat Honan: I see. And then my last question before I move it back to Antonio. I’m curious if you plan to continue working in China, or if you think that you will ultimately relocate somewhere else. Do you plan to do this work elsewhere?

He Jiankui: Some investors from Silicon Valley proposed to invest in me to start a company in the United States, with research done both in the United States and in China. This is a very interesting proposal, and I am considering it. I would be happy to work in the United States if there’s good opportunity.

Mat Honan: Let me just remind our readers—if you do have questions, you could put them in the chat and we will try to get to them. But in the meantime, Antonio, back over to you, please.

Antonio Regalado: Definitely, I’m curious about what your plans are. Yesterday Stat News reported some of the answers to today’s questions. They said that you have established yourself in the province of Hainan in China. So what kind of facility do you have there? Do you have a lab, or are you doing research? And where is the financial support coming from?

He Jiankui: So here I have an independent private research lab with a few people. We get funding from both the United States and also from China to support me to carry on the research on the gene therapy for Duchenne muscular dystrophy, for high cholesterol, and some other genetic diseases.

Antonio Regalado: Could you be more specific about where the funding is coming from? I mean, who is funding you, or what types of people are funding this research?

He Jiankui: There are people in the United States who made a donation to me. I’m not going to disclose the name and amount. Also the Chinese people, including some companies, are providing funding to me.

Antonio Regalado: I wonder if you could sketch out for us—I know people are interested—where you think all this [is] going to lead. With a long enough time frame—10 years, 20 years, 30 years—do you think the technology will be in use to change embryos, and how will it be used? What is the larger plan that you see?

He Jiankui: I would say in 50 years, like in 2074, embryo gene editing will be as common as IVF babies to prevent all the genetic disease we know today. So the babies born at that time will be free of genetic disease.

Antonio Regalado: You’re working on Alzheimer’s. This is a gene variant that was described in 2012 by deCode Genetics. This is one of these variants that is protective—it would protect against Alzheimer’s. Strictly speaking, it’s not a genetic disease. So what about the role of protective variants, or what could be called improvements to health?

He Jiankui: Well, I decided to do Alzheimer’s disease because my mother has Alzheimer’s. So I’m going to have Alzheimer’s too, and maybe my daughter and my granddaughter. So I want to do something to change it.

There’s no cure for Alzheimer’s today. I don’t know for how many years that will be true. But what we can do is: Since some people in Europe are at a very low risk [for] Alzheimer’s, why don’t we just make some modifications so our next generation also have this protective allele, so they have a low risk of Alzheimer’s or maybe are free of Alzheimer’s. That’s my goal.

Antonio Regalado: Well, a couple of questions. Will any country permit this? I mean, genome editing, producing genome-edited children, was made formally illegal in China, I think in 2021. And it’s prohibited in the United States in another way. So where can you go, or where will you go to further this technology?

He Jiankui: I believe society will eventually accept that embryo gene editing is a good thing because it improves human health. So I’m waiting for society to accept that. My current research is not doing any gene-edited baby or any pregnancy. What I do is a basic research in mice, monkeys, or human nonviable embryos. We only do basic research, but I’m certain that one day society will accept embryo gene editing.

Mat Honan: That raises a question for me. We’re talking about HIV or Alzheimer’s, but there are other aspects of this as well. You could be doing something where you’re optimizing for intelligence or optimizing for physical performance. And I’m curious where you think this leads, and if you think that there is a moral issue around, say, parents who are allowed to effectively design their children by editing their genes.

He Jiankui: Well, I advise you to read the paper I published in 2018 in the CRISPR Journal. It’s my personal thinking of the ethical guidelines for embryo gene editing. It was retracted by the CRISPR Journal. But I proposed that the embryo gene editing should only be used for disease. It should never be used for a nontherapeutic purpose, like making people smarter, stronger, or beautiful.

Mat Honan: Do you not think that becomes inevitable, though, if gene-editing embryos becomes common?

He Jiankui: Society will decide that.

Mat Honan: Moving on: You said that you were only working with animals or with nonviable embryos. Are there other people who you think are working with human embryos, with viable human embryos, or that you know of, or have heard about, continuing with that kind of work?

He Jiankui: Well, I don’t know yet. Actually, many scientists are keeping their distance from me. But there are people from somewhere, an island in Honduras or maybe some small East European country, inviting me to do that. And I refused. I refused. I will only do research in the United States and China or other major countries.

Mat Honan: So the short answer is, that sounded almost like a yes to me? You think that it is happening? Is that correct?

He Jiankui: I’m not answering that.

Mat Honan: Okay, fair enough. I’m going to move on to some reader questions here while we have the time. You mentioned basically having society come around to seeing that this is necessary work. Ravi asks: What type of regulatory framework do you believe is necessary to ensure responsible development and applications of this technology? You had mentioned limiting to therapeutic purposes. Are there other frameworks you think should be in place?

He Jiankui: I’m not answering this question.

Mat Honan: What you think should be in place in terms of regulation?

He Jiankui: Well, there are a lot of regulations. I personally comply with all the laws, regulations, and international ethics for my work.

Mat Honan: I see. Go ahead, Antonio.

Antonio Regalado: Let me just jump in with a related question. You talked about offers of funding from the United States, from Silicon Valley—offers of funding to support you. Is that to create a company, and how would accepting investment from entrepreneurs to start a company change public perception about the technology?

He Jiankui: Well, it was designed as a company registered in the United States and headquartered in the United States.

Antonio Regalado: But do you think that starting a company will make people more enthusiastic or interested in this technology?

He Jiankui: Well, for me, I would certainly be more happy to get an offer from the United States [if it came] from a university or research institution. I would be happy for that, but it’s not happening. But, well, a company started doing some basic research, and that’s also a good contribution.

Antonio Regalado: Getting back to the initial experiment—obviously, it’s been criticized a great deal. And I am just wondering, looking back, which of those criticisms do you accept? Which do you disagree with? Do you have regrets about the experiment?

He Jiankui: The only regret I have is to my family, my wife and my two daughters. In the last few years, they are living in a very difficult situation. I won’t let that happen again.

Antonio Regalado: The technology is viewed as controversial. I’m talking about embryo editing. So it’s a little bit surprising to me that you would return to it. Surprising and interesting. So why is it that you have decided to pursue this vision, this project, despite the problems? I mean, you’re still working on it. What is your motivation?

He Jiankui: Our stance is always for us to do something to benefit mankind.

Antonio Regalado: Speaking of mankind, or humankind, I did have a question about evolution. The gene edits that you made to CCR5 and now are working on to another gene in Alzheimer’s—these are natural mutations that occur in some populations, you mentioned in Europe. They’ve been discovered through population genetics. Studies of a large number of people can find these genetic variations that are protective, or believed to be protective, against disease. In the natural course of evolution, those might spread, right? But it would take hundreds of thousands of years. So with gene editing, you can introduce such a change into an embryo, I guess, in a matter of minutes.

So the question I have is: Is this an evolutionary project? Is it human technology being used to take over from evolution?

He Jiankui: I’m not interested in evolution. Evolution takes thousands of years. I only care about the people surrounding me—my family, and also the patients who would come to find me. What I want to do is help those people, help people in this living world. I’m not interested in evolution.

Antonio Regalado: Mat, any other question from the audience you’d like to throw in?

Mat Honan: Yeah, let me get to one from Rez, who’s asking: What do you see as the major hurdles in advancing CRISPR to more general health-care use cases? What do you see as the big barriers there?

He Jiankui: If you’re talking about somatic gene therapy, the bottleneck, of course, is delivery. Without breakthroughs in delivery technology, somatic gene therapy is heading toward a dead end. For the embryo gene editing, the bottleneck, of course, is: How long will it take people to accept new technology? Because as humans, we are always conservative. We are always worried about the new things, and it takes time for people to accept new technology.

Mat Honan: I wanted to get a question from Robert that goes back to our earlier discussion here, which is: What was your initial motivation to take this step with the three children?

He Jiankui: So several years ago, I went to a village in the center of China where more than 30% of people are infected with HIV. Back to the 1990s, many years ago, people sold blood, and it did something [spread HIV]. When I was there, I saw that there’s a very small kindergarten, only designed for the children of HIV patients. Why did that happen? Other public schools won’t take them. I felt that there’s a kind of discrimination to these children. And what I want to do is to do something to change it. If the HIV patient—if their children are not just free from but actually immune to HIV, then it will help them to go back to the society. For me, it’s just like a vaccine. It’s one vaccine to protect them for a lifetime.

Mat Honan: I see we’re running short on time here, and I do want to try to get to some more of our reader questions. I know Antonio has a last one as well. If you do have questions, please put them in the chat. And from Joseph, he wants to know: You say that you think that the society will come around. What do you think will be the first types of embryo DNA edits that would be acceptable to the medical community or to society at large?

He Jiankui: Very recently, a patient flew here to visit me in my office. They are a couple, they are over 40 years old. They want to have a baby and already did IVF. They have embryos, but the embryos have a problem with a chromosome. So this embryo is not good. So one thing, apparently, we could do to help them is to correct the chromosome problem so they can have a healthy embryo, so they can have children. We’re not creating any immunity to anything—it’s just to restore the health of the embryo. And I believe that would be a good start.

Mat Honan: Thank you, JK. Antonio, back over to you.

Antonio Regalado: JK, I’m curious about your relationship to the government in China, the central government. You were punished, but on the other hand, you’re free to continue to talk about science and do research. Does the government support you and your ideas? Are you a member of the political party? Have you been offered membership? What is your relationship to the government?

He Jiankui: Next question.

Antonio Regalado: Next question? Okay. Interesting. We’ll have to postpone that one for another day.

Mat, anything else? I think we’re coming up against time, and I’m wondering if we have reader questions. I have one here that I could ask, which is about the new technologies in CRISPR. People want to know where this technology is going, in terms of the methods. You used CRISPR to delete a gene. But CRISPR itself is constantly being improved. There are new tools. So in your lab, in your experiments, what gene-editing technology are you employing?

He Jiankui: So six years ago, we were using the original CRISPR-Cas9 invented by Jennifer Doudna. But today, we are moving on to base editing, invented by David Liu. The base editing, it’s safe in embryos. It won’t cut the DNA or break it—just small changes. So we no longer use CRISPR-Cas9. We’re using base editing.

Antonio Regalado: And can you tell me the nature of the genetic change that you’re experimenting with or would like to make in these cells to make them resistant to Alzheimer’s? How big a change are you making with this base editor, or trying to make with it?

He Jiankui: So to make people protected against Alzheimer’s, we just need a single base change in the whole human 3 billion letters of DNA. We just change one letter of it to protect people from Alzheimer’s.

Antonio Regalado: And how soon do you think that this could be in use? I mean, it sounds interesting. If I had a child, I might want them to be immune to Alzheimer’s. So this is quite an interesting proposal. What is the time frame in years—if it works in the lab—before it could be implemented in IVF clinics?

He Jiankui: I would say there’s the basic research that could be finished in two years. I won’t move on to the human trial. That’s not my role. It’s determined by society whether to accept it or not. And that’s the ethical side.

Antonio Regalado: A last question on this from a reader. The question is: How do you prove the benefits? Of course, you can make a genetic change. You can even create a person with a genetic change. But if it’s for Alzheimer’s, it’s going to take 70 years before you know and can prove the results. So how can you prove its medical benefit? Or how can you predict the medical benefit?

He Jiankui: So one thing is that we can observe it in the natural world. There are already thousands of people with this mutation. It helps them against Alzheimer’s. It naturally exists in the population, in humans, so that’s a natural human experiment. And also we could do it in mice. We could use Alzheimer’s model mice and then to modulate DNA to see the results.

You might argue that it takes many years to develop Alzheimer’s, but in society, we’ve done a lot with the HPV vaccine against certain women’s cancers. Cancer takes many years to happen, but they take the HPV vaccine at age eight or seven.

Mat Honan: Thank you so much. JK and Antonio, we are slightly past time here, and I’m going to go ahead and wrap it up. Thank you very much for joining us today, to both of you. And I also want to thank all of our subscribers who tuned in today. I do hope that we see you again next month at our Roundtable in August. It’s our subscriber-only series. And I hope you enjoyed today. Thanks, everybody.

Antonio Regalado: Thank you, JK.

He Jiankui: Thank you.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Google DeepMind’s new AI systems can now solve complex math problems

AI models can easily generate essays and other types of text. However, they’re nowhere near as good at solving math problems, which tend to involve logical reasoning—something that’s beyond the capabilities of most current AI systems.

But that may finally be changing. Google DeepMind says it has trained two specialized AI systems to solve complex math problems involving advanced reasoning. The systems worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad, a prestigious competition for high school students.

They won the equivalent of a silver medal, marking the first time any AI system has ever achieved such a high success rate on these kinds of problems. Read the full story.

—Rhiannon Williams

Why the US is still trying to make mirror-magnified solar energy work

The US is continuing its decades-long effort to commercialize a technology that converts sunlight into heat, funding a series of new projects using that energy to brew beer, produce low-carbon fuels, or keep grids running.

The Department of Energy has announced it is putting $33 million into nine pilot projects based on concentrating solar thermal power, MIT Technology Review can report exclusively. The technology uses large arrays of mirrors to concentrate sunlight onto a receiver, where it’s used to heat up molten salt, ceramic particles, or other materials that can store that energy for extended periods.

But early commercial efforts to produce clean electricity based on this technology have been bedeviled by high costs, low output, and other challenges. Read the full story.

—James Temple

“Copyright traps” could tell writers if an AI has scraped their work

Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set.

Now they have a new way to prove it: “copyright traps” developed by a team at Imperial College London, pieces of hidden text that allow writers and publishers to subtly mark their work in order to later detect whether it has been used in AI models or not. Read the full story.

—Melissa Heikkilä

How our genome is like a generative AI model

What does the genome do? You might have heard that it is a blueprint for an organism. Or that it’s a bit like a recipe. But building an organism is much more complex than constructing a house or baking a cake.

This week I came across an idea for a new way to think about the genome—one that borrows from the field of artificial intelligence. Two researchers are arguing that we should think about it as being more like a generative model, a form of AI that can generate new things.

You might be familiar with such AI tools—they’re the ones that can create text, images, or even films from various prompts. But do our genomes really work in the same way? Read the full story.

—Jessica Hamzelou

This story is from The Checkup, our weekly health and biotech newsletter. Sign up to receive it in your inbox every Thursday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI’s search engine is hereAnd it’s already getting stuff wrong. (The Atlantic $)
+ SearchGPT will eventually be folded into ChatGPT. (WP $)
+ Its launch is a clear threat to Google’s long-held search engine dominance. (Wired $)
+ Why you shouldn’t trust AI search engines. (MIT Technology Review)

2 The chip industry’s workers are demanding better treatment
As the sector’s profits soar, its employees aren’t seeing the benefits. (WSJ $)

3 What studying the human brain can teach us about AI
Trying to understand why AI does the things it does is key to controlling it. (Vox)
+ What is AI? (MIT Technology Review)
4 Russia is throttling access to YouTubeIt’s looking as though a total ban is imminent. (Bloomberg $) 5 Robots are finally becoming more useful
And it’s all thanks to AI. (FT $)
+ Is robotics about to have its own ChatGPT moment? (MIT Technology Review)

6 Voice actors are striking against video game companiesThey claim the firms have learnt nothing from the prior strikes against film and TV. (NYT $)
+ They want studios to seek actors’ consent for using their voices with AI. (Bloomberg $)

7 Identifying all of Mexico’s dead bodies is a forensic crisisScientists are doing their best to harness tech to their cause. (New Yorker $)
+ The mothers of Mexico’s missing are using social media to search for mass graves. (MIT Technology Review)

8 New Jersey is angling to become a major AI hubBruce Springsteen’s hometown wants a slice of those hefty new tax credits. (Wired $)
+ The $100 billion bet that a postindustrial US city can reinvent itself as a high-tech hub. (MIT Technology Review)

9 Mexico’s delivery workers are sick of food orders
It’s less waiting around, and fewer irate customers. (Rest of World)

10 How to find serenity in a plant-identifying appTake a minute to step outside and smell the roses. (The Guardian)

Quote of the day

“Just hug your IT folks.”

—Jerry Leever, an IT director at accounting, tax and advisory firm GHJ, explains to the Washington Post what it was like attempting to handle last week’s CrowdStrike meltdown.

The big story

Bright LEDs could spell the end of dark skies

August 2022

Scientists have known for years that light pollution is growing and can harm both humans and wildlife. In people, increased exposure to light at night disrupts sleep cycles and has been linked to cancer and cardiovascular disease, while wildlife suffers from interruption to their reproductive patterns, and increased danger.

Astronomers, policymakers, and lighting professionals are all working to find ways to reduce light pollution. Many of them advocate installing light-emitting diodes, or LEDs, in outdoor fixtures such as city streetlights, mainly for their ability to direct light to a targeted area.

But the high initial investment and durability of modern LEDs mean cities need to get the transition right the first time or potentially face decades of consequences. Read the full story.

—Shel Evergreen

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Lady Gaga! Celine Dion! Snoop Dogg! It’s safe to say tonight’s Paris Olympics opening ceremony is going to be suitably bonkers.
• Although nothing is ever going to top London 2012’s opening.
• Candace Bushnell, you will never not be fabulous.
• Who doesn’t love a good Kubrick stare?

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This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

What does the genome do? You might have heard that it is a blueprint for an organism. Or that it’s a bit like a recipe. But building an organism is much more complex than constructing a house or baking a cake.

This week I came across an idea for a new way to think about the genome—one that borrows from the field of artificial intelligence. Two researchers are arguing that we should think about it as being more like a generative model, a form of AI that can generate new things.

You might be familiar with such AI tools—they’re the ones that can create text, images, or even films from various prompts. Do our genomes really work in the same way? It’s a fascinating idea. Let’s explore.

When I was at school, I was taught that the genome is essentially a code for an organism. It contains the instructions needed to make the various proteins we need to build our cells and tissues and keep them working. It made sense to me to think of the human genome as being something like a program for a human being.

But this metaphor falls apart once you start to poke at it, says Kevin Mitchell, a neurogeneticist at Trinity College in Dublin, Ireland, who has spent a lot of time thinking about how the genome works.

A computer program is essentially a sequence of steps, each controlling a specific part of development. In human terms, this would be like having a set of instructions to start by building a brain, then a head, and then a neck, and so on. That’s just not how things work.

Another popular metaphor likens the genome to a blueprint for the body. But a blueprint is essentially a plan for what a structure should look like when it is fully built, with each part of the diagram representing a bit of the final product. Our genomes don’t work this way either.

It’s not as if you’ve got a gene for an elbow and a gene for an eyebrow. Multiple genes are involved in the development of multiple body parts. The functions of genes can overlap, and the same genes can work differently depending on when and where they are active. It’s far more complicated than a blueprint.

Then there’s the recipe metaphor. In some ways, this is more accurate than the analogy of a blueprint or program. It might be helpful to think about our genes as a set of ingredients and instructions, and to bear in mind that the final product is also at the mercy of variations in the temperature of the oven or the type of baking dish used, for example. Identical twins are born with the same DNA, after all, but they are often quite different by the time they’re adults.

But the recipe metaphor is too vague, says Mitchell. Instead, he and his colleague Nick Cheney at the University of Vermont are borrowing concepts from AI to capture what the genome does. Mitchell points to generative AI models like Midjourney and DALL-E, both of which can generate images from text prompts. These models work by capturing elements of existing images to create new ones.

Say you write a prompt for an image of a horse. The models have been trained on a huge number of images of horses, and these images are essentially compressed to allow the models to capture certain elements of what you might call “horsiness.” The AI can then construct a new image that contains these elements.

We can think about genetic data in a similar way. According to this model, we might consider evolution to be the training data. The genome is the compressed data—the set of information that can be used to create the new organism. It contains the elements we need, but there’s plenty of scope for variation. (There are lots more details about the various aspects of the model in the paper, which has not yet been peer-reviewed.)

Mitchell thinks it’s important to get our metaphors in order when we think about the genome. New technologies are allowing scientists to probe ever deeper into our genes and the roles they play. They can now study how all the genes are expressed in a single cell, for example, and how this varies across every cell in an embryo.

“We need to have a conceptual framework that will allow us to make sense of that,” says Mitchell. He hopes that the concept will aid the development of mathematical models that might help us better understand the intricate relationships between genes and the organisms they end up being part of—in other words, exactly how components of our genome contribute to our development.

Now read the rest of The CheckupRead more from MIT Technology Review’s archive:Last year, researchers built a new human genome reference designed to capture the diversity among us. They called it the “pangenome,” as Antonio Regalado reported.

Generative AI has taken the world by storm. Will Douglas Heaven explored six big questions that will determine the future of the technology.

A Disney director tried to use AI to generate a soundtrack in the style of Hans Zimmer. It wasn’t as good as the real thing, as Melissa Heikkilä found.

Melissa has also reported on how much energy it takes to create an image using generative AI. Turns out it’s about the same as charging your phone.

What is AI? No one can agree, as Will found in his recent deep dive on the topic.

From around the webEvidence from more than 1,400 rape cases in Maryland, some from as far back as 1977, are set to be processed by the end of the year, thanks to a new law. The state still has more than 6,000 untested rape kits. (ProPublica)

How well is your brain aging? A new tool has been designed to capture a person’s brain age based on an MRI scan, and which accounts for the possible effects of traumatic brain injuries. (NeuroImage)

Iran has reported the country’s first locally acquired cases of dengue, a viral infection spread by mosquitoes. There are concerns it could spread. (WHO)

IVF is expensive, and add-ons like endometrial scratching (which literally involves scratching the lining of the uterus) are not supported by strong evidence. Is the fertility industry profiting from vulnerability? (The Lancet)

Up to 2 million Americans are getting their supply of weight loss drugs like Wegovy or Zepbound from compounding pharmacies. They’re a fraction of the price of brand-name Big Pharma drugs, but there are some safety concerns. (KFF Health News)

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Recorded on July 25, 2024

CRISPR Babies: Six years later

Speakers: He Jiankui, CRISPR Pioneer, Antonio Regalado, senior editor for biomedicine, and Mat Honan, editor in chief

Gene editing can correct or improve the DNA of human embryos, essentially opening the door to “technological evolution” of our species. But in 2018, a premature attempt to use gene editing led to a prison term for He Jiankui, the researcher involved. Editor in chief Mat Honan and senior editor for biomedicine Antonio Regalado have a conversation with He Jiankui, biophysicist and creator of the first gene-edited humans, to revisit this controversial technology and the future of editing in IVF clinics.

Related Coverage

• EXCLUSIVE: Chinese scientists are creating CRISPR babies
• China’s CRISPR babies: Read exclusive excerpts from the unseen original research
• The creator of the CRISPR babies has been released from a Chinese prison

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Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set.

Now they have a new way to prove it: “copyright traps” developed by a team at Imperial College London, pieces of hidden text that allow writers and publishers to subtly mark their work in order to later detect whether it has been used in AI models or not. The idea is similar to traps that have been used by copyright holders throughout history—strategies like including fake locations on a map or fake words in a dictionary.

These AI copyright traps tap into one of the biggest fights in AI. A number of publishers and writers are in the middle of litigation against tech companies, claiming their intellectual property has been scraped into AI training data sets without their permission. The New York Times’ ongoing case against OpenAI is probably the most high-profile of these.

The code to generate and detect traps is currently available on GitHub, but the team also intends to build a tool that allows people to generate and insert copyright traps themselves.

“There is a complete lack of transparency in terms of which content is used to train models, and we think this is preventing finding the right balance [between AI companies and content creators],” says Yves-Alexandre de Montjoye, an associate professor of applied mathematics and computer science at Imperial College London, who led the research. It was presented at the International Conference on Machine Learning, a top AI conference being held in Vienna this week.

To create the traps, the team used a word generator to create thousands of synthetic sentences. These sentences are long and full of gibberish, and could look something like this: ”When in comes times of turmoil … whats on sale and more important when, is best, this list tells your who is opening on Thrs. at night with their regular sale times and other opening time from your neighbors. You still.”

The team generated 100 trap sentences and then randomly chose one to inject into a text many times, de Montjoy explains. The trap could be injected into text in multiple ways—for example, as white text on a white background, or embedded in the article’s source code. This sentence had to be repeated in the text 100 to 1,000 times.

To detect the traps, they fed a large language model the 100 synthetic sentences they had generated, and looked at whether it flagged them as new or not. If the model had seen a trap sentence in its training data, it would indicate a lower “surprise” (also known as “perplexity”) score. But if the model was “surprised” about sentences, it meant that it was encountering them for the first time, and therefore they weren’t traps.

In the past, researchers have suggested exploiting the fact that language models memorize their training data to determine whether something has appeared in that data. The technique, called a “membership inference attack,” works effectively in large state-of-the art models, which tend to memorize a lot of their data during training.

In contrast, smaller models, which are gaining popularity and can be run on mobile devices, memorize less and are thus less susceptible to membership inference attacks, which makes it harder to determine whether or not they were trained on a particular copyrighted document, says Gautam Kamath, an assistant computer science professor at the University of Waterloo, who was not part of the research.

Copyright traps are a way to do membership inference attacks even on smaller models. The team injected their traps into the training data set of CroissantLLM, a new bilingual French-English language model that was trained from scratch by a team of industry and academic researchers that the Imperial College London team partnered with. CroissantLLM has 1.3 billion parameters, a fraction as many as state-of-the-art models (GPT-4 reportedly has 1.76 trillion, for example).

The research shows it is indeed possible to introduce such traps into text data so as to significantly increase the efficacy of membership inference attacks, even for smaller models, says Kamath. But there’s still a lot to be done, he adds.

Repeating a 75-word phrase 1,000 times in a document is a big change to the original text, which could allow people training AI models to detect the trap and skip content containing it, or just delete it and train on the rest of the text, Kamath says. It also makes the original text hard to read.

This makes copyright traps impractical right now, says Sameer Singh, a professor of computer science at the University of California, Irvine, and a cofounder of the startup Spiffy AI. He was not part of the research. “A lot of companies do deduplication, [meaning] they clean up the data, and a bunch of this kind of stuff will probably get thrown out,” Singh says.

One way to improve copyright traps, says Kamath, would be to find other ways to mark copyrighted content so that membership inference attacks work better on them, or to improve membership inference attacks themselves.

De Montjoye acknowledges that the traps are not foolproof. A motivated attacker who knows about a trap can remove them, he says.

“Whether they can remove all of them or not is an open question, and that’s likely to be a bit of a cat-and-mouse game,” he says. But even then, the more traps are applied, the harder it becomes to remove all of them without significant engineering resources.

“It’s important to keep in mind that copyright traps may only be a stopgap solution, or merely an inconvenience to model trainers,” says Kamath. “One can not release a piece of content containing a trap and have any assurance that it will be an effective trap forever.”

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AI models can easily generate essays and other types of text. However, they’re nowhere near as good at solving math problems, which tend to involve logical reasoning—something that’s beyond the capabilities of most current AI systems.

But that may finally be changing. Google DeepMind says it has trained two specialized AI systems to solve complex math problems involving advanced reasoning. The systems—called AlphaProof and AlphaGeometry 2—worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad (IMO), a prestigious competition for high school students. They won the equivalent of a silver medal at the event.

It’s the first time any AI system has ever achieved such a high success rate on these kinds of problems. “This is great progress in the field of machine learning and AI,” says Pushmeet Kohli, vice president of research at Google DeepMind, who worked on the project. “No such system has been developed until now which could solve problems at this success rate with this level of generality.”

There are a few reasons math problems that involve advanced reasoning are difficult for AI systems to solve. These types of problems often require forming and drawing on abstractions. They also involve complex hierarchical planning, as well as setting subgoals, backtracking, and trying new paths. All these are challenging for AI.

“It is often easier to train a model for mathematics if you have a way to check its answers (e.g., in a formal language), but there is comparatively less formal mathematics data online compared to free-form natural language (informal language),” says Katie Collins, an researcher at the University of Cambridge who specializes in math and AI but was not involved in the project.

Bridging this gap was Google DeepMind’s goal in creating AlphaProof, a reinforcement-learning-based system that trains itself to prove mathematical statements in the formal programming language Lean. The key is a version of DeepMind’s Gemini AI that’s fine-tuned to automatically translate math problems phrased in natural, informal language into formal statements, which are easier for the AI to process. This created a large library of formal math problems with varying degrees of difficulty.

Automating the process of translating data into formal language is a big step forward for the math community, says Wenda Li, a lecturer in hybrid AI at the University of Edinburgh, who peer-reviewed the research but was not involved in the project.

“We can have much greater confidence in the correctness of published results if they are able to formulate this proving system, and it can also become more collaborative,” he adds.

The Gemini model works alongside AlphaZero—the reinforcement-learning model that Google DeepMind trained to master games such as Go and chess—to prove or disprove millions of mathematical problems. The more problems it has successfully solved, the better AlphaProof has become at tackling problems of increasing complexity.

Although AlphaProof was trained to tackle problems across a wide range of mathematical topics, AlphaGeometry 2—an improved version of a system that Google DeepMind announced in January—was optimized to tackle problems relating to movements of objects and equations involving angles, ratios, and distances. Because it was trained on significantly more synthetic data than its predecessor, it was able to take on much more challenging geometry questions.

To test the systems’ capabilities, Google DeepMind researchers tasked them with solving the six problems given to humans competing in this year’s IMO and proving that the answers were correct. AlphaProof solved two algebra problems and one number theory problem, one of which was the competition’s hardest. AlphaGeometry 2 successfully solved a geometry question, but two questions on combinatorics (an area of math focused on counting and arranging objects) were left unsolved.

“Generally, AlphaProof performs much better on algebra and number theory than combinatorics,” says Alex Davies, a research engineer on the AlphaProof team. “We are still working to understand why this is, which will hopefully lead us to improve the system.”

Two renowned mathematicians, Tim Gowers and Joseph Myers, checked the systems’ submissions. They awarded each of their four correct answers full marks (seven out of seven), giving the systems a total of 28 points out of a maximum of 42. A human participant earning this score would be awarded a silver medal and just miss out on gold, the threshold for which starts at 29 points.

This is the first time any AI system has been able to achieve a medal-level performance on IMO questions. “As a mathematician, I find it very impressive, and a significant jump from what was previously possible,” Gowers said during a press conference.

Myers agreed that the systems’ math answers represent a substantial advance over what AI could previously achieve. “It will be interesting to see how things scale and whether they can be made faster, and whether it can extend to other sorts of mathematics,” he said.

Creating AI systems that can solve more challenging mathematics problems could pave the way for exciting human-AI collaborations, helping mathematicians to both solve and invent new kinds of problems, says Collins. This in turn could help us learn more about how we humans tackle math.

“There is still much we don’t know about how humans solve complex mathematics problems,” she says.

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The US is continuing its decades-long effort to move a technology that converts sunlight into heat toward the marketplace by funding a series of new projects using that energy to brew beer, produce low-carbon fuels, or keep grids running.

On July 25, the Department of Energy will announce it is putting $33 million into nine pilot or demonstration projects based on concentrating solar thermal power, MIT Technology Review can report exclusively. The technology uses large arrays of mirrors to concentrate sunlight onto a receiver, where it’s used to heat up molten salt, ceramic particles, or other materials that can store that energy for extended periods.

“Under the Biden-Harris administration, DOE continues to invest in the next-generation solar technologies we need to tackle the climate crisis and ensure American scientific innovation remains the envy of the world,” Energy Secretary Jennifer Granholm said in a statement.

The DOE has been funding efforts to get concentrated solar energy off the ground since at least the 1970s. The idea was initially driven in part by the quest to develop more renewable, domestic sources of energy during the oil crisis of that era.

But early commercial efforts to produce clean electricity based on this technology have been bedeviled by high costs, low output, and other challenges.

Researchers continued to try to drive the field forward, in part by moving to higher-temperature systems that are more efficient and switching to new types of materials that can withstand them. The focus of the concentrating solar field has also shifted away from using the technology to produce electricity—a job that its solar photovoltaic cousin now does incredibly effectively, cheaply, and on a massive scale—and toward using it to provide the heat needed for various industrial processes or as a form of very long-duration energy storage for grids.

Indeed, a core promise of the technology is that heat can be stored more efficiently than electricity, potentially offering an alternative to very expensive large-scale battery plants. This could be especially useful for dealing with prolonged dips in renewable generation as solar, wind, and other fluctuating sources come to produce a larger and larger share of electricity.

Among the awardees:

• More than $7 million of the DOE funds will support a project at Firestone Walker Brewery in Paso Robles, California, which will tap into solar thermal energy to produce the steam needed for its lineup of IPAs and other beers.
• Another $6 million will go to Premier Resource Management’s planned concentrating solar power plant in Bakersfield, California, which would store thermal energy in retired fracking sites.
• Researchers at West Virginia University, who are working with NASA, secured $5 million to explore the use of solar thermal to produce a clean form of hydrogen, a fuel as well as a feedstock in the production of fertilizer, steel, and other industrial goods.

The DOE funds pilot and demonstration projects in the hopes of kick-starting commercialization of emerging energy technologies, helping research groups or companies to refine them, scale them up, and drive down costs.

In the case of concentrating solar thermal, costs still need to fall by about half to “really unlock broader applications,” says Becca Jones-Albertus, director of DOE’s Solar Energy Technologies Office.

But she says the department continues to invest in the development of the technology because it remains one of the most promising ways to address three big categories where the world still needs better solutions to cut climate warming emissions: long-duration grid storage, industrial heat, and steady forms of carbon-free electricity.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

PsiQuantum plans to build the biggest quantum computing facility in the US

Quantum computing firm PsiQuantum is partnering with universities and a national lab to build the largest US-based quantum computing facility in Chicago, the company has announced. The firm says it will house a quantum computer containing up to one million quantum bits, or qubits, within the next 10 years. At the moment, the largest quantum computers have around 1000 qubits.

But significant hurdles lie ahead. Building the infrastructure for this facility, particularly for the cooling system, will be the slowest and most expensive aspect of the construction. And when the facility is finally constructed, there will need to be improvements in the quantum algorithms run on the computers, as existing ones are too expensive and resource-intensive. Read the full story.

—Sarah Ward

AI trained on AI garbage spits out AI garbage

What’s new: AI models work by training on huge swaths of data from the internet. But as AI is increasingly being used to pump out web pages filled with junk content, that process is in danger of being undermined. New research shows that the quality of the model’s output gradually degrades when AI trains on AI-generated data. As subsequent models produce output that is then used as training data for future models, the effect gets worse.

Why it matters: This research may have serious implications for the largest AI models of today, because they use the internet as their database. And the problem is likely to get worse as an increasing number of AI-generated junk websites start cluttering up the internet. Read the full story.

—Scott J Mulligan

The race to clean up heavy-duty trucks

Truckers have to transport massive loads long distances, every single day, under intense time pressure—and they rely on the semi-trucks they drive to get the job done. Their diesel engines spew not only greenhouse gas emissions that cause climate change, but also nitrogen oxide, which can be extremely harmful for human health.

Cleaning up trucking presents a massive challenge. That’s why some companies are trying to ease the industry into change. Startup Range Energy is adding batteries to the trailers of semi-trucks. If the electrified trailers are attached to diesel trucks, they can improve the fuel economy. If they’re added to zero-emissions vehicles powered by batteries or hydrogen, they could boost range and efficiency.

Here’s what our climate reporter Casey Crownhart has learned about what’s holding back progress in trucking and how experts are thinking about a few different technologies that could help.

This story is from The Spark, our weekly newsletter giving you the inside track on all things climate and energy. Sign up to receive it in your inbox every Wednesday.

Introducing: MIT Technology Review Narrated

Every week at MIT Technology Review, we produce deeply reported analysis, breaking news, and beautifully crafted long-form stories that you can read on our website and in our app.

But we know you don’t always have time to sit down and read everything you want to each day—life just gets in the way. That’s why we’re launching MIT Technology Review Narrated: a place for you to download some of our best stories as podcast episodes you can listen to anytime, whatever you’re doing.

In partnership with News Over Audio, we’ll be making a selection of our stories available, each one read by a professional voice actor. You’ll be able to listen to them on the go or download them to listen to offline.

We’ll be publishing a new story each week on Spotify and Apple Podcasts, including some taken from our most recent print magazine.

Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

CRISPR Babies: Six years later

Today at 12.30pm ET, subscribers can join our editor in chief Mat Honan and senior editor for biomedicine Antonio Regalado for “CRISPR Babies: Six years later”: a live virtual interview with He Jiankui, the Chinese biophysicist whose team created the first gene-edited humans.

Although he served a prison term, he has not given up on his idea that changing genes in an embryo could create people resistant to common diseases, such as Alzheimer’s. Register to attend here. And, if you’re not a subscriber already but want to join us, sign up for a subscription today.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The CrowdStrike outage is a foreshadowing of what’s to come
It could have been so much worse. Next time it might be. (Vox)+ The company has sent IT workers $10 gift cards to say sorry. (Bloomberg $)
+ How to fix a Windows PC affected by the global outage. (MIT Technology Review)

2 Threads’ staff are wondering whether to allow political content
Post-Joe Biden stepping down, it’s reconsidering its restrictions on the recommendation of political posts. (The Information $)

3 China is stockpiling materials at a rapid pace
Grain, gas and oil is being collected to ward off future potential sanctions. (Economist $)

4 Pollution is spiking around e-retailers’ warehouses
And it’s nearby residents who are bearing the brunt of it. (The Verge)

5 Google appears to be exclusively surfacing Reddit results
Its links are not showing up on Bing, DuckDuckGo and others. (404 Media)
+ This new search engine roots out privacy violations. (Wired $)

6 Space is full of trashAnd it’s making space travel incredibly difficult.(Fast Company $)
+ SpaceX has a stranglehold on commercial space contracts. (Ars Technica)
+ What’s going on with Boeing’s Starliner mission? (The Atlantic $)
+ Why the first-ever space junk fine is such a big deal. (MIT Technology Review)

7 Confessions of a ransomware negotiatorNick Shah is putting his background as a hostage negotiator to good use.(Economist $)

8 Inside Singapore’s lab-grown meat experimentThe city state is backing cultivated meat at a time when others are retreating. (NYT $)
+ How I learned to stop worrying and love fake meat. (MIT Technology Review)

9 Influencer baby names are out of control
Please don’t call your future child Giraffe. Just saying. (Dazed)

10 This year’s Olympians are TikTok stars too
2024’s crop of influencer-athletes are reshaping the game. (NY Mag $)

Quote of the day

“We worried we’ve hired 10,000 people and we’ve built a smart timer.”

—A former senior Amazon employee reflects on the legacy of the company’s Echo smart home device to the Wall Street Journal.

The big story

California’s coming offshore wind boom faces big engineering hurdles

December 2022

The state of California has an ambitious goal: building 25 gigawatts of offshore wind by 2045. That’s equivalent to nearly a third of the state’s total generating capacity today, or enough to power 25 million homes.

But the plans are facing a daunting geological challenge: the continental shelf drops steeply just a few miles off the California coast. They also face enormous engineering and regulatory obstacles. Read the full story.

—James Temple

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• Love Wordle? Give these other word-based puzzles a whirl.
• How Garfield paved the way for The Sopranos—no, really!
• Shanghai’s burgeoning punk scene is super cool.
• This website contains an infinite number of editable boxes, because why not?

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The quantum computing firm PsiQuantum is partnering with the state of Illinois to build the largest US-based quantum computing facility, the company announced today.

The firm, which has headquarters in California, says it aims to house a quantum computer containing up to 1 million quantum bits, or qubits, within the next 10 years. At the moment, the largest quantum computers have around 1,000 qubits.

Quantum computers promise to do a wide range of tasks, from drug discovery to cryptography, at record-breaking speeds. Companies are using different approaches to build the systems and working hard to scale them up. Both Google and IBM, for example, make the qubits out of superconducting material. IonQ makes qubits by trapping ions using electromagnetic fields. PsiQuantum is building qubits from photons.

A major benefit of photonic quantum computing is the ability to operate at higher temperatures than superconducting systems. “Photons don’t feel heat and they don’t feel electromagnetic interference,” says Pete Shadbolt, PsiQuantum’s cofounder and chief scientific officer. This imperturbability makes the technology easier and cheaper to test in the lab, Shadbolt says.

It also reduces the cooling requirements, which should make the technology more energy efficient and easier to scale up. PsiQuantum’s computer can’t be operated at room temperature, because it needs superconducting detectors to locate photons and perform error correction. But those sensors only need to be cooled to a few degrees Kelvin, or a little under -450 °F. While that’s an icy temperature, it is still easier to achieve than what’s required for superconducting systems, which demand cryogenic cooling.

The company has opted not to build small-scale quantum computers (such as IBM’s Condor, which uses a little over 1,100 qubits). Instead it is aiming to manufacture and test what it calls “intermediate systems.” These include chips, cabinets, and superconducting photon detectors. PsiQuantum says it is targeting these larger-scale systems in part because smaller devices are unable to adequately correct errors and operate at a realistic price point.

Getting smaller-scale systems to do useful work has been an area of active research. But “just in the last few years, we’ve seen people waking up to the fact that small systems are not going to be useful,” says Shadbolt. In order to adequately correct the inevitable errors, he says, “you have to build a big system with about a million qubits.” The approach conserves resources, he says, because the company doesn’t spend time piecing together smaller systems. But skipping over them makes PsiQuantum’s technology difficult to compare to what’s already on the market.

The company won’t share details about the exact timeline of the Illinois project, which will include a collaboration with the University of Chicago, and several other Illinois universities. It does say it is hoping to break ground on a similar facility in Brisbane, Australia, next year and hopes that facility, which will house its own large-scale quantum computer, will be fully operational by 2027. “We expect Chicago to follow thereafter in terms of the site being operational,” the company said in a statement.

“It’s all or nothing [with PsiQuantum], which doesn’t mean it’s invalid,” says Christopher Monroe, a computer scientist at Duke University and ex-IonQ employee. “It’s just hard to measure progress along the way, so it’s a very risky kind of investment.”

Significant hurdles lie ahead. Building the infrastructure for this facility, particularly for the cooling system, will be the slowest and most expensive aspect of the construction. And when the facility is finally constructed, there will need to be improvements in the quantum algorithms run on the computers. Shadbolt says the current algorithms are far too expensive and resource intensive.

The sheer complexity of the construction project might seem daunting. “This could be the most complex quantum optical electronic system humans have ever built, and that’s hard,” says Shadbolt. “We take comfort in the fact that it resembles a supercomputer or a data center, and we’re building it using the same fabs, the same contract manufacturers, and the same engineers.”

Correction: we have updated the story to reflect that the partnership is only with the state of Illinois and its universities, and not a national lab

Update: we added comments from Christopher Monroe

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Truckers have to transport massive loads long distances, every single day, under intense time pressure—and they rely on the semi-trucks they drive to get the job done. Their diesel engines spew not only greenhouse gas emissions that cause climate change, but also nitrogen oxide, which can be extremely harmful for human health.

Cleaning up trucking, especially the biggest trucks, presents a massive challenge. That’s why some companies are trying to ease the industry into change. For my most recent story, I took a look at Range Energy, a startup that’s adding batteries to the trailers of semi-trucks. If the electrified trailers are attached to diesel trucks, they can improve the fuel economy. If they’re added to zero-emissions vehicles powered by batteries or hydrogen, they could boost range and efficiency.

During my reporting, I learned more about what’s holding back progress in trucking and how experts are thinking about a few different technologies that could help.

The entire transportation sector is slowly shifting toward electrification: EVs are hitting the road in increasing numbers, making up 18% of sales of new passenger vehicles in 2023.

Trucks may very well follow suit—nearly 350 models of zero-emissions medium- and heavy-duty trucks are already available worldwide, according to data from CALSTART. “I do see a lot of strength and demand in the battery electric space in particular,” says Stephanie Ly, senior manager for e-mobility strategy and manufacturing engagement at the World Resources Institute.

But battery-powered trucks will pose a few major challenges as they take to the roads. First, and perhaps most crucially, is their cost. Battery-powered trucks, especially big models like semi-trucks, will be significantly more expensive than diesel versions today.

There may be good news on this front: When you consider the cost of refueling and maintenance, it’s looking like electric trucks could soon compete with diesel. By 2030, the total cost of ownership of a battery electric long-haul truck will likely be lower than that of a diesel one in the US, according to a 2023 report from the International Council on Clean Transportation. The report looked at a number of states including California, Georgia, and New York, and found that the relatively high upfront cost for electric trucks are balanced out by lower operating expenses.

Another significant challenge for battery-powered trucking is weight: The larger the vehicle, the bigger the battery. That could be a problem given current regulations, which typically limit the weight of a rig both for safety reasons and to prevent wear and tear on roads (in the US, it’s 80,000 pounds). Operators tend to want to maximize the amount of goods they can carry in each load, so the added weight of a battery might not be welcome.

Finally, there’s the question of how far trucks can go, and how often they’ll need to stop. Time is money for truck drivers and fleet operators. Batteries will need to pack more energy into a smaller space so that trucks can have a long enough range to run their routes. Charging is another huge piece here—if drivers do need to stop to charge their trucks, they’ll need much more powerful chargers to enable them to top off quickly. That could present challenges for the grid, and operators might need to upgrade infrastructure in certain places to allow the huge amounts of power that would be needed for fast charging of massive batteries.

All these challenges for battery electric trucks add up. “What companies are really looking for is something they can swap out,” says Thomas Walker, transportation technology manager at the Clean Air Task Force. And right now, he says, we’re just not quite in a spot where batteries are a clean and obvious switch.

That’s why some experts say we should keep our options open when it comes to technologies for future heavy-duty trucks, and that includes hydrogen.

Batteries are currently beating out hydrogen in the race to clean up transportation, as I covered in a story earlier this year. For most vehicles and most people, batteries simply make more sense than hydrogen, for reasons that include everything from available infrastructure to fueling cost.

But heavy-duty trucks are a different beast: Heavier vehicles, bigger batteries, higher power charging, and longer distances might tip the balance in favor of hydrogen. (There are some big “ifs” here, including whether hydrogen prices will get low enough to make hydrogen-powered vehicles economical.)

For a sector as tough to decarbonize as heavy-duty trucking, we need all the help we can get. As Walker puts it, “It’s key that you start off with a lot of options and then narrow it down, rather than trying to pick which one’s going to win, because we really don’t know.”

Now read the rest of The SparkRelated readingTo learn more about Range Energy and how its electrified trailers could help transform trucking in the near future, check out my latest story here.

Hydrogen is losing the race to power cleaner cars, but heavy-duty trucks might represent a glimmer of hope for the technology. Dig into why in my story from earlier this year.

Getting the grid ready for fleets of electric trucks is going to be a big challenge. But for some short-distance vehicles in certain areas, we may actually be good to go already, as I reported in 2021.

COURTESY URBAN SKYTwo more thingsSpotting wildfires early and keeping track of them can be tough. Now one company wants to monitor blazes using high-altitude balloons. Next month in Colorado, Urban Sky is deploying balloons that are about as big as vans, and they’ll be keeping watch using much finer resolution than what’s possible with satellites without a human pilot. Read more about fire-tracking balloons in this story from Sarah Scoles.

A new forecasting model attempts to marry conventional techniques with AI to better predict the weather. The model from Google uses physics to work out larger atmospheric forces, then tags in AI for the smaller stuff. Check out the details in the latest from my colleague James O’Donnell.

Keeping up with climate Small rocky nodules in the deep sea might be a previously undiscovered source of oxygen. They contain metals such as lithium and are a potential target for deep-sea mining efforts. (Nature)

→ Polymetallic nodules are roughly the size and shape of potatoes, and they may be the future of mining for renewable energy. (MIT Technology Review)

A 350-foot-long blade from a wind turbine off the coast of Massachusetts broke off last week, and hunks of fiberglass have been washing up on local beaches. The incident is a setback for a struggling offshore wind industry, and we’re still not entirely sure what happened. (Heatmap News)

A new report shows that low-emissions steel- and iron-making processes are on the rise. But coal-powered operations are still growing too, threatening progress in the industry. (Canary Media)

Sunday, July 21, was likely the world’s hottest day in recorded history (so far). It edged out a record set just last year. (The Guardian)

Plastic forks, cups, and single-use packages are sometimes stamped with nice-sounding labels like “compostable,” “biodegradable,” or just “Earth-friendly.” But that doesn’t mean you can stick the items in your backyard compost pile—these marketing terms are basically the Wild West. (Washington Post)

While EVs are indisputably better than gas-powered cars in terms of climate emissions, they are heavier, meaning they wear through tires faster. The resulting particulate pollution presents a new challenge, one a startup company is trying to address with new tires designed for electric vehicles. (Canary Media)

Public fast chargers are popping up nearly everywhere in the US—at this pace, they’ll outnumber gas stations by 2030. And deployment is only expected to speed up. (Bloomberg)

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AI models work by training on huge swaths of data from the internet. But as AI is increasingly being used to pump out web pages filled with junk content, that process is in danger of being undermined.

New research published in Nature shows that the quality of the model’s output gradually degrades when AI trains on AI-generated data. As subsequent models produce output that is then used as training data for future models, the effect gets worse.

Ilia Shumailov, a computer scientist from the University of Oxford, who led the study, likens the process to taking photos of photos. “If you take a picture and you scan it, and then you print it, and you repeat this process over time, basically the noise overwhelms the whole process,” he says. “You’re left with a dark square.” The equivalent of the dark square for AI is called “model collapse,” he says, meaning the model just produces incoherent garbage.

This research may have serious implications for the largest AI models of today, because they use the internet as their database. GPT-3, for example, was trained in part on data from Common Crawl, an online repository of over 3 billion web pages. And the problem is likely to get worse as an increasing number of AI-generated junk websites start cluttering up the internet.

Current AI models aren’t just going to collapse, says Shumailov, but there may still be substantive effects: The improvements will slow down, and performance might suffer.

To determine the potential effect on performance, Shumailov and his colleagues fine-tuned a large language model (LLM) on a set of data from Wikipedia, then fine-tuned the new model on its own output over nine generations. The team measured how nonsensical the output was using a “perplexity score,” which measures an AI model’s confidence in its ability to predict the next part of a sequence; a higher score translates to a less accurate model.

The models trained on other models’ outputs had higher perplexity scores. For example, for each generation, the team asked the model for the next sentence after the following input:

“some started before 1360—was typically accomplished by a master mason and a small team of itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other authors reject this model, suggesting instead that leading architects designed the parish church towers based on early examples of Perpendicular.”

On the ninth and final generation, the model returned the following:

“architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-.”

Shumailov explains what he thinks is going on using this analogy: Imagine you’re trying to find the least likely name of a student in school. You could go through every student name, but it would take too long. Instead, you look at 100 of the 1,000 student names. You get a pretty good estimate, but it’s probably not the correct answer. Now imagine that another person comes and makes an estimate based on your 100 names, but only selects 50. This second person’s estimate is going to be even further off.

“You can certainly imagine that the same happens with machine learning models,” he says. “So if the first model has seen half of the internet, then perhaps the second model is not going to ask for half of the internet, but actually scrape the latest 100,000 tweets, and fit the model on top of it.”

Additionally, the internet doesn’t hold an unlimited amount of data. To feed their appetite for more, future AI models may need to train on synthetic data—or data that has been produced by AI.

“Foundation models really rely on the scale of data to perform well,” says Shayne Longpre, who studies how LLMs are trained at the MIT Media Lab, and who didn’t take part in this research. “And they’re looking to synthetic data under curated, controlled environments to be the solution to that. Because if they keep crawling more data on the web, there are going to be diminishing returns.”

Matthias Gerstgrasser, an AI researcher at Stanford who authored a different paper examining model collapse, says adding synthetic data to real-world data instead of replacing it doesn’t cause any major issues. But he adds: “One conclusion all the model collapse literature agrees on is that high-quality and diverse training data is important.”

Another effect of this degradation over time is that information that affects minority groups is heavily distorted in the model, as it tends to overfocus on samples that are more prevalent in the training data.

In current models, this may affect underrepresented languages as they require more synthetic (AI-generated) data sets, says Robert Mahari, who studies computational law at the MIT Media Lab (he did not take part in the research).

One idea that might help avoid degradation is to make sure the model gives more weight to the original human-generated data. Another part of Shumailov’s study allowed future generations to sample 10% of the original data set, which mitigated some of the negative effects.

That would require making a trail from the original human-generated data to further generations, known as data provenance.

But provenance requires some way to filter the internet into human-generated and AI-generated content, which hasn’t been cracked yet. Though a number of tools now exist that aim to determine whether text is AI-generated, they are often inaccurate.

“Unfortunately, we have more questions than answers,” says Shumailov. “But it’s clear that it’s important to know where your data comes from and how much you can trust it to capture a representative sample of the data you’re dealing with.”

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How to access Chinese LLM chatbots across the world

Hundreds of Chinese large language models have been released since the government started permitting AI companies to open up their models for the general public to play around with in the summer of 2023.

For users in the West, finding these Chinese models and trying them out can feel challenging, owing to language barriers and registration requirements.

But in fact, a lot of the chatbots support conversations in English and are surprisingly easy to access. Whether you’re just curious to find out how well they perform or want to conduct more serious experiments for work, there are lots of ways to access Chinese LLM-powered chatbots. Here’s how anyone can try one out in minutes.

—Zeyi Yang

This story is part of MIT Technology Review’s How To series: helping you to get things done. You can check out the rest of the series here.

If you want to read more about why Chinese companies are betting on open-source AI, check out the latest edition of China Report, our weekly newsletter exploring China’s relationship with tech. Sign up to receive it in your inbox every Tuesday.

How battery-powered trailers could transform trucking

Semi-trucks move over 11 billion tons of freight in the US each year, spewing greenhouse-gas emissions and other pollutants along the highways as they go.

Shifting these and other heavy-duty trucks to zero-emissions technologies will be a challenge—even more so than for smaller vehicles, since larger vehicles require bigger batteries and more powerful chargers. One company thinks the key to progress is hiding behind rigs inside its trailers: building battery-powered trailers that can help pull their own weight. Read the full story.

—Casey Crownhart

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 CrowdStrike’s IT meltdown was caused by a bug
A safety mechanism failed, which allowed the flaw to be distributed. (Bloomberg $)
+ It’s highly likely that it’s the largest outage in history. (The Guardian)
+ How to fix a Windows PC affected by the global outage. (MIT Technology Review)

2 Can memes really help Kamala Harris win?
Harris’ brat summer is playing out well online—for now. (Vox)
+ Her team hopes her Extremely Online traction will translate into real votes. (The Atlantic $)

3 Cruise’s driverless cars are returning to US roads
But they’ll be accompanied by human safety drivers. (NYT $)
+ Tesla is officially delaying its robotaxi fleet launch. (FT $)
+ What’s next for robotaxis in 2024. (MIT Technology Review)

4 Meta has released an AI model to rival OpenAI
Mark Zuckerberg is confident that future versions of Llama will lead the industry. (WP $)
+ Llama 3.1 is open-source, free, and free from controls. (Wired $)
+ The tech industry can’t agree on what open-source AI means. That’s a problem. (MIT Technology Review)

5 The world’s oceans are getting dangerously warmAnd scientists are worried they’re reaching their limits. (FT $)
+ Meta’s former CTO has a new $50 million project: ocean-based carbon removal. (MIT Technology Review)

6 Airbnb hosts wants guests to circumvent booking through the appAnd book with them directly. (Bloomberg $)

7 It’s getting harder to defend cities from intense stormsSo residents are taking planning matters into their own hands. (The Atlantic $)

8 X has reinstated the real gun emoji
Replacing the water pistol symbol, for some reason. (The Verge)

9 Early stage job interviews are increasingly handled by AI
Critics are concerned the systems consolidate, rather than eliminate bias. (Rest of World)

10 The Olympics is on high alert for motor doping
Bicycles are being examined for tiny, hidden motors. (IEEE Spectrum)

Quote of the day

“I felt that we were throwing away our humanity.”

—Noah, an artist for video game company Activision, tells Wired why he is so fearful about the industry’s increasing interest in using AI to replace creative humans.

The big story

AI was supposed to make police bodycams better. What happened?

April 2024

When police departments first started buying and deploying bodycams in the wake of the police killing of Michael Brown in Ferguson, Missouri, a decade ago, activists hoped it would bring about real change.

Years later, despite what’s become a multibillion-dollar market for these devices, the tech is far from a panacea. Most footage they generate goes unwatched. Officers often don’t use them properly. And if they do finally provide video to the public, it usually doesn’t tell the complete story.

A handful of AI startups see this problem as an opportunity to create what are essentially bodycam-to-text programs for different players in the legal system, mining this footage for misdeeds. But like the bodycams themselves, the technology still faces procedural, legal, and cultural barriers to success. Read the full story.

—Patrick Sisson

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

• These baby prairie dogs are having the time of their lives.
• Long live electro-house: and its recent revival.
• Did you know that zombies are actually Haitian in origin? Fascinating stuff.
• If it’s a vegetable, it can be roasted.

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Semi-trucks move over 11 billion tons of freight in the US each year, spewing greenhouse-gas emissions and other pollutants along the highways as they go.

Shifting these and other heavy-duty trucks to zero-emissions technologies will be a challenge—even more so than for smaller vehicles, since larger vehicles require bigger batteries and more powerful chargers. One company thinks the key to progress is hiding behind rigs inside its trailers.

Range Energy is building battery-powered trailers that can help pull their own weight. By adding batteries to trailers, the company says, it can make a sizable cut in emissions, even while using existing diesel vehicles. The trailers could also be used with zero-emissions technologies like hydrogen- or battery-powered trucks to extend their range and efficiency as they hit the roads.

While there’s a growing wave of innovation in zero-emissions trucking, few companies have considered looking at trailers, says Ali Javidan, founder and CEO of Range Energy. “Essentially, it’s still just a dumb box on wheels,” Javidan says.

Range Energy, founded in 2021, is looking to make trailers smarter, adding batteries and a motor. The newest version of the company’s product incorporates between 200 and 300 kilowatt-hours’ worth of batteries. That’s more than what might be inside a passenger electric vehicle—battery packs in SUVs and pickups can be up to 100 kWh. But it’s significantly less than the batteries needed to power an entirely electric semi-truck, currently estimated around 800 kWh or more.

In Range’s trailers, the battery pack and the systems that manage it are connected to an e-axle at the rear, which delivers the power and helps move the trailer. The whole assembly is connected to the truck by the kingpin, or hitch, which helps sense the truck’s movement and controls how the trailer responds. The goal isn’t to drive the trailer from the back, Javidan says, but to help make the trailer feel weightless to the truck pulling it.

Adding an electric trailer onto a diesel truck turns the rig into something of a hybrid vehicle. The result can significantly improve both greenhouse-gas emissions and other pollution, like nitrogen oxide (NOx) emissions, Javidan says.

The company tested out an earlier version of its trailer that included a smaller battery of 100 kWh on a route with a mix of both flat highway and stop-and-go urban conditions. The trailer was able to improve gas mileage by about 36% over the whole route. That translates to cutting greenhouse-gas emissions by around a quarter.

Range’s newer trailers with larger batteries should improve gas mileage even more, and in certain conditions they could double the fuel economy, cutting emissions by up to half, Javidan says.

While semi-trucks only make up about 5% of vehicles on the road, they’re responsible for about a quarter of greenhouse-gas emissions from transportation. Finding options to help clean up emissions from heavy-duty trucks will be a major piece of cleaning up the transportation sector while making sure we have the products we need and use every day.

Range trailers can also significantly improve emissions of NOx pollutants, which are harmful for human health. In fact, they can have an outsize impact, since NOx emissions tend to be highest during specific operating conditions like when an engine is shifting. Range’s trailers are able to ramp their contribution up when it’s most needed, so the newest models could cut NOx emissions by up to 70%, Javidan says.

As they hit the roads, battery-powered trailers might face some of the same challenges that fully electrified rigs are running into.

In the US, trucks can’t be heavier than 80,000 pounds (40 US tons). Zero-emissions vehicles get a small buffer of an additional 2,000-pound allowance, but battery-powered rigs can run something like 5,000 pounds heavier than their diesel counterparts. That’s a major concern for operators, since the amount they can haul can be limited by those restrictions.

However, many loads reach the volume limits of a trailer before hitting the weight limit. This is called “cubing out” in the industry, and it’s common when hauling packages, for example. (Think of the last package you ordered from Amazon—if it was a box that had one or two items inside and a whole lot of air, you get the picture.) Those are the loads Range trailers will likely be most useful for at first, Javidan says.

Another concern is that electric trailers will rely on the same charging infrastructure that’s in short supply for trucks today, says Stephanie Ly, a researcher at the World Resources Institute.

Large trucks could take hours to charge even on the fastest chargers available today—a problem for drivers, who often face pressure to complete deliveries quickly. And installing more powerful chargers could require significant planning and investment from utilities.

But trailers tend to have more downtime than tractors, because many companies own more trailers than trucks. And a 200-kWh battery would take less than an hour to charge on one of the fast chargers commonly available today, so the problem might be more surmountable than it is for fully electric trucks.

Range Energy has one of its newest trailers running pilot tests in California and will launch several more this year, Javidan says. Then, the company plans to start building the next batch, which it will begin delivering to customers in early 2025.

Javidan declined to share how much the company charges for each of its trailers but says an up-front investment in one could pay for itself through fuel savings in just five or six years on average. And if trailers are driven for more miles, or in places where charging is cheap or fuel is particularly expensive, that payoff could be even faster, a potentially appealing prospect for companies with large fleets.

Still, getting fleet operators on board with new technologies may be a challenge—one that will be crucial to improving the climate results from trucking, says Thomas Walker, transportation manager at the Clean Air Task Force.

“It’s a multi-segmented problem,” Walker says. “It’s not just the vehicle. It’s not just the grid. It’s all of it.”

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This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday.

I’ve talked a lot about Chinese large language models in this newsletter, and I’ve managed to try out quite a few of them in the past year. But many people, especially those who aren’t very familiar with China or the Chinese language, probably don’t even know how to start if they want to test these models themselves.

The good news is it’s actually not that hard! I recently dug around and realized that many Chinese AI models are much more accessible overseas than I expected. You can access the majority of them either by registering accounts on their websites or using popular open-source AI platforms like Hugging Face. So I published this practical guide today that lists a dozen of the top Chinese LLM chatbots you can use and the methods to easily access them in minutes, from anywhere in the world.

During my experiments with these models, one thing soon became clear: While most Chinese AI companies have set a higher bar for access to their products than their Western counterparts, a trend toward open-sourcing AI models is making them ever more accessible to an overseas audience.

Take Qwen (or Tongyi Qianwen, as it’s called in Chinese), for example. This is Alibaba’s flagship AI foundation model. Unlike the company’s domestic competitors like Baidu, ByteDance, or Tencent, Alibaba has chosen to offer Qwen as an open-source model and allow developers and commercial clients to use it for free.

The model, which just received a major 2.0 update this June, has received a lot of international recognition. In Hugging Face’s most recent ranking that compares the performance of all major open-source LLMs, Qwen2 was ranked at the very top, surpassing Meta’s Llama 3 and Microsoft’s Phi-3.

Similarly, a few Chinese startups, like DeepSeek and 01.AI, have also decided to make their models open source, and the performance of their LLM products also earned them a high ranking on the leaderboard. Companies like them are giving their models out for free to people both inside and outside China.

The natural question to ask is, why? What does open-source AI mean, and why are these companies betting that making their models more open and accessible will be a good business decision?

For Alibaba, it’s a strategy to grow its cloud business, says Kevin Xu, a tech investor and founder of Interconnected Capital. “The simple economic consideration is that if their open-source model becomes popular, more people will use Alibaba Cloud to build AI applications using Alibaba’s open-source models, and that obviously benefits Alibaba Cloud as a business,” he says.

Everything Alibaba has done in open-source AI—releasing its own models to the public and building an open-source platform mimicking Hugging Face in hopes of gathering the AI community in China—serves the purpose of getting more people to sign up for Alibaba Cloud and pay to use its servers.

Even for Chinese AI startups that aren’t in the cloud business, open-source AI still offers a tried-and-true playbook for faster commercialization. On the development side, it allows them to adapt established open-source models like Meta’s Llama to accelerate their product development process. On the market side, it pushes them to think of alternative model architectures that can help them stand out from the mainstream.

“Right now, AI in the West tends to have a very fixed view of how to make an AI model better, [which] is just to add more data or to scale it up larger,” says Eugene Cheah, the San Francisco–based founder of Recursal AI, an open-source AI platform. It’s extremely hard for smaller latecomers in the LLM industry to play this game and develop a model that will rival GPT-4 or Gemini when OpenAI and Google have an outsize advantage in computing resources.

The problem is even more acute for Chinese companies, since US export controls mean they can’t easily access cutting-edge chips. “Because they are constrained by the GPU shortages,” says Cheah, “I see Chinese groups as being willing to experiment on wild ideas to improve the model. And some of these things are bearing results”—they have led to more efficient models that are cheaper to train and use, which can appeal to budget-conscious clients and help the Chinese firms find a niche market alongside the AI giants.

Why does it matter? For one thing, these open-source AI models present an alternative future where the industry isn’t just dominated by deep-pocketed players like OpenAI, Microsoft, and Google. And they also show that Chinese scientists and companies are able to create state-of-the-art open-source LLMs that can even surpass products from their Western counterparts.

Xu notes that Abacus AI, a San Francisco–based startup, released a model this year that’s adapted and fine-tuned from Alibaba’s open-source Qwen model. It’s even referred to as “Liberated Qwen.”

The Chinese AI companies’ introduction of high-performing models that US startups can build upon is an example of the best-case scenario of open-source AI, “where everyone builds on top of each other like a positive development loop,” Xu says. ”It’s not just a single direction where the Chinese companies are taking all the best stuff from the US, but things are now [also] going back the other way.”

Do you believe that open-source AI models will be able to compete with private, closed-source models in the future? Let me know your thoughts at zeyi@technologyreview.com.

Now read the rest of China ReportCatch up with China1. While a Windows system outage disrupted computers across the world on Friday, China was largely unaffected. Instead of the CrowdStrike software that caused the chaos, Chinese companies usually use domestic cybersecurity software. (CNBC)

1. Nvidia is working on yet another flagship AI chip, known as B20. It’s designed to sell to the Chinese market without violating US export controls. (Reuters $)

2. In a recent interview, Donald Trump accused Taiwan of taking the semiconductor industry away from the US and asked it to pay more for American military equipment. (New York Times $)

3. Guo Wengui, a self-exiled tycoon from China who has in recent years become an ally to US right-wing figures, was convicted for defrauding over $1 billion from online followers to fund his lavish lifestyle. (Mother Jones)

4. China recently withdrew from Top500, an international forum that ranks the world’s fastest supercomputers. The new secrecy will make it harder to understand China’s supercomputing advances from the outside. (Wall Street Journal $)

5. China is now mining and selling so many rare earth elements that the global prices of them have plunged 20% in the past year. (Nikkei Asia $)

6. The supply chain of fentanyl precursor materials in China consists of thousands of small chemical manufacturers. And the intense competition among them has driven them to continue selling to drug cartels in Mexico without worrying about the consequences. (Foreign Policy)

Lost in translationChina is experiencing one of the most extreme summers in its climate history, marked by severe drought and flooding across the country. In fact, these weather events are happening so often this year that nonprofit organizations working in disaster rescue and climate change response are facing significant funding shortages, according to the Chinese publication Phoenix New Media.

Despite government efforts to allocate disaster relief funds and supplies, the frequency and intensity of extreme weather events have stretched resources thin for organizations like the Shuguang Rescue Alliance. By July, Shuguang had used up 80% of its budget for the entirety of 2024. Additionally, fundraisers noted that with more disasters happening, the public is experiencing fatigue when asked to donate to another cause. This year, public and corporate donations have declined to 1/10th their previous levels after disasters, exacerbating the funding difficulties.

One more thingAspiring drivers in Beijing will now have to pass a day-long virtual-reality driving course before they’re allowed behind the wheel of a real car. It almost looks like a huge arcade with realistic driving games. To be honest, this might be one of the better uses of VR?

My wife is getting her driver's license in Beijing. After she passed the first two tests she is now doing one day of virtual reality driving before she begins driving a real car tomorrow.

This is her school: pic.twitter.com/y6MG1KH5ZC

— Jason Smith – 上官杰文 (@ShangguanJiewen) June 21, 2024

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

AI companies promised to self-regulate one year ago. What’s changed?

One year ago, seven leading AI companies—Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI—committed with the White House to a set voluntary commitments on how to develop AI in a safe and trustworthy way.

The eight commitments included promises to do things like improve the testing and transparency around AI systems, and share information on potential harms and risks.

On the first anniversary of the voluntary commitments, MIT Technology Review asked the AI companies that signed the commitments for details on their work so far. Their replies show that the tech sector has made some welcome progress—with some pretty big caveats. Read the full story.

—Melissa Heikkilä

To read more about how the US is approaching AI regulation, check out the latest edition of The Algorithm, our weekly newsletter untangling the complicated world of AI. Sign up to receive it in your inbox every Monday.

Google’s new weather prediction system combines AI with traditional physics

What’s new: Researchers from Google have built a new weather prediction model that combines machine learning with more conventional techniques, potentially yielding accurate forecasts at a fraction of the current cost.

Why it matters: The model, called NeuralGCM, bridges a divide that’s grown among weather prediction experts in the last several years. While new machine-learning techniques that predict weather are extremely fast and efficient, they can struggle with long-term predictions. General circulation models, on the other hand, which have dominated weather prediction for the last 50 years, use complex equations to model changes in the atmosphere and give accurate projections, but they are exceedingly slow and expensive to run. The new model attempts to combine the two. Read the full story.

—James O’Donnell

CRISPR Babies: Six years later

This Thursday at 12.30pm ET, subscribers can join our editor in chief Mat Honan and senior editor for biomedicine Antonio Regalado for “CRISPR Babies: Six years later”: a live virtual interview with He Jiankui, the Chinese biophysicist whose team created the first gene-edited humans.

Although he served a prison term, he has not given up on his idea that changing genes in an embryo could create people resistant to common diseases, such as Alzheimer’s. Register to attend here. And, if you’re not a subscriber already but want to join us, sign up for a subscription today.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Google isn’t getting rid of third-party cookies after all
It’s a major pivot that the ad industry will welcome wholeheartedly. (Digiday)
+ Chrome won’t automatically block the trackers, unlike Safari and Firefox. (The Verge)
+ The company’s deal to buy Wiz for an eye-watering sum has fallen apart. (NYT $)

2 Silicon Valley’s Democrats are raring to go
Now Joe Biden has stepped down, they’re ready to throw their support behind Kamala Harris. (NYT $)
+ Harris’ polling data is still not great, though. (Vox)

3 What we learned from Sam Altman’s universal basic income studyNo-strings cash provided recipients with far more flexibility. (Vox)
+ The extra money also granted them more autonomy. (Bloomberg $)

4 Condé Nast has ordered Perplexity to stop using its journalism
It’s the second legal demand the AI search engine has received. (The Information $)
+ AI companies have a Donald Trump journalistic problem. (NY Mag $)

5 China is keeping schtum about its supercomputers
Which is making it harder for outsiders to track its progress. (WSJ $)
+ What’s next for the world’s fastest supercomputers. (MIT Technology Review)

6 Waymo is suing people who vandalized its driverless cars
It wants hundreds of thousands of dollars in compensation. (Wired $)

7 Beware off-brand weight-loss drugsBusiness is booming for Ozempic imitators—but the risks are real. (WP $)
+ Weight-loss injections have taken over the internet. But what does this mean for people IRL? (MIT Technology Review)

8 Greenland’s glacial lakes are burstingRising temperatures are not good news for their supportive walls of ice. (New Scientist $)

9 The tech industry’s utopian Californian city is on hold
For at least two years. (NYT $)

10 Teeny-tiny microphones are a content creator’s dream
The smaller the better. (FT $)

Quote of the day

“This does open the floodgates.”

—A Silicon Valley executive tells Wired how Joe Biden stepping down ahead of the US Presidential election has reinvigorated the tech community’s faith in the Democrats.

The big story

A brief, weird history of brainwashing

April 2024

On a spring day in 1959, war correspondent Edward Hunter testified before a US Senate subcommittee investigating “the effect of Red China Communes on the United States.”

Hunter introduced them to a supposedly scientific system for changing people’s minds, even making them love things they once hated.

Much of it was baseless, but Hunter’s sensational tales still became an important part of the disinformation that fueled a “mind-control race”, with the US government pumping millions of dollars into research on brain manipulation during the Cold War.

But while the science never exactly panned out, residual beliefs fostered by this bizarre conflict continue to play a role in ideological and scientific debates to this day. Read the full story.

—Annalee Newitz

We can still have nice things