TechSequences: Recent Episodes
TechSequences
The introduction of the internet, a pivotal event in the Third Industrial Revolution, was shaped by crucial design and policy decisions made by early internet pioneers. Decisions such as adopting packet-switching for ARPANET, developing TCP/IP, and creating HTML and HTTP were fundamental in building the modern Internet. These choices enabled efficient data transmission, standardized communication, and facilitated the exponential growth of online connectivity. However, these decisions also had unintended consequences, including security vulnerabilities and the spread of misinformation.
Join us for a conversation with Scott Bradner and Scott Shackelford, authors of the book Forks in the Digital Road: Key Decisions in the History of the Internet about these early decisions, their profound impact, and potential lessons for the future.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- Forks in the Digital Road: Key Decisions in the History of the Internet
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Dave Hallett
Cancer, called the “Emperor of All Maladies” has been a formidable adversary of mankind since time immemorial. With its multitude of forms and elusive nature, cancer presents a daunting challenge for drug discovery. However, AI offers a glimmer of hope. From lung and breast cancer to melanoma and leukemia, AI-driven drug discovery is harnessing the power of machine learning, deep learning, and predictive analytics, to target a diverse array of cancer types for more effective treatments.
Join us for a conversation with Dr. Dave Hallet, an experienced drug hunter with over 20 years’ experience leading successful teams and collaborations for drug discovery. Dave is also the Chief Science Officer and the interim CEO at Exscientia, a publicly listed global drug discovery company headquartered in the UK.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- NIH: Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
- AI is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work
- Startups Are Using AI to Predict Responses to Cancer Drugs
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Jen Sovada
Quantum sensing is poised to revolutionize virtually every aspect of our world. Quantum sensing’s distinctive ability to detect magnetic signatures is already aiding in navigation for countless fuel tankers worldwide, providing otherwise unachievable medical scans, and keeping all of our computer clocks in sync.
Join us for a conversation with Jen Sovada, President of SandboxAQ’s Global Public Sector, on the existing and potential uses of quantum sensing and the consequences for fields as diverse as national security to navigation.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- Air Force Research Lab opens new ‘extreme computing’ facility amid international arms race for quantum tech
- C-17 Tests Magnetic Navigation System That Works When GPS Doesn’t
- SandboxAQ Successfully Tests Its Quantum Navigation System With The U.S. Air Force
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Alp Kucukelbir
The manufacturing sector, notably cement and steel production, accounts for nearly 20% of global CO2 emissions. As artificial intelligence (AI) looms large in its potential to reshape all industries, there’s mounting pressure to integrate AI into manufacturing as a tool to combat climate change. Advocates highlight AI’s capacity to revolutionize manufacturing processes, offering optimization of operations, predictive maintenance to preempt equipment failures, and enhanced resource efficiency. Moreover, proponents envision AI facilitating the shift towards a circular economy, where materials like steel and plastics are recycled rather than discarded after their useful lifespans. However, critics caution against the substantial energy consumption associated with AI, suggesting that its benefits in streamlining manufacturing processes may be outweighed by its energy demands.
Join us for a discussion with Alp Kucukelbir, adjunct professor at Columbia University and Chief Scientist and co-founder of Fero Labs, as we delve into the potential and pitfalls of AI integration in manufacturing.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- AI likely to increase energy use and accelerate climate misinformation – report
- Artificial Intelligence for Climate Change Mitigation Roadmap
- Artificial Intelligence threats to climate change
- 9 ways AI is helping tackle climate change
- UN: How AI helps combat climate change
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guests: David Bellos and Alex Montagu
Aside from IP lawyers, how many of us, particularly technologists, know about the origins of copyright laws and how they have evolved from the 18th century (yes pre-industrial revolution) to present? Our guess is not many. Inspired by a thought-provoking book “Who Owns This Sentence?: A History of Copyrights and Wrongs”, our latest episode delves into the origins of copyright laws and their evolution. Today copyrights touch virtually everything we see, listen, experience, or work with. But do they protect those they claim to protect? And just how relevant are they in the Fourth Industrial Revolution and the age of AI?
Join us for a discussion with the authors, Professor David Bellos and Alex Montegu.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- Who Owns this Sentence: A History of Copyrights and Wrongs
- Getty Images is suing the creators of AI art tool Stable Diffusion for scraping its content
- AI’s future could hinge on one thorny legal question
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Magnus Revang
Every year, Gartner unveils its Hype Cycle for Emerging Technologies report, spotlighting 25 pivotal technologies to keep a keen eye on. It is hardly surprising then that this year generative AI took center stage in the report as Gartner believes it will yield transformational benefits in the next two to five years.
Join us for a a wide ranging discussion about the challenges in designing clever user experience and the innovations we have made, or can expect, thanks to generative AI with Magnus Revang, Chief Product Officer at Openstream.ai. Magnus is a former Gartner Research Vice President and an award-winning product and thought leader in the fields of user experience (UX), AI, and Conversational Virtual Assistants.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- What’s New in the 2023 Gartner Hype Cycle for Emerging Technologies
- The Next Frontier of Customer Engagement: AI enabled Customer Service
- What is Multi-Modal and Some Interesting Applications
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Jenny Reardon
Since the mid 2000’s the field of genetics has seen rapid technological innovation, particularly in DNA sequencing and genotyping technologies that enable analysis of large portions of an individual’s genome at a relatively affordable cost. These advancements made possible not only the monumental Human Genome Project, but also direct-to-consumer DNA testing kits. So did these innovations democratize access to genetic information, or not? What about questions of access, control and inclusion?
Please join us for a conversation with Dr. Jenny Reardon, Professor of Sociology and the Founding Director of the Science and Justice Research Center at the University of California, Santa Cruz. Her research draws into focus questions about identity, justice and democracy that are often silently embedded in scientific ideas and practices, particularly in modern genomic research.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- 23 and Me is Terrifying but Not for the Reasons the FDA Thinks
- On the Emergence of Science and Justice
- Why and How Bioethics Must Turn toward Justice: A Modest Proposal
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Sally Lehrman
There is no question that an informed public and a free press are critical requirements for a functioning democracy. But what if the public has little to no trust in the news media? How can we have a functioning democracy if citizens are wary of what is fact versus fiction?
Please join us for a conversation with Sally Lehrman, award winning journalist, author and founder of the Trust Project, an international consortium of news organizations working towards greater transparency and accountability in the global news industry. The Trust Project developed Trust Indicators to identify, across news outlets and distribution platforms, the use of appropriate rigors of journalism. These enable the public to identify trustworthy news sources and thus make informed choices.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- The Search For Credibility: What users evaluate, view, and miss when assessing credibility of online news web sites
- Engaging With News: Indicators of Trustworthiness
- Microsoft Misinformation Research (media literacy campaign)
- The 8 Trust Indicators Handout
- What people really want from news organizations
- News consumers are growing smarter more skeptical
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Zoë MacDonald
The increased number of Internet enabled cars affords us luxuries deemed unattainable a few decades ago. We can now navigate roads in a way to avoid traffic jams, diagnose problems and often fix glitches with a software upgrade, personalize our driving experience and entertainment, and much more. But they are also massive personal information collection engines, stockpiling information about you from your use of the car, the car’s app as well as 3rd party services such as Sirius XM or Google maps. And, by the way, most share or sell your data, all with little to no transparency or accountability to the users.
How did this come about? Are some manufacturers better than others? Should we accept these costs as the unavoidable cost of convenience? And if not, then what recourse do we have? To discuss these and more, join us for a conversation with Zoë MacDonald of the “Privacy Not Included Buyers Guide” of the Mozilla Foundation.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- Individual Car Brand Reviews
- It’s Official: Cars Are the Worst Product Category We Have Ever Reviewed for Privacy
- Petition urging car companies to stop data collection programs
- “Is This Even Legal?” Our Top Cars-And-Privacy Question, Answered
- Privacy Not Included Buyers Guide
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Nestor Maslej
Artificial intelligence has made inroads in each of the seven categories of art: architecture, cinema, literature, painting, music sculpture, and theater. And in doing so, has challenged what it means to create art, as well as stirred debate about the potential consequences for human creators (writers, actors, and artists), and consumers.
Join us for a discussion about the impact of AI on art with Nestor Maslej, fellow at the Centre for International Governance Innovation, a technology and policy think tank, and the research manager at the Institute for Human-Centered Artificial Intelligence (HAI) at Stanford University.
By the way — like the episode title? It was generated for us by AI
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- AI generated short film, The Frost
- Authorship Is Here to Stay — Though AI Will Change What That Means
- As Actors Strike for AI protections, Netflix lists $900,000 AI job
- WGA Would Allow Artificial Intelligence in Scriptwriting, as Long as Writers Maintain Credit
- The AI Index Report – Measuring Trends in Artificial Intelligence
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Courtney C. Radsch
A revolution is afoot in media and by extension, journalism. Economic factors have caused the decline of local media to the point where in the US two newspapers are forced to shut down every week. Another revolution – this one driven by advances in AI – is also shaking up media outlets and journalism. AI can represent enormous opportunities as well as existential risks for journalism. At question is both the use of AI by media outlets as well as the use of content (including copyrighted works) by AI companies. As media outlets like Germany’s biggest tabloid, BILD, and Buzzfeed start to use AI, critics warn that use of AI in media and journalism could create and amplify misinformation, disinformation as well as promote bias. It is clear that if we are to set guardrails for the use of AI, the time is now. But the question remains what and how?
Please join us for a conversation with author, researcher and scholar Dr. Courtney Radsch, the Director of the Center for Journalism and Liberty at the Open Markets Institute to discuss the potential consequences for media and journalism with the advent of AI.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- Sustaining Journalism, Sustaining Democracy: A Policy Guide on Platforms and the Press. Eds. Michael Karanicolas, Courtney Radsch, Akshat Agarwal and Nicholas Wilson. UCLA Institute for Technology, Law & Policy. April 2023.
- URGENT: Understanding and Responding to Global Emerging News Threats. Internews March 2023.
- Platformization and Media Capture: a Framework for Regulatory Analysis of Media-Related Platform Regulations. UCLA Journal of Law & Technology, Platforms and the Press, 28, no. 2 (Winter 2023): 175–223.
- Frenemies: Global approaches to rebalance the Big Tech v journalism relationshipBrookings Tech Tank. Aug. 29, 2022.
- Making Big Tech Pay for the News They Use. Center for International Media Assistance
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Sheldon Himelfarb
With the advances in AI and increasing sophistication in creating misleading content such as deepfakes, there is growing concern especially amongst academics and researchers about the threat mis- and dis-information pose not only for the 2024 election cycle and democracy, but also for pressing concerns such as public health and climate change. How can we address a polluted information ecosystem at a time of significant social divide and erosion of trust? How can scientific principles and approach inform policy?
Please join us for a conversation with Shelton Himelfarb, the co-founder and Executive Director of the International Panel on the Information Environment (IPIE) an independent global organization of over 300 leading scientists dedicated to providing actionable scientific knowledge on threats to our information landscape.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- IPIE
- With Climate Panel as a Beacon, Global Group Takes On Misinformation
- Nobel Prize Summit: Truth, Trust and Hope
- Federal Judge Limits Biden Officials’ Contacts With Social Media Sites
- False Accusation: PAUL M. BARRETT AND J. GRANT SIMS The Unfounded Claim that Social Media Companies Censor Conservatives
Guest: Jef Caers The EU is requiring all cars sold in the European Union by 2035 to be zero-emission vehicles, and the US has set a target of 67% by 2032. But there is a catch. Transition to Electric vehicles
Guest: Jane Pinelis A new movie called “The Creator” paints a vivid picture of humanity at war with AI in a dystopian future. Although the movie is science fiction, it highlights a major issue: How can we reliably assess and
Guest: Alex Engler
ChatGPT has highlighted the excitement and fear about the potential consequences of AI for humanity, and in doing so has pushed forth the need to examine if and how to regulate AI. However, we currently lack a coherent and global roadmap on how we should address issues such as fairness, transparency, standards and innovation. It is reasonable that like-minded governments such as the US, UK and EU, should find ways to cooperate on AI innovation and regulation, with an approach that promotes shared values such as respect for human rights, inclusion, non-discrimination and protection of privacy and personal data. However, this is easier said than done. So is there a realistic guide to managing AI’s risks and promises?
Please join us for a conversation with Alex Engler, fellow at the Brookings Institution, for a review of current regulatory perspectives and how to meaningfully collaborate on standardization, oversight and/or regulation, and innovation.
Hosted by: Alexa Raad and Leslie Daigle.
Further reading:
- Calls to regulate AI are growing louder. But how exactly do you regulate technology like this?
- ChatGPT banned in Italy over privacy concerns
- 70% of Workers using ChatGPT at Work Are Not Telling Their Bosses; Overall Usage Among Professionals Jumps to 43%
- AI Bill of Rights makes uneven progress on algorithmic protections
- The Eu and U.S. diverge on AI regulation: A transatlantic comparison and steps to alignment
- NIST AI Risk Management Framework
- AI Standards Hub (UK)
- Submission to the EC White Paper on Artificial Intelligence (AI) The importance and opportunities of transatlantic cooperation on AI
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Susie Alegre
Freedom of thought — the right to form your own thoughts, keep them private and to not be persecuted for your thoughts alone — is one of the most fundamental human rights. This right, recognized in the Universal Declaration of Human rights in 1948, seems so utterly basic that we may take its protection for granted. Yet this very right may be undermined every time you are presented with a curated selection of content determined by an algorithm purpose built to get and engage your attention. Join us as we talk to international human rights lawyer and author of “Freedom to Think: The Long Struggle to Liberate Our Minds” about how our thoughts and opinions are manipulated and what to do to reclaim this basic right.
Further reading:
- Freedom To Think: The Long Struggle to Liberate Our minds
- The Guardian Review of Freedom to Think
- United Nations Universal Declaration of Human Rights
- Core International Human Rights Treaties
- UN: Foundations of Human Rights Law
- Supreme court hears a case that could transform the internet
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Dan York
Low Earth Orbit (LEO) satellites such as Starlink have been credited with helping Ukraine in the Russo-Ukrainian conflict. Their promise extends beyond military reconnaissance to everything from in-flight Internet access to disaster recovery, but challenges remain. Even as companies race to put literally thousands of satellites into orbit, concerns about regulation, management of limited resources, and additional space junk are being raised. Please join us for a chat with Dan York, Director of Internet Technology at the Internet Society, to examine the promise, the implementation challenges, potential consequences of the growing LEO industry.
Further reading:
- How Elon Musk’s satellites have saved Ukraine and changed warfare
- Ukraine Official Asks Elon Musk for Starlink Stations Amid Russian Invasion
- Perspectives on LOE satellitesTragedy of the Commons
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Guest: Enza Iannopollo
The metaverse is already here. Companies like Meta, Alphabet, Apple, Microsoft, Nike, Nvidia, Epic Games and even SK Telecom have invested heavily. Thus far, even with 400 million users, the metaverse is still in its infancy. The 3-D virtual places are run by different companies with little interoperability in terms of devices or operating systems; there are no persistent identity and property rights. Would we expect more of the same? Join us for a conversation with Enza Iannopollo, Principal Analyst at Forrester for a conversation on the key challenges to solve to deliver the full promise of the metaverse.
Further reading:
- Snow Crash, Novel by Neal Stephenson
- Meta CEO Zuckerberg predicts the metaverse will be mainstream in 5-10 years
- Creators in the Creator Economy- Adobe
- The Smithsonian Institution’s augmented reality
- Mark in the metaverse
- A plot of virtual land that went or $4.3M the Sandbox is the most expensive metaverse property sale ever
- Investors are paying millions for virtual land in the metaverse
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
For centuries, humans sought to control their future, by anticipating the possibilities and planning accordingly. This model, conditioned on observations and experience – does not work as well in a chaotic and unpredictable world dotted with Black Swan events. How has the internet age redefined our mental models for predicting and even strategizing for the future? Please join us for talk with author, researcher and Fellows Advisory Board member of the Berkman Klein Center, Dr. David Weinberger.
Sources:
- Everyday Chaos: Technology, Complexity, and How We’re Thriving in a New World of Possibility, David Weinberger
- Black Swan by Naseem Nicholas Taleb
- Alien Knowledge (Wired)
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
User Generated Content, known as UGC, has changed the media landscape and business models. UGC includes anything from video, images, text, blogs, and even audio. It has launched protest campaigns, provided eyewitness accounts of major events, been monetized by influencers and brands and perhaps most importantly changed the way we consume news.
Please join us and our guest, Dr. Claire Wardle, co-founder and co-director of the Information Futures Lab, and Professor of the Practice at the Brown School of Public Health, to examine the consequences of user generated content on how we view ourselves and the world we live in.
Sources:
- Information Futures Lab at Brown University School of Public Health
- User Generated Content Represents 39% of Time Spent with Media
- Claire Wardle Ted Talk
- Time Magazine Person of the Year 2006
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Cybercriminal gangs have evolved into fully-fledged for-profit enterprises that bank on our increasing dependence on digital access to profit handsomely with little to no repercussions. Cybercrime costs $600 billion – or close to 1.0% of global GDP- and that number is only expected to increase. Acknowledgement of appropriate norms of behavior among nation states have not evolved beyond Cold War era treaties, nor have our regulations kept up with the evolution of technology and online business models. To combat global cybercrime collaboration is needed amongst like-minded allies as well as within the private sector.
Join us for a conversation with Cyber Threat Alliance CEO, Michael Daniel about the challenges and solutions to combating cyber criminals.
Sources:
- The Hidden Cost of Cybercrime
- New Survey Says We are Spending an Average of 7 hours a Day Online
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
Globally there are 4 billion people who use social media an average of two hours and 30 minutes a day. At the same time, social media has been simultaneously praised and denounced in equal measure for its influence on society. Regulators have proposed a range of policies including anti-trust to rein in what they see as its abuses. Yet most if not all of the proposals seek start with the current scenario and seek to blunt its egregious excesses. But is there a way to reimagine social media as a force for good? Yes, perhaps.
Please join us for a conversation with Prof. Ethan Zuckerman of University of Massachusetts, Amherst for a conversation about the “Good Web”.
Sources:
- KEYNOTE: Ethan Zuckerman – What Went Wrong With the Web, and Who’s Working to Make it Better
- The Good Web
- Rewire: Digital Cosmopolitans in the Age of Connection
- Mistrust: Why Losing Faith in Institutions Provides the Tools to Transform Them
- Podcast: Reimagining the Internet
Hosted by: Alexa Raad and Leslie Daigle.
The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.
The US national security and military complex has fostered scientific and technological innovations to gain and maintain the United States’ security and strategic advantage over its adversaries. Yet as technology and models for innovation have evolved, the means by which
Guest: Chuck Brooks Ukraine has become ground zero for a new kind of war: a hybrid war that blends conventional warfare and cyberwarfare with tactics such propaganda, fake news, and even foreign electoral intervention. In doing so, both sides of the
Guest: Rafal Rohozinski The Ukrainian conflict is the world’s first hybrid war. In other words, it is the first international conflict playing out on both the battlefield as well as cyberspace. Despite fears about Russia’s prowess in cyber offense capabilities,
Guest: Jeffrey Carr The conflict in Ukraine is redefining what a world war would look like, and it is decidedly different than what we had imagined. This may well be the world’s first “hybrid” war as the battlefield extends beyond the traditional
Guest: Vint Cerf We have reached a small milestone: Our 50th published episode! To mark the occasion, we take a look back at the technologies that shaped the 20th century, especially the Internet, and we look forward towards those that
Guest: Robert Tercek The race is on. The competition to make, define, own and monetize the metaverse has just begun. Although “metaverse” became a buzzword after Facebook changed its company name to “Meta” in 2021, many people are still trying to understand what exactly the metaverse is and whose
Ukraine, a fledging democracy is under attack on two fronts, with tanks and artillery as well as with disinformation and propaganda campaigns. It is not the only democracy under siege from disinformation campaigns. Many democracies face targeted campaigns to manipulate
Everything we have learned, lived and now remember is grounded in one unassuming fact: It took place in the physical world. And we may very well be the last generation for whom this distinction applies. Future generations will live, learn
Disinformation is fast becoming normalized as part of political campaigns everywhere. Information technologies are being harvested for their utility in targeting and manipulating opinions often with substantial consequences for civic discourse and the nature of truth. Join us for a
“Who are you?”. Answering that may seem at once easy and yet incredibly complex. In the real world, we are born with, gain or develop aspects of our identity. But distinguishing who is who is a lot more complex online.
If you did not know by now, rest assured there is a thriving disinformation industry. But have you ever wondered how it actually works? Who are companies who provide it? Who are the workers who act as Trolls, and why
The next iteration of the Internet, is called the “metaverse”. It is where we anticipate our physical and virtual worlds will converge. But options differ on what it will look like or if and how it will be controlled. Will the Metaverse
We are in a technical revolution where cutting edge innovations in areas such as AI, 5G, robotics, and even gene editing, offer enormous promises for our lives, ranging from the mundane to the spectacular. Our persistent optimism overestimates the positive
Vaccination status – namely, who IS and who IS NOT vaccinated is hotly debated on every front. The Covid-19/vaccine passport purports to help. These passports are digital or paper documents that show you were vaccinated against COVID-19, and/or have recently tested negative for
The pandemic and its massive impact on every aspect of our lives for the past 18 months has been a wakeup call on change, especially that which we cannot control. For centuries, our human ingenuity has sought to control
Does online interface design shape or influence how we behave online? For example, do we really behave worse than our true selves when given the option to mask our identity? When technologies that obfuscate online activities, mask identities or create fake profiles are just as plentiful as technologies designed to surveil and track every minutia of our physical and virtual lives what can or should we expect? How can we rethink the way we design our online spaces to tilt the balance for good? Join us for a conversation with Judith Donath, fellow at Harvard’s Berkman Center and the founder and former director of the Sociable Media Group at the MIT Media Lab. Her work combines knowledge from urban design, evolutionary biology and cognitive science to design innovative interfaces for on-line communities and virtual identities.
Guest: Judith Donath
Hosted by: Alexa Raad & Leslie Daigle
Transcript (autogenerated by Descript) Alexa Raad: In his 1889 essay “The Decay of Lying” Oscar Wilde, the famed Irish playwright, poet, and wit opined “Life imitates Art far more than Art imitates Life”. As professor Michelle Mendelson observes in her book, “Making Oscar Wilde”, Wilde’s essay highlighted an inherent if not always admitted truth about human behavior and social sciences.
That the law of etiquette governing polite society, where in fact, a mask tack was merely an elaborate art of impression management Oscar’s musics took place in an era when sociology was still in its infancy, psychology had not yet been born. And though the industrial revolution had changed society. The consequences were nowhere near as dramatic as those of the digital revolution of the late 20th and early 21st.
Right. You can’t help, but wonder if Oscar Wilde were alive today? What would he think of the online world and what are you upon that life is a mirror for the internet or the other way around. Consider this today, 40% of internet traffic is generated by bots and 45% of user accounts are the popular social media platform.
Instagram are reputed to be fake technologies that obvious scape online activities, mask identities, or create fake profiles are just as plentiful as technology’s designed to surveil and track every minutiae of our physical and virtual. Despite this push and pull. What is clear is that the concept of identity has become far more nuanced and complex, and that deception has been elevated from art and industry to Wharf.
Need evidence simply consider the rise of disinformation and the existential threats, it poses to the democratic institutions around the world. So should we accept deception as an inevitable consequence of our increasingly digital lives and simply look to mitigate its impact?
Or can we rethink interface design in a way that shapes online identity and influences that behavior tilting the balance?
Leslie Daigle: Our guest today is Judith Donath. She’s currently a fellow at Harvard’s Berkman center. She’s also the founder and former director of the social media group at the MIT media lab where she and her students created pioneering and influential social visualizations and interfaces that have been exhibited in museums and galleries worldwide.
As a writer and researcher, she examined various aspects of the internet and its social impact such as online communities, interfaces, virtual identities and privacy. Her work combines knowledge from urban design, evolutionary biology and cognitive science to design innovative interfaces for online communities and virtual ideas.
Judith is the author of the social machine designs for living online, published by MIT press and is currently writing a book about technology trust in deception. She obtained her bachelor’s degree in history from Yale and her master’s and PhD degrees in media, arts and sciences from MIT. Welcome Judith.
Thank you for having me. So in your book, the social machine designs for online living, you lay out a manifesto for rethinking how we design interfaces. You argue that design shapes society. Can you explain what you mean?
Judith Donath: Well, a lot of it is, if you think about let’s look at, you know, before we get to online things, it’s looking at even people’s behavior in certain buildings.
Um, the medieval churches, for instance, were built to inspire art, to make people feel very small in this year, very square. There was this powerful God controlling their fates. And so the design of the spaces that like that were meant to influence how people behave and that’s in a world where your physical self cannot be changed by the architectures.
It’s your perception of the world. Now, if you look at things like online spaces, The interface design not only affects what you’re seeing of other people. It affects the posture that you take, that you’re sitting around a particular screen that you can get up and leave at any time, but it also completely controls what you can see of other people.
It lets you, it controls whether their identities are. Um, defined and checked and redefine, or it decides to people can be easily anonymous. So that’s why the interfaces have such a powerful role in shaping how people behave. Because if they can’t really see the other participants, particularly as humans, they’re just, you know, lines of texts or jokes that float by.
The constraints that we normally put on our behavior, partly out of them, but they may be quite weakened by that. If you see the other very much as a person and you see that everything you’ve said sticks with you and your identity, maybe a lot more circumstances.
Leslie Daigle: That’s really, really interesting. And I know that having been interacting on the online world myself for a few decades, I sort of the difference of seeing people as just lines on texts, way back when, when that’s all we had to, you know, at least some level of avatars and whatnot nowadays is, is interesting.
Although there are some interesting differences of behavior that we see in, in online exchanges than we would ever see in the real world. I’m sure. I’m sure we’ll get to that. Um, but before we get to that, um, so I understand that you believe surveillance and privacy are also design issues. Can you elaborate a bit on that aspect of it?
Judith Donath: Um, well,
Alexa Raad: there’s
Judith Donath: surveillance is a
Leslie Daigle: big issue to elaborate
Judith Donath: on. So, I mean, let’s start talking that actually, but what is surveillance, um, before you say, why is it it’s sign issue? And surveillance has an effect on how people behave, because it basically says there’s some kind of control that’s on the external, whether it’s a being or an institution or just a group of sort of alpha people in your unit can affect you and you can control your own way or they can impose some kind of punishment or reward.
What surveillance does it say? That type of person or institution doesn’t have to be there at the moment. You
Leslie Daigle: know, it says there are other
Judith Donath: eyes on behavior are eyes of certain type, whether they’re other humans, whether it’s your belief in God, whether it’s cameras wrestling from every building, but there’s a set of eyes watching things and feeding that information about what people are doing to some kinds of.
What we can call like an authority institutional song type. So the control, so surveillance itself, isn’t a bad, it doesn’t control anything. But what it does is it can move information to a con to it empowers the controlling institutions and have these eyes. And to a large extent, the, what, the control that has learned, how people behave isn’t in that post activity.
Punishment or reward. It’s your anticipation of it? It’s your knowledge that this is likely to happen in a way, then lease yourself with this expectation of being
Alexa Raad: watched. So that brings me to the issue of pseudonyms, because a lot of times people create pseudonyms, uh, various pseudonyms for. Various social media platforms we post on, uh, whether it’s Facebook or let’s say Twitter, Instagram, and so forth.
And the conventional wisdom would say that if you actually had your own identity on there, then you would not behave. Perhaps the way that you can behave with a pseudonym, which masks you identity even potentially with surveillance, uh, being onboard
Leslie Daigle: it,
Alexa Raad: but you say that pseudonyms are actually, um, helpful and.
And, and, and can strengthen communities. It’s not just, uh, helping you maintain privacy and manage your identity, but that you can also strengthen online communities. So this seems to fly in the face of the conventional wisdom. What do you say to that? How do you respond? So
Judith Donath: let’s look at online identity is having sort of three broad varieties.
There’s anonymity, pseudonymity, and real identity. Anonymity is where you can act in particular ways and your behavior, both doesn’t carry through to know your real name, your real physical self, but it also doesn’t carry through over time. It’s just a standalone. So no history. Okay. There’s no history.
There’s nothing that ties it to the next thing that you do. It’s just, it just sort of out there actions. Um, that’s how, for instance, a site like fortune. Where people who really try very hard because they want to own something. They said, they think it’s clever, but it’s really designed so that the words come out, you really can’t build up a history with that.
And then there’s online identities that are tied to your real self. So you say something, but if you’re doing it under your physical name, it comes back to me. And the reason I say that I think pseudonyms and synonymous communities online are really important. It’s a pseudonym is a, some kind of identity that you can have in a mediated space that is not necessarily tied to your real world itself, but it is tied to a particular history of that identity.
The reason it’s important in a way that to do this online in a way that it’s not, or it doesn’t make sense in the physical world, besides that it’s possible to do online, is that in the physical world, because at least until very recently, most of what you did was fairly ephemeral. You could, um, go home to a family dinner and act the way your family expected you to do.
But then if you were out with your friends, you could be a very, very different. And these different facets of your personality were easily divided by time and space online because things are searchable and these different spaces will collapse. Because know if I search your name and everything is searchable and everything is done under your name comes up together, you lose that kind of contextual separation.
That has been the norm in our societies. The pseudonym lets you. Be able to have, you know, for instance, your social life not be something that’s constantly traced back to what you do at work.
Alexa Raad: Oh, you have a LinkedIn profile, which is all, all professional, but then you can post on your Facebook things that
Judith Donath: are Twitter, cat that was under a different name.
Now the issues you’re talking about, about behavior or the reason that pseudonym is important and it works only if you still value that identity. So if I want to build up a very credible identity on unsafe Twitter, as someone who has really interesting things to say about the session, blah, blah, blah, blah.
For some reason, maybe it’s because I live in a, in a nation where the things I want to say are not acceptable, or maybe because my political opinions are not acceptable at my job, or to make them. I may want to keep that private and separate, but I still want it to have a long-term reputation that I still value.
And so it’s not. So you could still value how that identity is presents itself and behaves because you care about building that up over time. And that’s why it’s a design issue because it’s that incumbent on the designers of the spaces to say. We want you to develop a persona here that you value highly, that you will take care of, and that you will
Leslie Daigle: not want to be
Judith Donath: despised by the other users here, but that’s something that you have to design your injure things so that those are really solid and significant and trusting.
Alexa Raad: Multifaceted. So there’s a question about trolls though, because trolls, for example, really don’t care about having a history of their persona, right? I mean, they are meant to be very argumentative. They’re meant to be in your face. Um, often not controlled, you know, often bots. So they don’t necessarily behave in the way that you’re describing.
How would you design an interface and they’re a part of our everyday lives. You know, we can’t really sometimes tell the difference of what is a troll and what is a, uh, you know, a human being. So how do you, how do you design that interface so that you lessen this type of very aggressive, confrontational type of behavior?
Judith Donath: I million, a lot of the work that my students and I had done at the media lab was in an area we called social visualization and things like, um, portraits of people through their words. So for instance, one of the projects we were interested in was how do you visualize, oh, I’m going to use Twitter as an example, just because it’s, it’s easy and people know it, but I don’t mean this to be particularly specific to Twitter.
Right now, if you see some tweet comes by and you’ve no idea who this person is, it’s what comes up. If you look at them quickly, it’s like their name. It’s who they claim to be. Um, they may have tons of followers, but they may have bought those. On the other hand it’s if those behaviors were truly, if the way they were acting was truly indistinguishable.
It’s, let’s say they are troll from a sort of helpful contributing person whose words you’d be interested in. They wouldn’t actually be that troll because they would be saying interesting things. I mean, to the extent that they’re a troll and you don’t want to hear from them is that they have some history of unpleasant interactions of making false statements that you would find offensive or something that you consider problematic.
And we’ll get into later whether the issue of whether the issue between whether they’re humans or machines, but the idea of the social visualization is something that would help you see much more at a glance. Something of, of someone’s history. So if you could see a visualization that would show you instead of like walking down the street and there’s so much information you see in people’s faces and how they interact with each other, like in a few seconds, you build up a often, fairly accurate impression of their personality.
How do you take the history of what someone has said the way they interact with others and say, here’s a way to visualize this so that at a glance you can see. This person seems intriguing versus this seems like someone who is just here to harass others.
Leslie Daigle: I don’t have an image in my mind if something like Charles Schultz is pig pen character with sort of like the cloud of dust that follows him everywhere.
Judith Donath: Okay. So, yeah. So I mean, with doing visualizations like this, which still, I mean, I see very little done with this. And the reason for that I think is, has to do with. Limited number of social networks and things of that. There’s not that much experimentation these days, but so there’s two sides of it. One is the question, what is the data that you would want to visualize for something like this?
You know, what is, you know, in a public space, we’re not worried worrying about privacy issues, but you know, is it, is it their words? Is it, what are the types of people they follow? Is it some, how do you depict, who follows them? How do you get a sense of the extent to which their interactions. Inspire reaction versus are reactive to others, you know?
And there, it turns out you could start to pull out those patterns fairly easily, but then you get into that other question, which is another design, which is how do you represent it so that it’s not just, you know, another graphic where yeah. The Charles looked kind of cool, but the nice people that kind of cool too, is you want to think that how do you make it intuitive without being too heavy handed at editorial piece?
Right. So you don’t want it to say, well, if they do this sort of thing, we’re going to make it all like gray and brown and angular versus some of that. So that’s another really interesting set of research questions. How do you make those visualizations so that they are re neutral yet intuitive?
Leslie Daigle: Can we see a tiny, tiny little reflection of that?
I think in online comments and in like on Amazon, for instance, right. You can see reviewer comments and that there are. Indications as to whether this person has been, you know, as a new customer of Amazon or whether they’ve been a long time customer of Amazon, you know, verified purchaser or whatever. So tiny little fragments of, of sort of history pertinent to the, to the, the reality of this person.
Judith Donath: Yeah. And I mean, I think, you know, even 15, 20 years ago, there was like a lot of excitement about things like word clouds. And I think part of the, um, I would like to see more work being done in this area. And I think part of it is that we’ve become so used to looking at LinkedIn Facebook, Twitter, Instagram, which have not any, I mean, their interfaces are not particularly visually compelling and they don’t have a real business reason to do this kind of thing.
So I think we haven’t been seeing a lot of work in this area, but you know, if you can think of. Not just seeing whether they’ve reviewed things before, but to start seeing like a word cloud of their reviews, just to have it sense. Like, is this someone who’s always super negative, are the words they’re using in this review, something they use all the time.
Do they usually review restaurants? You know, what type of things do they review? Are their reviews, usually quite contrarian to the other things or, you know, to everything else. There’s, there’s a bunch of things like that that are, that would actually give you a pretty good picture of how. Believable they were, or even the things we’ve used.
There are another interesting one, just because a lot of times what would be useful, there would be something that was a fairly subjective view, because if I want to look at say, move very views or book reviews, I want to see it in comparison to things that I like. Because if I happen to like a particular type of coworking movie that only 10% of other people.
Other people share that taste. I mean, that’s also partly the way I love algorithms has gotten in trouble, sending people down more and more unusual routes of things that they like, that others don’t like, which seems to always end up in like Nazi literature somehow. But there are ways of dealing with those problems in the algorithm, looking for niche, ask tastes that I don’t think I always have to end up there, but certainly are useful to be able to see a, to have a lens.
To be able to choose between looking at things like that through a subjective or a more objective lens.
Leslie Daigle: And I think, I think there’s an important thing to drill down on here a little bit, which is, I mean, I guess in part I would, my brain would summarize a little bit of what we’ve discussed so far as there’s the content of messages that we see today, whether it’s text or video or whatever, but that in spite of the fact that we in the online world sort of read each piece individually and think that that’s the thing.
That it, that it needs to be understood. And that in the context of who has shared it and, and sort of whether they have history or whether they are, you know, anonymous and have no history, um, or, and then beyond history is sort of like, what, what flavor are they? So there’s, there’s all of that, that you’re, you’re describing as trying to capture that in some way, visually in, in, in an interface.
But let’s not lose sight of the fact that the importance of capturing it in the interface, from what I hear you articulating is we can get to better interactions if we have a better representation of the context of these messaging, so that it doesn’t just always spiral down the drain into, you know, flame wars and, and, and all kinds of negativity.
Judith Donath: Yeah. And context is extremely important. And I think a lot of things. Issues we’re seeing with, um, fake news and not, I mean, there’s those go into different directions? One of the problems is that, you know, and this is not speaking specifically about news, you get from people, but, um, how much less context people have even for news.
So are you going to go with my partner all the time? Who’s a professor who will tell me, oh my God, did you see this new story? I’m like, well, where is it from? It’s like, it was all my news reader. I’m like, okay. It didn’t, it didn’t originate in your news reader. I mean, can you tell me, like, is this from the Washington post or is it from Fox news or it’s from some random guy in Romania writing?
I have no idea. I don’t know, but like, if you can’t tell me the context of where it was published and. So here again, that’s a one, it’s an interface issue in how news is presented, because that context of what is the publisher who signed off on this as a story is really, really important. And I think we meet, you know, in an ideal world, we should, we need to get back to having some sense of where did muse originate.
And I think. Yeah, for a lot of the Postmates is the problems that we have with fake video, whether it’s like highly technical, deep fakes, where it’s just people splicing stuff together in ways to make it seem like people said things, they hadn’t said it’s really, you’re a technological fix is always going to be a few steps behind the ability to make things like that.
But so what you need is at least the easy opportunity to decide what gatekeepers you mean. For that. And then it gets back to those issues with, you know what I was saying about like data portraits, because it’s, what’s true of getting your news from, uh, a big news source when it’s world news is also true about how you understand words in a conversation.
If you don’t know who said it to a large extent, you, you can’t tell if it’s sarcastic, if it’s, um, if it’s well-meaning. Something that was said from deep knowledge or off the top of someone’s head, the source of any kind of words is a really, really important part of them. And that divorce between source and content online is I think the cause of a lot of the problems we have with, um, both bad behavior and poor information.
Alexa Raad: Whenever I hear you talk about, you know, source and context. And if, I think earlier you were talking about history to me that, that, that becomes the question of reputation, right? Because reputation is a way it’s a shortcut for us to, to decide whether we’re going to provide faith and value in what we’re seeing.
So
Leslie Daigle: how do you,
Alexa Raad: how do you mean we use interface design in a way to make it very clear? For folks to be able to tell reputation adequately or at least read reputation adequately. Um, you just talked about your, your partner being able to really distinguish between various news stories that come into the feed.
So that’s one example. Another example is, you know, on Twitter, Having, uh, having the, uh, the Twitter handle, you know, tell you how many followers, maybe the number of followers isn’t necessarily a good indication of the quality of reputation. So what would you, how would you redesign or what kind of design fixes would you propose to them?
Um,
Judith Donath: again, it means prayer. I think the, the number and the types of followers are interesting, but okay. Either let’s look at the followers piece. So if you were going to do like a, a Twitter Richard of some kind, you might want to not just say how many followers somebody had, but have some indication of what those folks, whereas where of what are the kinds of patterns in them.
So that if someone has anything. Here. I think that, you know, in this ideal, beautifully designed world, you might, there might be different things you could subscribe to for the portraits you make. So you might think this is a useful way and this isn’t, but once among the useful things could be some sense of how many followers do those followers have, because that’s some very simple troll things.
You can make a whole farm of sort of bots that whose purpose is to judge. Add more numbers to people, smaller accounts, but that’s something that you could see the difference between, you know, 10,000 organic followers versus 10,000 fake followers. So, but it wouldn’t have to be labeled. This is true or fake.
It’s just that the organic. Set would have a much greater range in what type of followers they had, where at least for now the less organic followers set is going to be much. There’s gonna be a lot less diversity in it. Um, in terms of their number of followers, where they say things about, you know, it could be what, um, and, and again, I think a lot of it is just seeing.
What are the types of words. So this sort of word cloud ESC piece, what are the types of words that characterize what they’re saying? And I just want to also make a distinction between reputation and history, because a lot of the reason I keep talking about history is that if I’m visualizing history, it’s saying, okay, there is certainly a subjectivity in the choice of things I choose to visualize the way I choose to do it.
But the data I’m working with is the data that this person has generated. So the words they’ve said, or the people they’ve followed or who has followed them reputation is a lot, is I think a very interesting, but it is to some level, if, just to keep the word, to describe a different phenomenon, it’s how someone is seen or what their reputation is within a particular community.
So. For instance, if we look at the like sort of Washington post versus Fox news thing, I would be much more comfortable with things like, especially with large interfaces, like faces. Not necessarily saying this is a good source of news, and this is a bad source of news because depending on the community within that community, they have one Fox news has a very good reputation in particular communities.
And personally, I am not comfortable with platforms like Facebook making the decision. I may think that it’s completely ridiculous, but I’m not comfortable with them making them. Decision, what I’m more comfortable with is ways of representing what, for instance, what type of information has come out over there, or how often have things been proved to be fake in particular or false in particular spaces, but having that platform provided visualization and information being much more objective.
Then the designation of what’s good or bad, and that that’s something that should come organically within particular communities, but they should have more information to be working with.
Alexa Raad: So, uh, so I have a follow-up question. This is again, going to do the distinction that you made about history and reputation.
So take the example of my Twitter. I hardly tweet. I have not made any kind of effort, uh, nor do I really care to build a Twitter following the kinds of stuff I tweet once in a while are truly legit.
Leslie Daigle: Um, I believe
Alexa Raad: I am a legit person with some fairly decent viewpoints. However. If you apply the heuristic rules of thumb in terms of design, right?
I would fail most of them. I don’t have a lot of followers. Uh, sometimes I tweet, I retweet a lot of texts, sequences, for example, tweets, I don’t have, um, I don’t tweet a lot of number of different things. I don’t tweet about my, you know, dog or, uh, the politics. So it’s not that I have a variety of things.
And yet I am a very legitimate source. I think of opinion. At least if not news. So how would interface design deal with somebody like me introvert? Yeah.
Judith Donath: I mean, that’s the piece, it’s like what your, the things you tweet about your, your
Leslie Daigle: Israeli SPARTS. So,
Judith Donath: but. But you’re saying the words in general, the content is good.
So something that would just say, okay, I’m seeing what this content is. These are the topics you could get. You know, he did like a workload. You’d see what the topics were. So I know if I really want to read about dogs, you’re not the place to be going. Um, it’s, you know, it’s, it’s not going to look like the most exciting account to follow.
If I read something that you then posted an item. Yeah, I would see probably that like something that showed like, well, this is fairly sparse, but it’s a sort of an announcement and sort of solid pieces like that. Um, it wouldn’t necessarily be bad. It just is not going to, you know, it’s not going to really stand up.
It’s like this totally exciting piece. Um, it wouldn’t give you any red flags is a big part of it. And it’s also another reason to say. That whole issue with like, say pseudonymity, et cetera. It doesn’t mean that that’s better than being tied to your real world itself. To a large extent you’re saying here that you have other reasons for being online, your, your Twitter pans, mainly about giving people information to go re look at this podcast.
And so it really doesn’t make sense for it not to be tied to who you are in real life. It’s, it’s all about that. So it doesn’t necessarily make it. Look bad. It would just give people a little bit more immediate sense of what type of thing is.
Leslie Daigle: The interesting thing there is that there is, there are online services now to help you detect whether, you know, a Twitter account is real or as a bot, you know, bot or not, I think is one of them.
And I recall testing the tech sequences, Twitter account against it, and it thought the text cleanses Twitter account was a bot, which I take exception to because I crashed every single one of those posts by hand. So, um,
Judith Donath: right. Well, and that’s partly why I think that. Um, even that bot or not judgment, I think is one that it’s maybe not that interesting to make because at a certain level of, if an account is mainly about pointing out other interesting information and it’s in a context such as Twitter, like where I’m maybe going to look for information.
I mean, at what point, like if you had a, if you had software. Most of those things for you, would it make it less interesting that I then got to see that this post was up or not like bot or not is not necessarily the thing you want to know? And it also, there’s a huge amount of work that goes into making bots that are not.
Just that are very hard to distinguish from people. And, you know, you’ve, you know, people have been doing this with the load enterprise for decades, you know, who make certain mistakes, you behave in certain quirky ways. It will seem like it’s human. I don’t know how bot or not what their ground truth is for whether they’re right or not.
And so that may, so having, just having the information that lets you see is this interest. Is this falling particular patterns of things that I like maybe a lot better than having something else, just giving you that. Yes or no answer. We have a, a student, Aaron, my student had Erin Zimmerman and I wrote a paper on this.
Um, what’s it called? Well, it was from like 2007. It was called is Britney Spears span. And, um, because it was looking at those spam detectors that would say this thing is spam, this account isn’t and. The argument was that, that really, wasn’t the interesting question that people, that, what types of things, people were looking for a lot more multi-dimensional and that, yes, there are certain times where you want that and that a lot of people’s online presence, maybe a little bit more mixed, like they may be using timers to do things.
So they may have a certain set of things they do automatically in a certain set of things they do by themselves. Sometimes that matters, but it doesn’t always. And that you do rather have something that’s more multi-dimensional in how it understands. What gives you the information to form your own set of types.
Leslie Daigle: Right. And, and part of the challenge there, I think is that if we were left with our own devices to only follow the stuff that we think is interesting, um, then, then we wind up sort of in a closed world syndrome as well. And, and given that, you know, research after research has shown that fake news. It’s so much more likely to go viral because it catches people’s interest in some way than real news or real facts, and is more compelling in some way then than that in ways that real news can never touch.
So what can we, what can we say about forming communities around, you know, fake news versus real news or, or, you know, does this actually get us out of the mess of faith?
Judith Donath: It’s a step in that direction. The problem you’re talking about about fake news, being more interesting is much bigger than this and that’s yeah.
And that’s a big sort of systemic issue with how we get information, why we care about. And I think, um, I mean, this is a lot of the topic of the book I’m working on now, but I think at some level, a part of it is almost inevitable. Given the role of use in a world where we’re dealing with communities that are way beyond what any person is able to affect.
It was a really interesting article in the New York times, a few years ago, about farmers in the Midwest who were complete climate change deniers from a political standpoint, like they thought it was wrong. They thought was something that like a bunch of stupid Democrats had thought up, but. When they were talking about their farming practices, they were taking climate change into account because they actually needed to do that because they denied the change in what was happening with their day to day, year to year, whether their crops would fail so they could hold those two ideas simultaneously.
And I think a lot of our, the issue that we’re having with fake news is that when people are dealing with. Information that is immediately actionable to them. They care a lot about whether it’s real or not. So if you talk to people like in, uh, in their job and they want to know if like the branch that they’re working in is going to be closed down or, you know, what are the things you have?
Do they really care? Like what information is right. And who is grumpy who’s in and who’s out. Or if there’s a lot of decisions they have to make, if it’s immediate to their community. But part of the, I think part of the problem that we have. And this isn’t the whole piece, is that the line between news and information, especially when you’re dealing at levels that you think you not don’t really have any effect on you is that your people are often looking for things other than what is true.
And I think it’s, yeah, this gets into the basic question. One of the big issues I’m writing about in this book is that the assumption that what people are looking for is true. Where reality is a somewhat naive assumption and that a lot of what they are looking for are markers of affiliation. They’re looking for things that are entertaining.
And so news that there’s the affiliative side of news. That was a lot of what we’re seeing within the Trump administration is that if you. If I say the sky is blue and you look up and you say, yes, it is. We agree, but it’s not like a particularly bonding thing because it’s just true. Right. Advice say, you know, the sky is purple with pink polka dots, and you look up at that same blue cloud with white clouds sky say, yeah, that is, you know, it’s got those pink polka dots there.
I see them. Then we actually have a bonding thing because we’re, I’m basically saying. You know, we agree. One of us is agreeing to follow the other and to take their statements and to take voice LT as the key. Piece of in determining what you’ll say is true or not. And that’s a very different thing than true.
Yeah. It taps
Alexa Raad: so much into our own human instincts to be tribal, to always like form under various banners, whether it’s football, uh, affiliations. Right. Um, or whether it’s, you know, interest, affiliations or political. Um, I read one of the really good advice. If I see you gave to news organizations, I’d like you to reiterate that on the show, you talked about how journalists sometimes and news organizations actually perpetuate some of this fake news and the way that it’s a design issue in a way, right.
The way that they handle it. Talk about that, please. How should we do not do it?
Judith Donath: One big piece is not to repeat things in a not true. So, and I think that’s been said at this point by a number of observers is that you don’t want, like, you don’t want to amplify stuff. That’s fake. So, and again, I think I wrote that piece at the beginning of the Trump administration and the whole.
Yeah, my inauguration was bigger. Like more people with my inauguration than the ones you could look at the photograph and see that there were many more of you was like, yes, this is the biggest inauguration. You don’t want to repeat things that are not true without surrounding them with this constant default false statement that, um, so that’s one he’s and the, the other is you want to.
Put enough context that it’s not, that it’s very easy to get very outraged about things. And so to the extent that you can put it in a context of why this is true or why this is not true with this background of understanding why you should care is a really important piece, because if you don’t understand why caring about this has consequences, like what are the consequences of being wrong about that?
Is I think a piece that helps focus people’s interest in whether it was true or not. If you don’t feel that there’s any consequence to not caring about the truth, then it’s much easier. Not.
Leslie Daigle: So much to think about and so much ground covered. And I feel that we could probably talk all day. Uh, it’s just fascinating to see all the pieces come together and, you know, I would like, I would like to see social media interfaces designed the way you describe, but, um, maybe, maybe when I’m retired and rich ha um, Great.
It’s been been a fantastic conversation. Thank you so much for joining us
Judith Donath: today. Well, thank you so much for inviting me. This was really enjoyable. Your questions were fabulous. Thank you.
On Jan 11th, 2021, Parler, the alt right social media platform, found itself turned off and homeless, as Internet giants such as Amazon, Apple and Google, all booted it from their platforms in little more than 24 hours. The decision to eject or turn down Parler was greeted with both relief and alarm, as well as renewed debate. Yet for others the decision reignited debate about free speech, its boundaries, and whether it is government or big tech that ultimately controls it.
In an increasingly digital society, where speech is increasingly manifested in bit and bytes, does it mean that having free speech must then include the infrastructure needed to support it? And if so, then who has the power to generate or stifle free speech?
Leading us through this discussion is Russ White, who began working with computers in the mid 1980s and computer networks in the 1990s. He is a well-known routing and internet transport expert who has coauthored more than 40 software patents participated in the development of several internet standards and internet governance and help develop professional certifications.
Guest: Russ White
Hosted by: Alexa Raad & Leslie Daigle
Related resources
- https://www.buzzfeednews.com/article/johnpaczkowski/amazon-parler-aws
- https://www.bbc.com/news/technology-55615214
- https://www.cnn.com/2021/01/10/tech/what-is-parler/index.html
- https://gizmodo.com/parler-users-breached-deep-inside-u-s-capitol-building-1846042905
- https://www.nytimes.com/2020/11/11/technology/parler-rumble-newsmax.html
- https://qz.com/1956380/amazons-parler-ban-displays-big-techs-power-over-online-speech/
- https://www.thepublicdiscourse.com/2021/04/75039/
- https://www.thepublicdiscourse.com/2021/04/75188/
Transcript (auto-generated by Descript) Alexa Raad: On January 11th, 2021, Parler, the alt-right social media platform found itself, turned off and homeless as internet giants, such as Amazon, apple, and Google all booted it from the platforms in little more than 24 hours. The impetus for the decision.
Wasn’t the events leading up to and including on January 6th, when the social media platform was reportedly used by many of the capital insurrectionists to communicate, coordinate, and plan the attack on the nation’s capital. These tactical Haemus argued that the small, but by then notorious social media platform had not done enough to moderate or police content that was posted on it and was thus in violation of their various terms of service jettisoning parlor off the apple and Google platforms meant that the app would no longer be available for download at the tech giants respective app stores.
In the last month of 2020 parlor had grown exponentially. Its growth was driven by the Exodus of millions of AltRight and Trump supporters from Facebook and Twitter, where he, as these two companies started to crack down on posts, deemed to spread this information and our insight violence before it went offline in January, 2021 parlor claimed about 15 million total.
On January 9th, apple listed parlor as the number one free app for iPhones. And by Monday morning, it was gone. The decision to eject or turn down parlor was greeted with both relief alarm, as well as renewed debate for those sobered up by the events of January 6th, the decision was welcomed. Yet for others, the decision reignited debate about free speech it’s boundaries and whether it is government or big tech that ultimately controls it in an increasingly digital society where speech is increasingly manifested and bits and bytes.
Does it mean that having free speech must then include the infrastructure needed to support it? And if so, then who has the power to generate or stifle free speech?
Leslie Daigle: Our guest for this episode is Russ white rest began working with computers in the mid 1980s and computer networks. In 1990. He is a well-known routing and internet transport expert who has coauthored more than 40 software patents participated in the development of several internet standards and internet governance and help develop professional certifications.
Russ is a co-host of the history of networking and the hedge podcasts, and is an active member of the IET. His most recent works are computer networking problems and solutions network dis-aggregation fundamentals, video training, and abstraction, and computer networks. Video training. Welcome again, right.
Russ White: Well, I thank you. I’m not really sure who you’re talking about when you
Alexa Raad: time passes
Leslie Daigle: and things just sort of follow along don’t
Russ White: they, they do. As long as you stay engaged, things happen and you end up with a resume that’s longer than you can fit on one page and then it’s just gets embarrassing.
Leslie Daigle: But, but so much fun to talk about.
So let’s dive in. So tell us what is the infrastructure of free speech and why can’t free speech exist independently of infrastructure.
Russ White: So there are two kinds of views about free speech in the world. One of which is that. You know, you have this ability to speak. And as long as you have the ability to speak, even if no one can hear you, then it’s perfectly fine to say that you have free speech.
So you might like in this to, uh, you know, a room sitting someplace in a bar or a crowded venue, and there is a wooden box sitting in the corner and anybody can get up there and stand up there and say whatever they want to say, but no one listens because it’s loud and you know, whatever else, uh, well, that’s not really necessarily.
Free speech within the U S United context of the United States and the American founding kind of means it really means as much about being able to build the infrastructure where people hear you. Now, they may not listen still because they might be busy or whatever, but it’s, you have to be able to build the box that you want to stand on.
You have to be able to be in the venue and build the amplifier. T, uh, you know, whatever it is you’re going to build, uh, the classic example would be a printing press, right? Cause that’s what we had at the founding of America was just printing presses. And when the stamp act came about, I’ll go into jumping a little history here, tobacco.
A bit of what I’m saying when the stamp came about the colonists considered this to be totally horrible for free speech, not just because of monetary perspective, but because of free speech, because what was going on was, is in order to publish a newspaper, you had to have a stamp piece of paper. Well, the government stamp the paper.
So if the government didn’t want you to speak, they just didn’t stamp your paper and you couldn’t speak now, theoretically, they still had free speech. You can still stand on a corner and yell and scream, whatever you want to. Yet the stamp act was considered a push against free speech because you could no longer print newspapers.
So it just didn’t include the free speech itself. It included the means in which you use to speak
Leslie Daigle: at point of clarification there. Was that stem applied to each individual article or to each individual issue of the newspaper or the newspaper as a licensing, the newspaper as a
Alexa Raad: whole,
Russ White: each piece of paper on which the newspaper was printed.
Wow. So, and property transfers and pamphlets and books, and anything you wanted to publish. If you were going to publish, you had to have the stamp on each sheet of paper to show that you had paid tax on that sheet of paper. Taxed to the
Alexa Raad: British government, right?
Russ White: Yes. Tax tax to the British government.
Right. So there were two cries. I get about that. The first was that it was taxation without representation, which is the one everyone remembers. The other one was that this would quell the newspapers that were opposed to British rule in America. And so that was the free speech argument that they were printing pamphlets and stuff to try to convince people that America needed to be independent of Britain.
That’s really not the argument they were making at the time. That’s where it went eventually, but that wasn’t the argument they were making at the time. But, um, so the concern was, well, if the British government doesn’t like what you’re going to say, then they just won’t give you the tax to pay. And you just can’t say it.
Well, you’re
Leslie Daigle: already making me feel better about where we are today with the internet, because we’re at least not there yet, I
Alexa Raad: think. But
Leslie Daigle: I understand that you believe that the free speech promise of section two 30 us legislation is not working and it’s time for everyone to insist on reform. So what do you think we need to do?
Russ White: So I’ll start here. I think that the problem with two section two 30 to me is that we have bifurcated the world into publishers and into platforms. And I’m not convinced that that bifurcation works any longer. Um, I just don’t know that that, that split between publisher and platform even make sense, because a platform is supposed to be like a printing press.
Anybody can go to print, whatever they want to and put it out there. The publishers, the person who’s writing it. And so what section two 30 says is if you’re a platform you’re not liable for what publishers are publishing through your platform, the question is, is Facebook really? Either a platinum. Or is it a publisher?
And that’s the argument we get mired in. And I would say that they’re neither, they’re a third thing and we just don’t have regulations for them. And that’s how they’ve gotten large. These social media companies have gotten large and stuff is because they go to court in this situation and they say, oh, no, no, no.
Platform, therefore you can’t argue with me about what other people are publishing on me. And in other places they go to court and they say, no, no, no, I’m a publisher because I’m a publisher. I have the right to filter content. Okay. Got it. But one of, one of those two, like is true or there needs to be a third thing that describes what these companies do.
Right. We’re kind of playing both sides of the game at this time.
Alexa Raad: So, what do you think that third thing is? How would we describe folks like Google and Facebook and Twitter?
Russ White: Yeah. I don’t know that I have a good name for it. Unfortunately
Alexa Raad: we are, um, in the attention economy, right? So attention is a currency and one of the.
The issues with parlor getting so much attention was how amplified that these messages became. Right. So does that point to the fact that there are perhaps three separate things we should think about? One is the free speech itself, the equivalent being that person in the corner of the bar on a soap box with a town crier.
The second is the infrastructure on which they do it. I go on Facebook. I have very few followers, whatever I say, doesn’t really carry that much amplification or attention Trump gets on and he is on Twitter and the amplification is much more massive. So. The problem seems to be in the amplification itself of some of these messages, that that was what the worry was with parlor.
Um, so how do we deal with
Russ White: that? My opinion on that would be. There are that the social media network itself does some of that amplification. And we don’t understand that process from the outside because it’s a black box to us. And then our only amplify, they also filter. So one of the examples I’ve used in the past is that we often, you know, we have this thing there’s even a, uh, an infinite money, infinite monkey transfer protocol.
That’s an RFC that is. That’s out there, uh, where you have the infinite monkeys typing on an infinite number of typewriters and will they produce the works of Shakespeare? And the answer is not what everybody expects to people who say yes or no, but the answer really is, it depends on whether you have a Shakespeare looking filter in front of what the monkeys are typing.
It’s the filter that matters when you get that much random information thrown at you. It’s the filter that matters. And that’s where I think these social media companies have a lot of power. They can shut down message. They can bring message up. And we don’t understand that process from the outside. So they’re neither publishers because they’re not really creating the content.
And they’re not really platforms because they’re doing more than just providing a printing press. They’re actually filtering the content.
Alexa Raad: Are they broadcasters?
Russ White: Yeah.
Leslie Daigle: It’s more than that, right? Because newspapers, friends. Post any, you know, any, they have control over what articles do and do not appear on their pages.
And, you know, depending on what era of newspapers you’re talking about, they do create them themselves, but they might accept them from elsewhere. But I think part of what you’re getting at RAICES, it’s not just that they filter newspapers can filter what goes into the pages and they have a distribution, a circulation list.
So they know sort of how wide their distribution is, but that’s pretty much where it stops. Right? I mean, you might. You might get your story picked up and carried in another, you know, newspaper of, of similar distribution, right? It might get talked about on radio, but it doesn’t have that same kind of amplification that liking something and making a story go viral as they do on a social media platform has, and that’s a level of.
At that I think the difference is that that’s a level of broadcast and circulation. That’s actually under the control of the notional consumer of the platform.
Russ White: Yeah. Yeah. I mean, that’s, that’s, I think that’s a very valid point as well, to some degree it’s under the control of the consumer of the platform, but it’s not entirely because.
The social media network can decide where things go on your timeline, they can decide
Leslie Daigle: the infamous algorithm.
Russ White: Right? Exactly. Or they may decide you just, even though they. You think you care about it? They don’t think you should see it, or they don’t think you should care.
Leslie Daigle: All, all fair points, but, but it is still the case that, that is, that is, uh, an aspect of amplification that doesn’t exist in the newspaper
Alexa Raad: exists,
Russ White: correct?
Yeah.
Alexa Raad: Another aspect of amplification, which is not necessarily in the control of the direct control of publishers platforms or thing ex uh, of Twitter and Facebook. Is these bots, increasingly the amplifications are being done by bots, not humans. So in the real context of, let’s say a disinformation campaign, there were probably less humans than there are bots who are spreading this message.
So given that these, you know, Facebook or Twitter really don’t have, they’re not generating bots necessarily, then who’s responsible.
Russ White: Right. And so that is the problem. And I think that’s legally why we need to spread out and have a third thing so that we can think through the issues of that third thing,
Alexa Raad: third classification per se.
Russ White: Right. Exactly. Because I don’t think we understand who should be responsible in these cases. And I think that rather than me saying, oh, I think it should be so-and-so. My my perspective is we don’t actually know the answer that we need to have a debate over that. We need to talk about that and understand it.
And right now we don’t even understand it. We don’t even understand it all because all we’re doing is pushing these companies into publisher versus platform. And we’re not thinking about this third thing that they actually are. Um, so one thing I suggested at one point in an article I wrote is that you could force the companies in order to say that, uh, you know, you’re going to operate in this space.
One thing you need to do is you need to have an open version of your filtering process. And say a researcher can go throw 10,000 tweets in the tweet deck, and it’s never published anywhere. It’s not a matter of ever being published. It’s a matter of then Twitter has to come back and say, that’s a one, that’s a two.
And you might be able to say, well, I need to describe the audience that I’m sending these tweets to, to see whether that’s a one or a 10 with a 10 being we’re going to actively pursue this and try to make it viral. And a one being we’re going to shadow ban this it’s going to be disappeared and you don’t have to know why those decisions are made from the outside as a researcher.
You used to have to know. There’s actually that stuff going on and the more you can quantify it, and the more you can understand it, the more we can have a discussion around what should these companies be allowed to do. And legally who’s responsible for some of this stuff. Right. But we don’t have this information right now.
We’re completely blind to the way these things work.
Leslie Daigle: So it’s, it’s, who’s responsible and it’s also what should they, or should they not be allowed to do? Right. Right. Um, yeah. Right. Not knowing what they’re doing or what the influences are. It’s kind of hard to come up with a list of things that are good behavior versus
Alexa Raad: bad.
Right?
Russ White: Exactly. And then there’s the other question of, is it okay to have an alt-right platform and then all left platform? Is that okay? Are we okay with that as a culture or are we more like, we need to have one platform where everybody talks right. I don’t know the answer to that. I mean, my, my, my inclination is to say, it’s okay to have both, let people build what they want to build and have their own discussions.
Um, it’s not much different than having the filter bubble. We already know. And so, you know, maybe that’s okay. Um, and you just have different, everybody knows that this network is this way and that network is that way. Um, maybe that’s the better solution. Um, on the other side, maybe we should have local governments encouraging people to use geographic networks that would bring the geography together.
I think one of the reasons we have problems right now with the splitting that it’s doing in our culture, like the radical divide between people is that we all live in filter bubbles. And they’re based on what we believe, which it’s the self-reinforcing thing that’s going on. And before when we had newspapers, yeah.
You had a democratic newspaper in Raleigh. You also had a Republican one. And if you subscribe to both, you would get the regional news from both perspectives. You could understand, we don’t get that anymore.
Leslie Daigle: Yeah. And that’s, that’s kind of where I wanted to go, which is, you know, you asked whether it’s okay to have an alt-right and, and an alt left or whatever, you know, entirely, um, segregating self segregating groups.
Um, but then where do you go for truth? Right. And, and I, I realized that. I really probably should edit that word right out of this podcast. Um, but where do you, where do you go for, for, for perspectives that are broader than your own perspective? Let me put it that way. I mean, you’re right. Traditionally we’ve sort of gone to newspapers for that and to a certain extent we still can, but what, you know, bubbles are okay, as long as it’s possible to exit them or, or understand that there isn’t outside of the bubble.
And I also understand. We don’t know where the edge of our bubble is these
Russ White: days. Right. Right. And I think that’s a, that’s actually a, more of a human and a philosophical problem. Right. I mean, a cultural issue. Are we actually teaching people to seek out, um, I’ve this is culturally I’ve noticed we have a much stronger attachment to keeping an open mind than we do to learning.
And I’m not sure that’s a good thing. I’m not convinced that, you know, keeping it up in mind is great. But my stepfather was used to say, just don’t have it so open that your brain falls out. But I do, you know, I do worry that we are a little bit, we have gotten ourselves to the point where we’re more concerned about winning arguments or about whatever it is than we are about learning.
Leslie Daigle: And that’s, that’s only. You know, reinforced by the way, social media platforms work and the way amplification works, not just in social media platforms, but in any context where you’re only hearing people that agree with you and you get that amplification, then, then there. Ever less room for any other perspective to, to, to creep in.
And I mean, we live my opinion. We live in a world of such luxury at this point that, um, in, in this particular part of the world that we can dream up just about any, any imaginary world and think that we’re living in it. Right. I mean, um, to the point where somebody on Facebook. Your friend who lives by the sea is actually from Barbados, was pointing out that, you know, how many helium balloon carcasses, he picks up on the beach every morning.
Right? And it’s an astonishing number. And these things are terribly dangerous in the sea because they look like jellyfish to creatures that eat them. And then that die because they’re clogged with, you know, helium balloons. But, but the whole point of this particular story is, you know, people. Helium balloons are fun and people like to release them and let them go.
And they’re gone. Right? There’s there’s your bubble. It’s gone. It just disappears. It’s like, no, no, they don’t disappear. They come down somewhere. Just not somewhere in your bubble.
Alexa Raad: Some consequences. Yeah. I want to go back to the argument that, that you brought up, which is whether we should have this all right.
And left. And in the previous world, perhaps, and they, what is no longer there, the free market, Adam Smith’s free market would basically dictate that, you know, it, things work themselves out, people gravitate and, and, you know, eventually we don’t have just one thing or another, maybe we have the best of all solutions.
However, again, I think that ignores the fact that. We not only have, it’s not just humans that are in the mix. It’s also, uh, bots that amplify, but it’s also nefarious actors who have an interest it’s no longer just the algorithm. It’s nation states who have an interest in manipulation. And part of that manipulation is using technology that is not necessarily human technology that is evolving very sophisticated bots now, deep fakes, and God knows what else.
To manipulate. And so I’m not sure whether these, these Mo I don’t have an answer to this in other words, but I don’t know. I think it’s much more complex.
Russ White: Well, I’ll throw in one more level of complexity. And that is, that is that I don’t know how many, how many people actually understand how social media networks work.
And I don’t just mean, I don’t just mean. Facebook and Twitter. I mean, Google search and Amazon are in my mind forms of social media networks as well, but they are driven by attention. As you said, at the very beginning, we live in an attention driven economy and. They are going to do whatever it takes to get you to be online and engaged as much as possible and as often as possible.
And that’s part of the reason that these attend these bubbles form is because it’s not necessarily that they’re trying to do a bad thing. You know, it’s, it’s that they’re trying to get people to engage so they can get advertisers. And so they can get people to buy and. But to get your engagement, what they want to get your attention and engage you.
They need to give you content. That’s going to get you emotionally. Well. Yeah. Yeah. Because know, and, and I’ll tell you fear sells better than anything else. And then sex sells. That’s the second best thing. It’s fear and sex. Those are the two things that are going to sell. And therefore they’re going to send you the most atrocious.
Stuff, but sexually and fear wise to get you to get emotionally wound up because the more emotionally wound up you are, the more you’ll be engaged. The more you’ll use the platform, the more you’ll buy, the more you click through on ads. So it’s not just bots and stuff. It’s I think it’s actually the structure of what Zuboff would call surveillance capitalism, right?
Yes.
Alexa Raad: Yeah.
Leslie Daigle: So I don’t know what that says about me that I get Kevin.
Alexa Raad: They’re not fearsome,
Russ White: but yes. Maybe people know you don’t like to get wound up wisely. That must be it. That must be it. Yeah. But I mean, I just noticed that even in the ads that I see now, I don’t have a Facebook account, but even in other places, I noticed it in the ads. In email and everything’s apocalyptic, everything is everything.
Is, this is the last hope. This is the last chance. And I don’t know. I think we, I think we have become so attuned to fear and so driven by fear and sex that we just it’s primal.
Leslie Daigle: So I think that that actually comes back in and you answer your own question about, is it okay to have, you know, an alt-right and an alt left?
And the answer is probably no for the very simple reason that none of them is going to say pure to the original. You know, original drive, they’re going to get owned intentionally or inadvertently by marketing or disinformation campaigns, or just, you know, spun out of control on, on, on overdrive, in, in dunes scrolled
Alexa Raad: memes.
Russ White: Right? Right. Well, I don’t think you see anything different with Facebook today. I just think that we don’t talk to each other on Facebook, even though we have the common space. Okay, exactly. The common spaces is like, there’s like this ditch on one side and ditch on the other side. And everybody lives in those ditches and they climb to the top of the hill to yell at each other.
And that’s pretty much all we do, you know? So I don’t know that.
Leslie Daigle: Yeah, I think that’s, I think that’s largely true, but, but I think that’s a, it’s an end. Right? And it’s like, you don’t want to have separate, completely disparate platforms and we need to address the problem of actually, you know, engaging in discussion with people.
Can
Alexa Raad: we go to the issue of free speech? So what parlor did after, uh, they got kicked off Amazon, AWS. They basically went and launched a lawsuit, arguing that Amazon’s decision had really disenfranchised them because they really did not have any, Amazon was so big that it was really difficult for them to go anywhere else.
And of course they didn’t win. So that lawsuit, you could look at it and say, well, they were arguing that Amazon was effectively controlling free speech because by kicking them off, AWS, Amazon was shutting free speech down and their defeat could potentially argue that. Well, no, uh, Amazon has 30% of the cost.
Market so they could build their own infrastructure. They could go somewhere else. So the question is who actually controls it. If infrastructure of free speech is actually attached to the concept of free speech, then who controls it?
Russ White: Yeah. So I think Amazon’s case is a little bit different because Amazon is not Facebook.
Amazon is not, they’re just literally a hosting company. And I think in some sense they’re closer to a printing press or a platform than Facebook is. And in this case, you know, if Amazon didn’t like what, what parlor was doing, they should have found some other way to me of taking care of that thing, kicking them off the servers.
Um, Now there’s another piece of this that people don’t think about is competition is not just about whether they, whether or not parlor could get someplace else, because obviously they did they’re back up and running. Um, I don’t know what their user count is now or anything like that, but they are back up and running now, but it’s also, there is a time value to information.
And we often forget this, that if Facebook blocks somebody accidentally for 24 hours, You know, if they just happen to block a political candidate for, for the 24 hours before voting takes place and they do it. Oh, sorry. That was the algorithm. That’s really difficult to swallow from my perspective, because you know, there’s time value in that information.
And it’s not just that they’re up or down, it’s that you took them down for X period of time, which costs them X number of users right. Or wrong, you know, that’s an issue that we don’t really deal with a whole lot. So, I mean, I don’t know. I, you know, I understand about parlor when I kind of understand Amazon’s point of view, user service, things like that.
But then you go back to the question of, if you undercut the infrastructure, what does free speech really mean? Like w where do you go from here? If, anytime you set up a service like parlor or on the other side, you know, it could be all left as well as all right. You can undercut them by just taking away their infrastructure.
I mean, where do we go from there? How do we make that work? And again, I think that’s, we don’t delineate very well Dewey. I mean, we’re treating Amazon like a publisher. When they cut down parlor rather than a platform, is that I don’t know, is that valid or the rules or things just squishy enough right now that we really don’t know what to do and companies don’t know what to do.
Do we need better definitions?
Leslie Daigle: And I think part of the problem was the perspective that it wasn’t just about speech at that point. It was about inciting action. Right. And, and I mean, that does take it up a whole different level of. Is this permissible. Um, so I think, I think we can’t fail to, we can’t fail to acknowledge that even as, yes, we don’t want, we certainly don’t want to wind up in a position where like some countries in the world alternative viewpoints are simply involuntarily snuffed out.
Yes. Not
Russ White: so much. Okay. Well the last pro independence newspaper was shut down yesterday in Hong Kong. That’s right. That’s right. As an
Alexa Raad: example, I read me right after this capital six, uh, uh, January 6th by its, there was a really good article on free speech. And the line that I remember most was that free speech is not about just free speech.
It’s not about freedom from the consequences of your. I think that’s the point you were making
Leslie Daigle: Leslie. Yeah. Much, much nicer articulation of it. And so given that and moving away from the should, should they have been, you know, should they have been ripped off of AWS? What, what would be the right way to deal with those consequences and, and, you know, attempt to prevent that sort of behavior being fomented?
I mean, the ladder is a little hard to answer because. Yeah, tracking behavior on social media platforms is impossible and, and even, even dicier than free speech, but what would be the right way to pursue consequences in that
Russ White: context? So what would you do if it were a newspaper? That’s my question, right?
Would you have the FBI going in destroy the presses and most of the time the answer would be no, you would stop the distribution of the material in question. And you would tell the newspaper owner, you are going to be taken to jail or whatever it’s going to be for allowing that on, you know, to be printed.
It wouldn’t be, oh, we’ll just send somebody in to destroy the person. I don’t know of any instance where that would be considered a valid response. And so I think that if you just take it out of the virtual world and put it in the physical world, a lot of times these questions kind of, you can start seeing that.
Yeah. You know, maybe we just don’t have the right tools in place or something like that.
Leslie Daigle: Except in this case, parlor didn’t own the printing presses. They were renting them.
Russ White: Right, but even, so would you say it’s okay. Let’s say you convert Amazon into a physical printing company and somebody renting their press for two hours a day to print something.
You don’t agree with it. But
Alexa Raad: I will put it. That’s actually fair comparison because in your own view, you’re saying that we can’t really think of them as publishers. We can’t think of them as platforms, but when you compare them and bring the comparison you just brought, like, will you go to a, to a newspaper and destroy their printing presses?
It’s really not an apples to apples comparison. Your point is we don’t know what this beast is. That’s right.
Russ White: And so, yeah. So in some cases it may be the right thing for Facebook to kick somebody off. Right. I don’t know. Um, is that threshold transparent? Is it something anybody knows is going to happen before it happens or is it random and is that fair?
Is it fair to allow a private company to say no, you’re kicked off without it. Um, outside legal, like legislative things saying, no, they can be kicked off for this. And not for that.
Leslie Daigle: I don’t know. But I do know that there’s a lot of stores I can’t go into even now that wearing a mask. So it’s not, I mean, I think, I think that I do think that companies have some right to say what they are willing to accept, um, in, in, in, on their premises and that, but the, the, the pivot point here is, is the question of, is Facebook so much.
The single platform that everybody has a right to say what they want on it, uh, or, or is it, um, or is it a private company?
Russ White: Right. And Facebook, by the way, has argued in court that people who tell them that they can’t filter are pushing are, um, are damaging. Facebook’s free speech, right? Hmm. So that’s another entire, that’s another entire line of argument that then, you know?
Yeah. So now, so now, so now this is why you say things like, well, maybe it’s okay to have all right. Not left, but maybe on the other hand to counter balance that you need to have local governments having their own services that are apolitical without nothing political is allowed. It’s just locally then.
And where people can talk to each other or something. I don’t know. You know, maybe there’s a way for governments to try to balance the bubbles that are driven, or maybe there are ways of taking the profit out of building the bubble.
Alexa Raad: Ah, well, they tried that with broadcasting. Uh, there was, uh, I forget the folks who were much better at this than me, but there was a rule where almost that the nightly news was supposed to be.
It just providing you with the information and the facts. And I think it was during Reagan’s era that they changed the rules and advertising became so much more prominent, which then has had this long tail consequences, this domino effect of now. You know, these, uh, outlets are owned by money-making media conglomerates.
And the whole premise of the business model is based around advertising and my moneymaking and in an attention economy it’s sometimes becomes toxic.
Russ White: Right? Exactly. So that’s, that’s the question. Can you find ways to make it where these companies aren’t making 80% profit off of for grabbing your attention?
I don’t know. Right. Yeah. But until we define it and try to understand it better, we’re just stuck in a, we’re just stuck in an argument of whether they’re platforms or whether they’re publishers. Yeah.
Alexa Raad: But
Leslie Daigle: I think it’s a really good, it’s a really good opening question of, you know, is it Fisher, is it foul or is there yet another different type of beast and, and, and how do these pieces fit together so that we can better understand.
You know, where we think responsibility lies and what, what does good look like in terms of behavior? Um, so clearly, clearly we don’t have the answers, but it was a fascinating discussion to start exploring more of the problem. So thank you very much for, for joining us today, Russ.
Russ White: Thanks. Um, it’s always great coming on with you guys and you know, you should invite me more often, you know, it’s perfectly
Alexa Raad: fine.
Thank you so much for us. Always great to talk.
Russ White: Yup. Great talking to you.
Machine Learning, or ML, is attributed as a natural outgrowth of the intersection of Computer Science and Statistics. Machine Learning builds on both and focuses on the question of how to get computers to program themselves from an initial structure and then experience. Yet merely thinking about Machine Learning in such stark outlines misses the question of how we can build systems that are attentive to social welfare, create new economic markets and solve large scale problems that enhance human life.
Our guest in this episode is Professor Michael I. Jordan, who is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences, and he is considered the world’s foremost authority on ML.
Guest: Michael I. Jordan
Hosted by: Alexa Raad & Leslie Daigle
Related resources * https://datainnovation.org/2020/08/5-qs-for-dr-michael-i-jordan-professor-at-the-university-of-california-berkeley/ * https://spectrum.ieee.org/the-institute/ieee-member-news/stop-calling-everything-ai-machinelearning-pioneer-says * https://hdsr.mitpress.mit.edu/volume1issue1 * https://hdsr.mitpress.mit.edu/pub/wot7mkc1/release/8
Transcript (auto-generated by Descript) Alexa Raad: The term artificial intelligence was coined in 1957. That year an AI proof of concept, a program called logic theorist was developed by Alan Newell, cliff Shaw and Herbert Simon, and presented at the Dartmouth summer research project on artificial intelligence, hosted by John McCarthy and Marvin Minsky, Minsky and McCarthy both later recognized as fathers of AI described artificial intelligence.
As any tasks performed by a machine that would have previously been considered to require human intelligence so far though, no software or entity comprised of software and hardware has in fact, exhibited human level intelligence and cognition. Most of what is labeled AI today is in fact, a subsection of AI called machine learning machine learning or ML is attributed as a natural outgrowth of the intersection of computer science and statistics.
If the defining question of computer sciences is a given problem solvable. And if so, how do we build a machine to solve it? And if the defining question of statistics is what can we deduce or infer from a set of data and a set of modeling assumptions and with what reliability then machine learning builds on both and focuses on the question of how to get computers, to program themselves from an initial structure and then experience.
And incorporate additional computational architectures and algorithms that can better capture store index, retrieve, and merge these data and how to orchestrate learning sub-tasks into a larger system. Yet, even this is a very simplistic definition. Merely thinking about machine learning in such stark outlines, misses the question of how we can build systems so they can deliver positive results while avoiding unintended negative consequence.
Perhaps we ought to think about machine learning as a new discipline and engineering one that incorporates fields as varied as economics, humanities, and social sciences. Maybe then with this new construct in mind, we can develop machine learning systems that are attentive to social welfare, create new economic markets and solve large scale problems that enhance human life.
Leslie Daigle: Michael Jordan is the P Hong Chan distinguished professor in the department of electrical engineering and computer science and the department of statistics at the university of California, Berkeley. He received his master’s in mathematics from Arizona state university and earned his PhD in cognitive science in 1985 from the university of California, San Diego.
He was a professor at MIT from 1988 to 99. Professor Jordan is a member of the national academy of sciences, a member of the national academy of engineering and a member of the American academy of arts and sciences. He is a fellow of the American association for the advancement of science. He has been named a name and lecturer and a medallion lecture by the Institute of mathematical statistics.
Professor Jordan was a plenary lecturer at the international Congress of mathematicians in 2018. He received the. Render prize from the American mathematical society in 2021. The I triple E John Von Neumann medal in 2020. The HCI research excellence award in 2016. The David D Broomall heart prize in 2015 and the ACM atrium triple AI Allen Newell award in 2009.
He is a fellow of the triple AI ACM, ASA CSS. I Tripoli IMSD, ISB and Siam. Welcome.
Michael I. Jordan: Thank you.
Leslie Daigle: So let’s start with some definitions. First, in addition to what Alexa said in the intro, what is machine learning? What is AI, why are they confused with when other so often
Michael I. Jordan: I’ll be happy to try to give you some definitions, but I must say I don’t really like definitions and I don’t think we tend to operate with definitions in mind.
I think there are intellectual trends that reflect our era. And I like to think of our era as lasting, like a century. That’s kind of the intellectual era we’re living in. And the last century has been full of new developments. You know, information sciences, broadly speaking computer science the growth of statistics economic science arose in this year.
And this had huge implications for human beings. We’re now living with these kinds of ideas in our midst and, you know, control systems of all sorts. And also the growth of a whole, a whole branch of information technology is kind of the, the phenomenon, perhaps the most striking change in our last say 10 or 20 years is that.
The the, the, the magnitude of it, the growth and scale of data, the availability of data in all areas of human inquiry and discourse, sciences technology and the granularity of it. There’s now data about, you know, each individual human there’s data about each individual gene in the genome about each region of the sky.
And that’s qualitatively different than previous eras, even though some of the ideas and thinking were the same the scale of it and the specificity of it, the scope of the inquiry has now shifted. But I think of our era as that and the implications for systems, indeed, I really liked the introduction.
It’s less about a specific computer or how an entity is that’s intelligent behaves, and it’s more about the overall system that we’re all part of, which may be planetary and scale. The system that you know, had that underlies finance the system underlies a worldwide commerce. Transportation.
These systems have computers and data and flows and people you know, involved in helping with decision-making. And I think that’s the right scale and scope to think about in our era and the, and the trends behind that are, are definitely not just AI of the notion that you’d put intelligence and thought into a computer.
It’s operations research, it’s control theory, it’s economics it’s certainly statistics and, and comp computing kind of as an infrastructure line behind all of that. And it’s also the human experience of living with all of this, you know, being in the midst of computing. And developing an understanding of that developing legal structures to think about it.
And so that all that big phenomenon is certainly not subsumed within the field of AI AI, I still think of as an intellectual aspiration, it’s almost a philosophical one. It’s thinking about what would it be like if a computer could really think like us, you know, reason plan and so on. And I still think it’s an interesting.
Absolutely fascinating philosophical aspiration. It just hasn’t happened yet. It’s not even clear what the glimmer is. That’s going to sort of start to make it happen. But in the meantime, computers can do things that are, you know striking and in some sense, intelligent, I mean, a computer could compute the digits of PI.
I can’t do that. So is the computer more intelligent than me? Well, for some narrow kind of tasks, for sure. And that’s what we want. It can calculate things that we could never calculate. But that doesn’t somehow mean that we’ve been surpassed and intelligence. It just means we have kind of a new tool in the universe.
And so, so machine learning itself is a terminology that I’m only partially I’m a little more fond of, as the introduction alluded to, it’s kind of a merger of statistical or inferential thinking with computing. But the, the seeds of that have been present for a couple hundred years ago, gals the original statistician, if you will, or one of the original one.
Who was doing astronomy with the kind of methods we use today and doing it on, you know, with computing of the day would recognize what we’re doing now. You know, so it it’s conceptually, not all that different, but,
Leslie Daigle: but yet I get the feeling that, that part of what you’re saying is that a difference in as a friend of mine used to like to say a difference in number, it can mean a difference in kind, I mean, while Yohanas coupler would, would recognize that this is the study of where the stars are in the sky.
What’s available by way of data. And therefore the inferences that can be drawn are really a whole other world.
Michael I. Jordan: Something qualitatively has changed. It’s not clear though, that conceptually really we’re using gradient descent methods and we’re using large amounts of data to find patterns. And again, our, our forebearers you know, the gals of the world would recognize all of that.
But I think this thing I tried to get at about the specificity, that it’s granular data. So it used to be that, you know, if you’re a physicist, you’d study data, You know, the motion of objects, you know, and there’s, the laws ethic was Emay that would characterize all objects. So that was the goal of, of science.
And now you have motions of particular kinds of objects and particular kinds of situations, and you want to have a specific laws and then it’s even way more true about human beings. You know, the idea that you would have a single law for all humans seems a little kind of outdated, almost. But there was not data to do anything different.
Now there’s data about humans in all kinds of situations, including the commercial ones and social ones of all kinds. And you can start to actually do a little science at that level of something more specific. So I’d argue, that’s actually, what’s changed that it’s qualitatively different in that it’s now specific and granular and that’s kind of the kind of inference.
And if you’re a business, of course, you’re trying to do personalization. That’s one of the core ideas of beauty. And that reflects the fact that I’m not going to build one business for everybody. I’m going to a different from different people. I make more money doing that. So that that’s also, if you will qualitatively different, but the, but it definitely do not think of AI has Kevin come up with great ideas or machine learning that were qualitatively you know, breakthroughs.
I don’t think that’s happened and that’s changed everything. It’s rather the cumulation of scale and scope as you’re talking about together with some pretty good ideas, have unleashed some things. And I do believe in a hundred years, we’ll look back and there will have been some qualitative conceptual breakthroughs.
I just don’t know what they are.
Alexa Raad: Speaking of scale, you yourself have pointed out that. Advances in machine learning have powered innovative products and services from companies like a Google, Netflix, Amazon, you know, and others, you could argue that machine learning is fundamental to their business model.
As you just said, you know, there’s so much data and also so much of it is just so granular, but has ha have those advantages now, given an unfair advantage to these. Big companies, the big tech, because they consume so much time.
Michael I. Jordan: Yeah, that’s a great question. I, I, my answer is going to be no maybe slightly surprising way.
First of all, I really want to distinguish between a an Amazon business model or maybe Alibaba in China to make this a little more international. And the Google business model and Facebook, their business model fundamentally is about advertising. That’s how they make the rules. Right. And that goes back to the, you know, you went to television arose, it needed to be free.
You didn’t want to be able to pay for it. So you had to think of a way to monetize it. You did that with advertising. So Google, Alibaba, you’re talking about Google and not Alibaba. So Google and Facebook, it’s wise a little bit more. Free services and you’ve got, gotta make money somehow. And so surprisingly, you can kind of corner the market on advertising.
So in that sense, it’s unfair. If your advertisers out there dominated, it’s unfair to them, but I, I guess I don’t care so much, but you know, Amazon and then Alibaba in China, they bring packages to people’s door. Right. And that’s a whole different kind of business model, a whole different kind of service.
And what about whether it’s more healthy to human beings? You know, you could argue, I mean, bringing search boxes information is useful to people. But when you’re really bringing packages to people’s door it’s, you’re doing somebody economics. You’ve got something that people are willing to pay for a little bit.
Right. And you’re now in the kind of the world of providing a real business model and, and all the things that come with that you create markets, you create links between producers and consumers, data flows on the basis of that. You create computing infrastructure to support all of that data flows on the basis of that.
And I think of that as a much more natural kind of business model. And to scale that and to make it viable and successful and safe and useful, you’ve got to do all kinds of machine learning or the hood. So particular, you’ve got to model a fraud. You know, people are putting their credit cards into an e-commerce site.
You know, you got to make sure that, you know, what’s fraud and what’s not, you gotta model supply chains because if you’re serving a billion products to a hundreds of loads of people, which is in fact what these companies are doing, you got to know where all the products are in the supply chain at all moments.
And that’s now unbelievably scalable. You know, relative to what companies used to do. So you know, those companies had to gather all that data and make all those models and build all those infrastructures to be able to do e-commerce at that scale. Now, do they have an unfair, unfair advantage for e-commerce?
Well, no, they have an advantage, but it’s fair. They, they built it, they did it, and they were bringing value to people. Does that also give them an unfair advantage for things like you know, natural language processing or things that like academics might want to work on another domains because just have all this data.
Yeah. I don’t, I don’t, I don’t believe so. If I want to build a system that does, you know, medical predictions or. Or does something, you know, some other business model fit to do with, you know, social interactions among people or whatever. I got to collect that data myself and, you know, and maybe the company has some had started, but rarely is their data granular enough for the phenomenon I care about in my little business while they’re on my little pieces.
So actually I think it’s actually quite surprising that, that the companies don’t have data for for a lot of the new ideas or science ideas or, or business models that you know, there’s plenty of room and niche for smaller companies to merge, but also for academics to do all kinds of research.
Yeah. And, and I don’t think that has changed. In fact, what Google has started to do at some point is they have so much language, data. They provided things like, you know, a natural language service, if you will, that does translation and they make it free and they advertise against it. So it’s not even yet a business model.
But, but now that becomes a commodity. If I’m doing natural and it’s, I can build on top of that and then do more. And so it’s no longer a natural advantage for them in terms of any kind of business. And
Leslie Daigle: it strikes me that a lot of the tension in the space around AI and I use that term intentionally.
And, and these questions is the sense that somehow there’s somebody getting ahead ahead of where we are for some value of wheat. So whether it’s an, an unfair advantage, because some, you know, there’s an irreducible, large, granular set of data, or whether it’s, you know, I don’t understand. What, this is how his analysis is done.
You know, whether it’s computing, the digits of PI or which we do know how to do, but you know, there’s the sense of if I can’t touch it, smell it, feel it, or build it myself then. Is it somehow, is that a problem? And that’s okay. I think that’s where we’re seeing some of the tension around fear of AI. It has been expressed by a number of.
Leading figures in industry and maybe goes a little bit to the, to the heart of, is it an unfair advantage?
Michael I. Jordan: There’s too much packed in there. I’m going to disagree with about half of that. You know, the fear of AI that expressed by something, because initially I think you mean like Elon Musk or whatever, and no one in AI believes that stuff just to, you know, and Alon, you know, the genius of our time and so on, but doesn’t know much about it.
And, and, and thinks that it’s thought at computers and we should interface to the brain and has all these crazy ideas about it. Fun, crazy stuff, but science fiction for hundreds of years. So let’s discount that, you know, fear of AI, what we have fear of, you know, individual humans have fear of all kinds of things, including vaccines, including, you know, I think all kinds of things out of, out of our control.
Right. And so I, again, we could talk about that, but I don’t think that’s really what the fear is. Fear of monopoly. You know, that’s not a fear. That’s part of, you know, economic systems that we have to have regulations of government. We have to have discourse about that, but let’s as nothing to do with the Elon Musk’s fear of AI you know, do some companies have advantages because of discoveries in AI that they kind of hold and then they can exploit.
Right. And, and I’m going to argue very much. No, because AI is still mostly done at the I’m using the term AI again, just to kind of help out with the discussion. But the development of the algorithms and the infrastructure and all that, it’s all done completely openly. All the work is mostly done still in academia.
And some of it’s done in some of the big labs. It’s all in the archive within a day. It’s all out there. There are no papers held back. Right. And it’s all pretty simple stuff. You know, this is not super advanced you know, mathematics, it’s pretty easy stuff. And you go all around the world, which I do, you know, and, and the, the 20 five-year-olds there, I’ve read all those books.
And they know it just as well as any researcher inside of Google, I can guarantee you. Right. So what else does Google have? But they have large numbers of computers, so they can do some kind of show off things like AlphaGo or something, which others may be can’t do as easily. But I can get tons of computers just by paying Amazon a little bit and using the cloud.
Right. It’s not, you know, and that’s true in many countries. So I really D currently, I mean, this may change, but there, there are not such, there are no, no, there are no conceptual advantages held by the companies. And then there might be market position advantage, but that’s classical, you know, standard oil had that.
And then it had to be, as we think about an economic. Yeah, I
Leslie Daigle: think my point was that not that these were necessarily valid positions and what you’ve described as really how they aren’t, how there isn’t an intelligence, that’s, that’s scary and, and, and worthy of fear, but that this is where people’s fears come from.
So people who are not in AI, who are not in machine learning, who don’t understand pattern recognition from, you know, today we’re a hundred years ago. It, it does. Potentially stand up a situation where you don’t recognize it. So therefore it’s fearful, it’s something worthy of fear. So the
Michael I. Jordan: policy is, is there’s real.
And I have it too. And like I alluded to it, I think it’s real and meaningful. And, and and certainly I would not claim that women should not have fear of the future and fear of technology. Moreover another element in what you were getting at that I do agree with is transparency issues. So if I go to the bank and money, get alone and the bank says, no, I don’t want, they just have them point to an algorithm and say, the algorithm is that’s, that’s not something, I don’t think that’s about fear.
That’s about the legal system. And that’s about our recourse as human beings and about what we expect and naturally, and should have. And if that’s not, if the algorithm is not doing that well, the album is messed up and that’s a, that’s an engineering and a technical problem to work on. And I personally believe there’s a bit of a bit too much optimism on by a lot of the AI colleagues saying AI just solves all these problems.
It can do better predictions than anybody. Therefore let’s just use it. It’ll only help humanity. I think that’s just dead wrong because humanity needs transparency. They need to understand the decisions they need to have. You know, it all embedded in the legal system and embedded in a social contract and compact.
I think it’s going to take decades to kind of make machine learning and algorithms emerge well with our social compact. I don’t think it’s, I think it’s, it is a development of a whole new branch of engineering to do that. And I don’t like the naive Tay that somehow AI just solves the problems magically.
It is a complicated, useful tool. And you’ve got to include it as a tool and think about it very, very deeply. And I don’t think there’s maybe been a branch of engineering that ever emerged that was quite as complex as this chemical engineering is one I tend to refer to, you know, the idea of doing chemicals at scale was complicated and it was going to have a huge impact that people were excited.
Right. But it didn’t have humans quite as much in the mix as this. So this is
Alexa Raad: complicated. So speaking of branches of engineering in reading your bio and doing some research about you, it struck me that you are the Michael Jordan of ML and I, you know, pun intended. You are the foremost authority in the world on ML you have in your writings, you’ve said that We have a major challenge in our hands, bringing together computers and humans in a way that can enhance the human life.
And you’ve argued that this really is a new branch of engineering that builds on various other branches and disciplines like statistics like social sciences, economics, humanities, et cetera. And you’ve argued that this should be taught as a new way, a new engineering concept in schools. Can you talk a little bit about why, you know, statistics and computing and data science all makes sense?
Why your disciplines like economics and humanities sort of in the, in the mix and the consideration and what kinds of conceptual flaws. Would we be subject to, if we ignored the importance of say economics or psychology?
Michael I. Jordan: Okay. Yeah. Thanks. That’s a fantastic question. That’s the one I hope will kind of continue to be asked for the coming decades because that really gets at the heart of this.
You know first of all the word engineering is kind of been deprecated or, or it’s, it’s it’s a crude, a certain bad smell. If you say. Social science. That sounds great. You know, if you say social engineering, that sounds terrible. You say genome science, that sounds wonderful. Genome engineering.
It has even like mathematics, like it’s, you know, mathematics stands all by itself, but the people talk about the mathematical sciences. That sounds great. Bioengineering engineering doesn’t sound any good. I could keep on going so now. Okay. Maybe there’s something to that, but you know, Think about how much engineering has done for human beings, you know, arguably more than anything in the sciences, like civil engineering gave us our bridges and our buildings and, you know, and, and KIPP was a chemical engineering you know, gave us all the materials.
We have included all the drugs and medicines that we have and and electrical engineering, you know, it’s just, you know, obviously. In terms of human, happiness you know, engineering has arguably contributed more than anything else. Right? So, so how come the term has gotten w I think it’s partly just because there’s these externalities, these other things that happen that people didn’t think about it.
And so that’s why I’m very eager. I think we are developing engineering here. It’s real world systems that you deploy with compute. And they do things in the real world, like, you know, healthcare and, you know, transportation and financial kind of things. And they are supposed to bring value to human beings.
They’re supposed to work and they’re supposed to be robust and, and all that. There’s scientific understanding behind it, just like there was for chemical engineering and electronic currency, but it’s really about the system it’s really about working and doing good things for good for people. And so we’ve got to think through all the implications that was it really going to work, is it really going to have good plays?
And that those are, that’s an engineering style of thinking. And so I want to bring in younger students and, and, and teach them that they can think through these things themselves. And they often, some of them want to just do pure science and write down, you know, laws, but some of them want to change things now.
They want to build better structures, you know, for healthcare, for social justice, for whatever they want, they have their own problems and they got to think them through in an engineering kind of way. Yeah. Right. And so part of when you have humans in the mix engineering kind of way has to have economics and humanities in it, because it’s not just about data, it’s about values.
It’s about what you want, what does humans want, but what does group would have groups of humans want? How do we make sure that people are protected? That’s really
Alexa Raad: critical. I mean, this is, this is the argument that’s really come into into discussion lately from a number of different angles. And I was very interested when I read that this is what you are advocating.
And this is sort of the why you’re asking, not just the, what, you know, what can we make, how can we make it, but sort of the why to what end and what are the values that underlie it.
Michael I. Jordan: That’s right. I, and the, those I don’t have the values that I want to impose on the systems nor do I think Zuckerberg does, or, you know, Right.
The Biden, I think it has to emerge from down below. People should be able to express their values and have them be recognized and then brought in and respected as part of the systems. And, you know, to a large extent, that’s what economists try to do. Right. And of course their field is still in development.
And I think it’s going to change greatly in the next a hundred years. It’s going to have to be a whole new kind of economics that has data. You know, these more granular level interactions and so on. I think a lot of them recognize that. But that, that person, so I got interested in this partly just by thinking about congestion and, you know, if machine learning systems are doing decision making, like I trying to help you to figure out the fastest path to the airport, you know, it’s a dumb, obvious thing that a machine learning system can help you do.
It’s got lots of data, you know, it finds that. And if it’s just working for me, then it’s going to do a great job. But if it’s working for everybody, It may send everybody down the same road. Cause it looks like the fastest route. Now we’ve created congestion and it’s no longer the fastest road. All right.
So what do you do at that point? Well, if you’re kind of a classical it person like Facebook kind of style, I think you would try to say, well, we know a lot about these humans. We know their browsing history. We’ve collected lots of data about them. We know who wants to go on what road now, this is an exaggeration, but that’s kind of the spirit.
Is that we how we personalize, we know that you Alexa want to go down that road because we just know you were pretty well. And we know that Leslie, you want to go down that road and we figured out how to balance the traffic and make sure it all flows. Right. And I hope you kind of see the ridiculous statistics.
The smarter thing to do would be to create an economy, a market where you, Alexa, kind of told her that moment in some way that there’s possible congestion, that if you want to save your money, you could go on a slightly slower route and, you know, save the money for some other day. And you, Leslie may be in a big hurry today and you may want to spend your extra money.
When I say money. I mean, you know, some currency. And when I say, you know, decide, I mean, there might be a little, you know, agent working on your behalf to try to help this whole kind of, you know,
Alexa Raad: really for self-driving cars. This is a, this is a real issue for self-driving cars.
Michael I. Jordan: Yeah, well, it’s, it is certainly about self-driving cars, but it’s just about cars.
It’s about, it’s about moving bodies from one place to another. And, and again, it’s about the values you have, you know, what are you and it’s value. It may, this is a dumb example of value. How much of a hurry? I mean, right.
Alexa Raad: Yeah, but that’s an economic
Michael I. Jordan: value. It’s an economic value. It matters to me, my children, or I’ve got to go to the hospital.
It matters to me a lot in that moment, perhaps. And I need, and I don’t even know that before the moment occurs, so it could have been burned in the data, right. It couldn’t be just a learning system. It’s gotta be thought through in the moment that I’m plumped into a situation. And that’s what economics is about kind of in the market in the moment things occur.
And then you react and the intelligence of the market makes something somehow ideally better. And, and that style of thinking has just not been present in machine learning or certainly AI. And that to me is a, is a huge defect of the whole.
Leslie Daigle: And, and is that related to the notion of needing to be able to bring different systems of machine learning together, to interact with each other?
Because I mean, the self-driving cars and example of there are multiple different specialized systems analyzing a lot of data at same time. How do you bring that together in a coordinated fashion?
Michael I. Jordan: Yeah, it’s partly related to that. I mean, that’s just kind of classical engineering that systems have components and you got to bring them together.
So it all kind of works well, but you know, economics is also about scarcity and conflict. Right. That there’s not enough road space to go down and there’s our preference. And it’s also about building connections between producers and consumers. And so the other kind of thing that helped me to think this through a bit, other than just things like traffic or whatever is, think about domains like music or the arts, or even journalists.
Right there a producer and there’s a consumer, right. And you really want to set up a link between them that the kinds of producers producing for certain set of consumers and the consumers know how to find that producer. And because something valuable is passing between them say a song is being listened to, and it’s a song that someone loves that’s value should be reflected in some sense, economically.
So I like to thinking about like a system that does, doesn’t just stream music to people and then advertises to make money for the people doing the streaming, rather a system that reveals to the artists. Here’s the people that are listening to me and lets me reach out and say, Hey, I’ll come play your wedding.
Or, Hey, do you like this song? And does that at scale and creates a whole market of producer, consumer relationships, using all of our technology, our machine learning and our data and all that. And the platform disappears at that point. It doesn’t have to do advertising. It just makes connections. And some money starts to flow because it’s real value.
Just like I was listening to Amazon earlier, you know, sends packages from people and say, I’ll pay for it. There’s real money there. And then you can just, you know, the, the musician gets most of that money perhaps. And the, the, the platform takes 5% instead of taking all of the money is what’s currently happening.
That starts to say, okay, how can I build a system like that? Yeah. Is it enough just to analyze the data and put it out there, you know, and know the, you know, on the cloud, no, I got to have economics principles. Otherwise this is going to be gained. It’s not going to work, you know, and, and so on. And so I’ve got to go to my economics books or my colleagues and say, how do we build a system that has got, you know, principals are, and they’re all learning from each other.
And as Leslie alluded to, there’s got to now be cooperation. Like, you know, if we’re trying to learn about another example is like recommendations for restaurants, right? I can’t experience all restaurants and decide which ones I prefer. But you experienced, I experienced that and we start to recommend to each other.
And then the restaurants are also trying to connect up and, and build platforms that actually then allow that kind of information to be shared. That’s economics meets machine learning.
Leslie Daigle: That’s really excellent. And, and while we’ve covered a lot of ground, I feel like there’s so much more that we could delve into, but sadly we’re, we’re just about out of time.
So I’ll, I’ll wrap up by asking you what are three key things that you think we need. We, the general populous need to keep in mind. As we face this brave new world with more machine learning.
Michael I. Jordan: Ooh. Okay. That’s a hard, that’s a great hard question. I think that we’re living in a, in a really tough time and we’re all kind of trying to ignore the fact that there’s so much misinformation and there’s so much distrust and it’s really the worst of times in some ways the pandemic sort of amplified it, but in some ways, almost not.
It made us retreat into our homes for awhile. But, but that, that, that fact is serious. And I think computer science has a role. It has a, had a role to play in that. So I I think the general has got to kind of push back on that the, you know, ask technologists to do better. It’s got to be, you know, willing to insist that it not be this way, not just wait for it to happen.
You know, education is still critical. I think that we’ve, you know, we nearly had fascism in the United States in the last two years just to bring up something kind of, and I think there’s two ways that we escape fascism and it’s not over done deal yet. Number one was the rule of law did prevail.
The judges make good decisions. And those judges that often were Republican, you know, judges, but they still had the rule of law and their brains and why? Well, they were educated in the American system, including the universities and their parties. That was one part of it. The other part of it is that we are a diverse country.
And I think that, you know, the the the, the black people in the south, just to really be clear about it really rose up and really said, you know, we’re participating and we have a voice and we’re going to make these things happen. And I think that helped us escape fascism, that they were, they were smart enough to see fascism and realize they didn’t want.
Right. And so culture and, and rule of law is something the United States has done pretty well. And so you know those were endangered. And so I think that people have to focus on those strengths and ask, you know, technology, not just to bring us miracles and, you know, and expect for vast wealth and all that.
But rather than it merges well with culture and it merges well with rule of law, because that’s the way we’re going to continue to build a decent society going. So hopefully it was helpful. They’ll give you three things, but hopefully that was helpful.