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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A moral backlash against AI will probably slow down AGI development, published by Geoffrey Miller on May 31, 2023 on The Effective Altruism Forum.Note: This is a submission for the 2023 Open Philanthropy AI Worldviews contest, due May 31, 2023. It addresses Question 1: “What is the probability that AGI is developed by January 1, 2043?”OverviewPeople tend to view harmful things as evil, and treat them as evil, to minimize their spread and impact. If enough people are hurt, betrayed, or outraged by AI applications, or lose their jobs, professional identity, and sense of purpose to AI, and/or become concerned about the existential risks of AI, then an intense public anti-AI backlash is likely to develop. That backlash could become a global, sustained, coordinated movement that morally stigmatizes AI researchers, AI companies, and AI funding sources. If that happens, then AGI is much less likely to develop by the year 2043. Negative public sentiment could be much more powerful in slowing AI than even the most draconian global regulations or formal moratorium, yet it is a neglected factor in most current AI timelines.IntroductionThe likelihood of AGI being developed by 2043 depends on two main factors: (1) how technically difficult it will be for AI researchers to make progress on AGI, and (2) how many resources – in terms of talent, funding, hardware, software, training data, etc. – are available for making that progress. Many experts’ ‘AI timelines’ for predicting AI development assume that AGI likelihood will be dominated by the first factor (technical difficulty), and assume that the second factor (available resources) will continue increasing.In this essay I disagree with that assumption. The resources allocated into AI research, development, and deployment may be much more vulnerable to public outrage and anti-AI hatred than the current AI hype cycle suggests. Specifically, I argue that ongoing AI developments are likely to provoke a moral backlash against AI that will choke off many of the key resources for making further AI progress. This public backlash could deploy the ancient psychology of moral stigmatization against our most advanced information technologies. The backlash is likely to be global, sustained, passionate, and well-organized. It may start with grass-roots concerns among a few expert ‘AI doomers’, and among journalists concerned about narrow AI risks, but it is likely to become better-organized over time as anti-AI activists join together to fight an emerging existential threat to our species.(Note that this question of anti-AI backlash likelihood is largely orthogonal to the issues of whether AGI is possible, and whether AI alignment is possible.)I’m not talking about a violent Butlerian Jihad. In the social media era, violence in the service of a social cause is almost always counter-productive, because it undermines the moral superiority and virtue-signaling strategies of righteous activists. (Indeed, a lot of ‘violence by activists’ turns out to be false flag operations funded by vested interests to discredit the activists that are fighting those vested interests.)Rather, I’m talking about a non-violent anti-AI movement at the social, cultural, political, and economic levels. For such a movement to slow down the development of AGI by 2043 (relative to the current expectations of Open Philanthropy panelists judging this essay competition), it only has to arise sometime in the next 20 years, and to gather enough public, media, political, and/or investor support that it can handicap the AI industry’s progress towards AGI, in ways that have not yet been incorporated into most experts’ AI timelines.An anti-AI backlash could include political, religious, ideological, and ethical objections to AI, sparked by vivid, outrageous, newsworthy fai...

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A compute-based framework for thinking about the future of AI, published by Matthew Barnett on May 31, 2023 on The Effective Altruism Forum.How should we expect AI to unfold over the coming decades? In this article, I explain and defend a compute-based framework for thinking about AI automation. This framework makes the following claims, which I defend throughout the article:The most salient impact of AI will be its ability to automate labor, which is likely to trigger a productivity explosion later this century, greatly altering the course of history.The availability of useful compute is the most important factor that determines progress in AI, a trend which will likely continue into the foreseeable future.AI performance is likely to become relatively predictable on most important, general measures of performance, at least when predicting over short time horizons.While none of these ideas are new, my goal is to provide a single article that articulates and defends the framework as a cohesive whole. In doing so, I present the perspective that Epoch researchers find most illuminating about the future of AI. Using this framework, I will justify a value of 40% for the probability of Transformative AI (TAI) arriving before 2043.SummaryThe post is structured as follows.In part one, I will argue that what matters most is when AI will be able to automate a wide variety of tasks in the economy. The importance of this milestone is substantiated by simple models of the economy that predict AI could greatly accelerate the world economic growth rate, dramatically changing our world.In part two, I will argue that availability of data is less important than compute for explaining progress in AI, and that compute may even play an important role driving algorithmic progress.In part three, I will argue against a commonly held view that AI progress is inherently unpredictable, providing reasons to think that AI capabilities may be anticipated in advance.Finally, in part four, I will conclude by using the framework to build a probability distribution over the date of arrival for transformative AI.Part 1: Widespread automation from AIWhen discussing AI timelines, it is often taken for granted that the relevant milestone is the development of Artificial General Intelligence (AGI), or a software system that can do or learn “everything that a human can do.” However, this definition is vague. For instance, it's unclear whether the system needs to surpass all humans, some upper decile, or the median human.Perhaps more importantly, it’s not immediately obvious why we should care about the arrival of a single software system with certain properties. Plausibly, a set of narrow software programs could drastically change the world before the arrival of any monolithic AGI system (Drexler, 2019). In general, it seems more useful to characterize AI timelines in terms of the impacts AI will have on the world. But, that still leaves open the question of what impacts we should expect AI to have and how we can measure those impacts.As a starting point, it seems that automating labor is likely to be the driving force behind developing AI, providing huge and direct financial incentives for AI companies to develop the technology. The productivity explosion hypothesis says that if AI can automate the majority of important tasks in the economy, then a dramatic economic expansion will follow, increasing the rate of technological, scientific, and economic growth by at least an order of magnitude above its current rate (Davidson, 2021).A productivity explosion is a robust implication of simple models of economic growth models, which helps explain why the topic is so important to study. What's striking is that the productivity explosion thesis appears to follow naturally from some standard assump...

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