Daniel Böttger proposes a new theory of consciousness as recursive reflections of neural oscillations, explaining qualia and suggesting experimental tests. Longer summary

This guest post by Daniel Böttger proposes a new theory of consciousness, describing it as recursive reflections of neural oscillations. The theory posits that qualia arise from the internal processing of information within oscillating neural patterns, which can reflect on themselves. The post explains how this theory accounts for various characteristics of qualia and consciousness, and suggests ways to test the theory using EEG source analysis. Shorter summary

Scott Alexander analyzes the astronomical costs and resources needed for future AI models, sparked by Sam Altman's reported $7 trillion fundraising goal. Longer summary

Scott Alexander discusses Sam Altman's reported plan to raise $7 trillion for AI development. He breaks down the potential costs of future GPT models, explaining how each generation requires exponentially more computing power, energy, and training data. The post explores the challenges of scaling AI, including the need for vast amounts of computing power, energy infrastructure, and training data that may not exist yet. Scott also considers the implications for AI safety and OpenAI's stance on responsible AI development. Shorter summary

Scott Alexander argues that intelligence is a useful, non-Platonic concept, and that this understanding supports the coherence of AI risk concerns. Longer summary

Scott Alexander argues against the claim that AI doomers are 'Platonists' who believe in an objective concept of intelligence. He explains that intelligence, like other concepts, is a bundle of useful correlations that exist in a normal, fuzzy way. Scott demonstrates how intelligence is a useful concept by showing correlations between different cognitive abilities in humans and animals. He then argues that thinking about AI in terms of intelligence has been fruitful, citing the success of approaches that focus on increasing compute and training data. Finally, he explains how this understanding of intelligence is sufficient for the concept of an 'intelligence explosion' to be coherent. Shorter summary

Scott Alexander summarizes and responds to comments on his book review about IRBs, covering various perspectives on research regulation. Longer summary

Scott Alexander summarizes key comments on his book review of 'From Oversight to Overkill' about IRBs (Institutional Review Boards). The post covers various perspectives on IRBs and research regulations, including stories from researchers, comparisons to other industries, discussions on regulation and liability, debates on act vs. omission distinctions, potential applications to AI governance, and other miscellaneous observations. Scott provides additional context and his own thoughts on many of the comments. Shorter summary

Scott Alexander examines optimistic and pessimistic scenarios for AI risk, weighing the potential for intermediate AIs to help solve alignment against the threat of deceptive 'sleeper agent' AIs. Longer summary

Scott Alexander discusses the varying estimates of AI extinction risk among experts and presents his own perspective, balancing optimistic and pessimistic scenarios. He argues that intermediate AIs could help solve alignment problems before a world-killing AI emerges, but also considers the possibility of 'sleeper agent' AIs that pretend to be aligned while waiting for an opportunity to act against human interests. The post explores key assumptions that differentiate optimistic and pessimistic views on AI risk, including AI coherence, cooperation, alignment solvability, superweapon feasibility, and the nature of AI progress. Shorter summary

Scott Alexander analyzes results from a 2022 prediction contest, discussing top performers and methods for improving forecast accuracy. Longer summary

Scott Alexander reviews the results of a 2022 prediction contest where 508 participants assigned probabilities to 71 yes-or-no questions about future events. The post discusses the performance of individual forecasters, aggregation methods, and prediction markets. It highlights the success of superforecasters, the wisdom of crowds, and prediction markets. The article also announces winners, discusses demographic factors in forecasting ability, and introduces a new contest for 2023, emphasizing the potential for improving forecasting accuracy through various methods. Shorter summary

Scott Alexander summarizes a debate between Yudkowsky and Christiano on whether AI progress will be gradual or sudden, exploring their key arguments and implications. Longer summary

This post summarizes a debate between Eliezer Yudkowsky and Paul Christiano on AI takeoff speeds. Christiano argues for a gradual takeoff where AI capabilities increase smoothly, while Yudkowsky predicts a sudden, discontinuous jump to superintelligence. The post explores their key arguments, including historical analogies, the nature of intelligence and recursive self-improvement, and how to measure AI progress. It concludes that while forecasters slightly favor Christiano's view, both scenarios present significant risks that are worth preparing for. Shorter summary

Scott Alexander reviews competing methodologies for predicting AI timelines, focusing on Ajeya Cotra's biological anchors approach and Eliezer Yudkowsky's critique. Longer summary

Scott Alexander reviews Ajeya Cotra's report on AI timelines for Open Philanthropy, which uses biological anchors to estimate when transformative AI might arrive, and Eliezer Yudkowsky's critique of this methodology. The post explains Cotra's approach, Yudkowsky's objections, and various responses, ultimately concluding that while the report may not significantly change existing beliefs, the debate highlights important considerations in AI forecasting. Shorter summary

Scott Alexander responds to comments on his AI risk post, discussing AI self-awareness, narrow vs. general AI, catastrophe probabilities, and research priorities. Longer summary

Scott Alexander responds to various comments on his original post about AI risk. He addresses topics such as the nature of self-awareness in AI, the distinction between narrow and general AI, probabilities of AI-related catastrophes, incentives for misinformation, arguments for AGI timelines, and the relationship between near-term and long-term AI research. Scott uses analogies and metaphors to illustrate complex ideas about AI development and potential risks. Shorter summary

Scott Alexander presents a monthly collection of 42 diverse links covering topics from science, politics, culture, and technology, highlighting interesting findings and current events. Longer summary

This post is a collection of 42 diverse links and topics, ranging from scientific studies to political news and cultural observations. Scott Alexander curates these links monthly, covering a wide array of subjects including zoology, sociology, economics, psychology, technology, and more. The links often highlight interesting or surprising findings, such as studies on cult demographics, obesity research controversies, and the effects of testosterone on belief in minority positions. Scott also includes humorous content, political commentary, and discussions on current events and social trends. Shorter summary

The post examines the relationship between neuron count and intelligence across species, challenging traditional brain size measures and exploring implications for AI development. Longer summary

This post discusses the relationship between brain size, neuron count, and intelligence across different species. It challenges traditional measures like absolute brain size and encephalization quotient, focusing instead on the number of cortical neurons as a key factor in intelligence. The post highlights birds as an example, explaining how their dense neuron packing allows them to achieve primate-level intelligence with much smaller brains. The author then explores the implications of this for understanding intelligence and its potential impact on AI development, suggesting that AI capabilities might scale linearly with computing power. The post ends with a humorous reference to pilot whales, which have more cortical neurons than humans but aren't known for higher intelligence. Shorter summary

A collection of links covering topics from drug regulation and genetic testing to tax reform and AI developments, with Scott's commentary on each item. Longer summary

This is a wide-ranging links post covering various news items and studies. Topics include FDA regulations and drug pricing, genetic testing advances, Russian Facebook ads from the 2016 election, studies on education and intelligence, tax reform effects on graduate students, and various scientific developments. The post moves quickly between topics, providing brief commentary and often linking to additional sources. Several items focus on medical and healthcare issues, from insulin prices to studies on pain medication effectiveness. The post maintains Scott's characteristic mix of intellectual curiosity and light humor throughout. Shorter summary

Scott Alexander explores theories to reconcile contradictory views on AI progress rates, considering the implications for AI development timelines and intelligence scaling. Longer summary

Scott Alexander discusses the apparent contradiction between Eliezer Yudkowsky's argument that AI progress will be rapid once it reaches human level, and Katja Grace's data showing gradual AI improvement across human-level tasks. He explores several theories to reconcile this, including mutational load, purpose-built hardware, varying sub-abilities, and the possibility that human intelligence variation is actually vast compared to other animals. The post ends by considering implications for AI development timelines and potential rapid scaling of intelligence. Shorter summary

Scott analyzes a new survey of AI researchers, showing diverse opinions on AI timelines and risks, with many acknowledging potential dangers but few prioritizing safety research. Longer summary

This post discusses a recent survey of AI researchers about their opinions on AI progress and potential risks. The survey, conducted by Grace et al., shows a wide range of predictions about when human-level AI might be achieved, with significant uncertainty among experts. The post highlights that while many AI researchers acknowledge potential risks from poorly-aligned AI, few consider it among the most important problems in the field. Scott compares these results to a previous survey by Muller and Bostrom, noting some differences in methodology and results. He concludes by expressing encouragement that researchers are taking AI safety arguments seriously, while also pointing out a potential disconnect between acknowledging risks and prioritizing work on them. Shorter summary

Scott argues for the importance of starting AI safety research now, presenting key problems and reasons why early work is crucial. Longer summary

This post argues for the importance of starting AI safety research now, rather than waiting until AI becomes more advanced. Scott presents five key points about AI development and potential risks, then discusses three specific problems in AI safety: wireheading, weird decision theory, and the evil genie effect. He explains why these problems are relevant and can be worked on now, addressing counterarguments about the usefulness of early research. The post concludes by presenting three reasons why we shouldn't delay AI safety work: the treacherous turn, hard takeoff scenarios, and ordinary time constraints given AI progress predictions. Shorter summary