Scott examines the rise of AI superforecasters that now match or exceed top human forecasters, explores how they work and their current performance, and analyzes implications for decision-making, prediction markets, and the future role of AI opinions. Longer summary

Scott discusses the emergence of AI superforecasters that are now matching or slightly exceeding top human forecasters in prediction accuracy. He describes how these AI systems work (using scaffolding around frontier models like GPT/Claude), demonstrates their use with examples from FutureSearch and Preseen, and analyzes their performance on platforms like Metaculus where they're competing in tournaments against humans. The post explores both near-term implications (AI forecasters being easier to access than human superforecasters, potentially influencing policy and business decisions) and longer-term possibilities (AI forecasters serving as an 'opinion layer' for AI systems, transformation of prediction markets into AI-vs-AI competitions). Scott argues these developments could be genuinely beneficial, giving people access to superhuman forecasting ability just as AI threatens other aspects of society. Shorter summary

Scott reviews the state of prediction markets after explosive growth, finding that most volume is degenerate sports gambling rather than useful forecasting, and proposes both technical solutions and two potential futures for the field. Longer summary

Scott Alexander examines the current state of prediction markets after their recent explosion in popularity, noting that while volume has grown from millions to billions per month, most of it comes from sports betting rather than the epistemic improvement he'd hoped for. He explores several problems: markets aren't asking the most important questions society needs answered; resolution criteria disputes ("rulescucking") create controversy; and there are concerns about insider trading and manipulation. He proposes solutions including a novel approach to conditional markets and suggests two paths forward: either creating user-generated, subjectively-resolved real-money markets (the "Siskind Cube"), or accepting that prediction markets' main value may be as training data for AI forecasters that could make the markets themselves obsolete by late 2026. Shorter summary

Announcement of an AMA session with the AI Futures Project team about AI, forecasting, and alignment. Longer summary

This is a short announcement post for an AMA (Ask Me Anything) session with the AI Futures Project team, where they will be answering questions about AI, forecasting, and alignment for a specific time period. The post includes links to the project's team page, their AI 2027 scenario work, and their blog. Shorter summary

Scott announces his collaboration with AI Futures Project's blog and their upcoming AMA, highlighting recent posts including one about AI time horizons that was validated by new OpenAI data. Longer summary

Scott Alexander announces he will be shifting most of his AI blogging to the AI Futures Project blog, where he has already co-written several posts. He highlights three recent posts, particularly one about AI time horizons that was validated by new OpenAI data showing faster horizon growth than previously estimated. He also announces an upcoming AMA with the AI Futures Project team on ACX. Shorter summary

Scott introduces a new AI forecasting project predicting rapid AI development and potential superintelligence by 2028, led by Daniel Kokotajlo, whose previous 2021 predictions proved remarkably accurate. Longer summary

Scott Alexander introduces a new AI forecasting project led by Daniel Kokotajlo and a team of experts, which predicts rapid AI developments leading to superintelligence by 2028. The post begins by noting how accurate Kokotajlo's 2021 predictions were, then presents the team's forecast which includes an intelligence explosion in 2027, government involvement in AI companies, and potential scenarios ranging from misaligned AI to technofeudalism. Scott notes that while team members have varying timelines, they consider this an 80th percentile fast scenario that shouldn't be ruled out. Shorter summary

Scott examines a new AI forecaster, discusses Polymarket's success, and reviews recent developments in prediction markets and forecasting. Longer summary

This post discusses recent developments in AI forecasting and prediction markets. It starts by examining FiveThirtyNine, a new AI forecaster claiming to be superintelligent, but finds its performance questionable. The post then briefly mentions r/MarkMyWords, a subreddit for bold predictions. It goes on to discuss Polymarket's recent success, particularly in betting on the 2024 US presidential election. The post concludes with a roundup of interesting prediction markets and forecasting-related news, including political betting controversies in the UK and updates on the Kalshi vs. CFTC legal battle. 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 examines GPT-3's capabilities, improvements over GPT-2, and potential implications for AI development through scaling. Longer summary

Scott Alexander discusses GPT-3, a large language model developed by OpenAI. He compares its capabilities to its predecessor GPT-2, noting improvements in text generation and basic arithmetic. The post explores the implications of GPT-3's performance, discussing scaling laws in neural networks and potential future developments. Scott ponders whether continued scaling of such models could lead to more advanced AI capabilities, while also considering the limitations and uncertainties surrounding this approach. 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