Scott argues that even if AGI requires a new paradigm beyond LLMs, we shouldn't expect significant delays, since Lindy's Law suggests major paradigm shifts could occur within 3-5 years, and new paradigms typically emerge precisely when scaling hits limits. Longer summary

Scott addresses the objection that AGI is far off because LLMs need a 'new paradigm' to reach AGI. He traces the evolutionary tree of AI development from neural networks through transformers to modern LLMs, then applies Lindy's Law to show that even paradigm shifts as major as deep learning or transformers should be expected within 3-5 years at the 25th percentile. He argues this timeline is comparable to LLM-only predictions anyway. Scott also makes a subtler point: new paradigms historically emerge when old ones hit scaling limits, meaning they won't cause delays but rather continue progress from where scaling left off. He concludes that extrapolating from current LLM scaling remains the best forecasting method whether or not LLMs themselves reach AGI. Shorter summary

Scott Alexander analyzes EA's approach to criticism, arguing that specific, targeted critiques are more valuable than vague, paradigmatic ones for driving real change. Longer summary

Scott Alexander discusses the phenomenon of Effective Altruism's (EA) seemingly excessive openness to criticism. He argues that while EA appears to welcome criticism, much of it is paradigmatic and vague, leading to little actionable change. He contrasts this with specific, targeted criticisms that actually challenge individuals and practices, which he believes are more likely to lead to real improvements. Scott uses examples from psychiatry and EA to illustrate his point, suggesting that embracing vague criticisms can lead to inefficiency and a move away from precise, effective practices. He concludes by discussing the nature of paradigm shifts, arguing that they arise from specific anomalies rather than broad, ideological critiques. 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 presents highlights from reader comments on his review of Kuhn's 'The Structure of Scientific Revolutions', offering various perspectives and clarifications on Kuhn's ideas about scientific progress. Longer summary

This post highlights comments on Scott Alexander's review of Thomas Kuhn's 'The Structure of Scientific Revolutions'. The comments provide additional context, clarifications, and perspectives on Kuhn's work. They discuss topics such as the nature of paradigm shifts, the relationship between different scientific theories, Kuhn's metaphysics, and the implications of his ideas for the philosophy of science. The post also includes an analogy comparing scientific progress to building skyscrapers, and touches on the debate between Kuhn and Popper's views on falsification in science. Shorter summary

Scott Alexander presents a 'Grand Unified Chart' showing how different domains of knowledge share a similar structure in interpreting the world, arguing this is due to basic brain algorithms and effective epistemology. Longer summary

Scott Alexander draws parallels between different domains of knowledge, showing how they all share a similar structure in interpreting the world. He presents a 'Grand Unified Chart' that compares Philosophy of Science, Bayesian Probability, Psychology, Discourse, Society, and Neuroscience. Each domain is broken down into three components: pre-existing ideas, discrepancies, and actual experiences. Scott argues that this structure is universal because it's built into basic brain algorithms and is the most effective way to do epistemology. He emphasizes that the interaction between facts and theories is bidirectional, and that theory change is a complex process resistant to simple contradictions. Shorter summary

Scott reviews Kuhn's 'Structure of Scientific Revolutions', finding its thesis on paradigm shifts in science intriguing but frustratingly vague, especially beyond physics. Longer summary

Scott reviews Thomas Kuhn's 'The Structure of Scientific Revolutions', which argues that science progresses through paradigm shifts rather than simple accumulation of facts. Kuhn posits that scientific paradigms are comprehensive worldviews that guide research and interpretation of data. When anomalies accumulate, a new paradigm may emerge to replace the old one. Scott finds Kuhn's thesis intriguing but frustratingly vague in parts, especially in applying it beyond physics. He draws connections between Kuhn's ideas and predictive coding in neuroscience, suggesting both describe how pre-existing mental structures shape perception and understanding. Overall, Scott sees value in Kuhn's perspective but wishes for more clarity and examples from other scientific fields. Shorter summary

Scott Alexander examines his evolving view on scientific consensus, realizing it's more reliable and self-correcting than he previously thought. Longer summary

Scott Alexander reflects on his changing perspective towards scientific consensus, sharing personal experiences where he initially believed he was defying consensus but later discovered that the scientific community was often ahead of or aligned with his views. He discusses examples from various fields including the replication crisis, nutrition science, social justice issues, and AI risk. Alexander concludes that scientific consensus, while not perfect, is remarkably effective and trustworthy, often self-correcting within a decade of new evidence emerging. Shorter summary

Scott explores the nature of scientific contrarianism, discussing how ideas spread through the scientific community and the challenges faced by both crackpots and legitimate contrarians. Longer summary

This post discusses the concept of contrarians and crackpots in science, exploring how ideas move through different levels of the scientific community. Scott examines cases like Gary Taubes and the serotonin theory of depression to illustrate how scientific consensus can differ at various levels. He proposes a pyramid model of scientific knowledge dissemination and discusses how contrarians might be skipping levels in this pyramid. The post then contrasts virtuous contrarians with crackpots, noting that the former often face indifference rather than opposition. Scott concludes by discussing paradigm shifts in science and how even correct contrarians often lose credit for their ideas. Shorter summary