Scott explains why Ajeya Cotra's influential 'Biological Anchors' report correctly predicted the AI scaling boom but got AGI timelines wrong by twenty years, due to severely underestimating the rate of algorithmic progress. Longer summary

Scott analyzes why Ajeya Cotra's landmark 2020 'Biological Anchors' report predicted AGI around 2050, when current estimates now center on the late 2020s to 2040s. The report correctly predicted the scaling hypothesis and AI boom, but underestimated one crucial parameter: algorithmic progress was actually 200% per year instead of the predicted 30%. This single error, compounded across exponential growth, threw off the entire timeline by about twenty years. Scott examines various contemporary critiques of the report, finding that most concerns about the methodology were actually non-issues, while one throwaway concern (about algorithmic progress estimates being poorly researched) turned out to be the fatal flaw. He concludes this demonstrates both the power and limitations of probabilistic forecasting. Shorter summary

Scott Alexander grades his 2018 predictions for 2023 and makes new predictions for 2028, with a strong focus on AI developments. Longer summary

Scott Alexander reviews his predictions from 2018 for 2023, grading himself on accuracy across various domains including AI, world affairs, US culture and politics, economics, science/technology, and existential risks. He then offers new predictions for 2028, focusing heavily on AI developments and their potential impacts on society, economics, and politics. Shorter summary

Scott Alexander evaluates his predictions about the Trump presidency, finding he performed about average overall with some notable successes and failures. Longer summary

Scott Alexander reviews and grades his predictions about Donald Trump's presidency, covering topics from Trump's base diversity to the likelihood of a coup. He analyzes his successes and failures, discussing his performance on prediction markets and his overall accuracy compared to average pundits. Scott concludes that he did about average in his predictions, with some notable successes in race-related predictions and on prediction markets, but also made mistakes in overestimating Trump's competence and underestimating his continued support from Republicans. Shorter summary

Scott Alexander explores the possibility of a 'General Factor of Correctness' and its implications for rationality and decision-making across various fields. Longer summary

Scott Alexander discusses the concept of a 'General Factor of Correctness', inspired by Eliezer Yudkowsky's essay on the 'Correct Contrarian Cluster'. He explores whether people who are correct about one controversial topic are more likely to be correct about others, beyond what we'd expect from chance. The post delves into the challenges of identifying such a factor, including separating it from expert consensus agreement, IQ, or education level. Scott examines studies on calibration and prediction accuracy, noting intriguing correlations between calibration skills and certain beliefs. He concludes by emphasizing the importance of this concept to the rationalist project, suggesting that if such a 'correctness skill' exists, cultivating it could be valuable for improving decision-making across various domains. Shorter summary