Epoch AI: Literature Review of TAI Timelines

Literature review of transformative artificial intelligence timelines | Epoch AI Latest Publications & Commentary Papers & Reports Newsletter Podcast Data & Resources Datasets Overview Benchmarking Models Frontier Data Centers Hardware Companies Chip Sales Polling Resources AI Trends & Statistics Data Insights Projects FrontierMath GATE Playground Distributed Training Model Counts About About Us Our Team Careers Consultations Our Funding Donate Contact Search epoch.ai Search Enter a query to search for results Placeholder Article Literature review of transformative artificial intelligence timelines report Literature review of transformative artificial intelligence timelines We summarize and compare several models and forecasts predicting when transformative AI will be developed. Cite Published Jan 17, 2023 Authors Keith Wynroe, David Atkinson, Jaime Sevilla Previous work: Grokking “Forecasting TAI with biological anchors“ , Grokking “Semi-informative priors over AI timelines” Highlights The review includes quantitative models, including both outside and inside view, and judgment-based forecasts by (teams of) experts. While we do not necessarily endorse their conclusions, the inside-view model the Epoch AI team found most compelling is Ajeya Cotra’s “Forecasting TAI with biological anchors” , the best-rated outside-view model was Tom Davidson’s “Semi-informative priors over AI timelines” , and the best-rated judgment-based forecast was Samotsvety’s AGI Timelines Forecast . The inside-view models we reviewed predicted shorter timelines (e.g. bioanchors has a median of 2052) while the outside-view models predicted longer timelines (e.g. semi-informative priors has a median over 2100). The judgment-based forecasts are skewed towards agreement with the inside-view models, and are often more aggressive (e.g. Samotsvety assigned a median of 2043). Introduction Over the last few years, we have seen many attempts to quantitatively forecast the arrival of transformative and/or general Artificial Intelligence (TAI/AGI) using very different methodologies and assumptions. Keeping track of and assessing these models’ relative strengths can be daunting for a reader not familiar with the field. As such, the purpose of this review is to: Provide a relatively comprehensive source of influential timeline estimates, as well as brief overviews of the methodologies of various models, so readers can make an informed decision over which seem most compelling to them. Provide a concise summarization of each model/forecast distribution over arrival dates. Provide an aggregation of internal Epoch AI subjective weights over these models/forecasts. These weightings do not necessarily reflect team members’ “all-things-considered” timelines, rather they are aimed at providing a sense of our views on the relative trustworthiness of the models. For aggregating internal weights, we split the timelines into “model-based” and “judgment-based” timelines. Model-based timelines are giv

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