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Grokking “Forecasting TAI With Biological Anchors” – Epoch

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I give a visual explanation of Ajeya Cotra’s draft report, Forecasting TAI with biological anchors, summarising the key assumptions, intuitions, and conclusions. Notes: Thanks to Michael Aird, Ashwin Acharya, and the Epoch team for suggestions and feedback! Special thanks to Jaime Sevilla and Ajeya Cotra for detailed feedback. Click here to skip the summary Ajeya Cotra’s biological anchors framework attempts to forecast the development of Transformative AI (TAI) by treating compute as a key bottleneck to AI progress. This lets us focus on a concrete measure (compute, measured in FLOP) as a proxy for the question “when will TAI be developed?” Given this, we can decompose the question into two main questions: The second question can be tackled by turning to existing trends in three main factors: (1) algorithmic progress e.g. improved algorithmic efficiency, (2) decreasing computation prices e.g. due to hardware improvements, and (3) increased willingness to spend on compute. The first qu

Grokking “Forecasting TAI with biological anchors” | Epoch AI Notes: I give a visual explanation of Ajeya Cotra’s draft report, Forecasting TAI with biological anchors (Cotra, 2020) , summarising the key assumptions, intuitions, and conclusions The diagrams can be found here – you can click on some of the boxes to get linked to the part of the report that you’re interested in 1 Thanks to Michael Aird, Ashwin Acharya, and the Epoch AI team for suggestions and feedback! Special thanks to Jaime Sevilla and Ajeya Cotra for detailed feedback. Executive Summary Click here to skip the summary Ajeya C

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