EA - What a compute-centric framework says about AI takeoff speeds - draft report by Tom Davidson

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What a compute-centric framework says about AI takeoff speeds - draft report, published by Tom Davidson on January 23, 2023 on The Effective Altruism Forum.I’ve written a draft report on AI takeoff speeds, the question of how quickly AI capabilities might improve as we approach and surpass human-level AI. Will human-level AI be a bolt from the blue, or will we have AI that is nearly as capable many years earlier?Most of the analysis is from the perspective of a compute-centric framework, inspired by that used in the Bio Anchors report, in which AI capabilities increase continuously with more training compute and work to develop better AI algorithms.This post doesn’t summarise the report. Instead I want to explain some of the high-level takeaways from the research which I think apply even if you don’t buy the compute-centric framework.The frameworkh/t Dan Kokotajlo for writing most of this sectionThis report accompanies and explains (h/t Epoch for building this!), a user-friendly quantitative model of AGI timelines and takeoff, which you can go play around with right now. (By AGI I mean “AI that can readily[1] perform 100% of cognitive tasks” as well as a human professional; AGI could be many AI systems working together, or one unified system.)Takeoff simulation with Tom’s best-guess value for each parameter.The framework was inspired by and builds upon the previous “Bio Anchors” report. The “core” of the Bio Anchors report was a three-factor model for forecasting AGI timelines:Dan’s visual representation of Bio Anchors reportCompute to train AGI using 2020 algorithms. The first and most subjective factor is a probability distribution over training requirements (measured in FLOP) given today’s ideas. It allows for some probability to be placed in the “no amount would be enough” bucket.The probability distribution is shown by the coloured blocks on the y-axis in the above figure.Algorithmic progress. The second factor is the rate at which new ideas come along, lowering AGI training requirements. Bio Anchors models this as a steady exponential decline.It’s shown by the falling yellow lines.Bigger training runs. The third factor is the rate at which FLOP used on training runs increases, as a result of better hardware and more $ spending. Bio Anchors assumes that hardware improves at a steady exponential rate.The FLOP used on the biggest training run is shown by the rising purple lines.Once there’s been enough algorithmic progress, and training runs are big enough, we can train AGI. (How much is enough? That depends on the first factor!)This draft report builds a more detailed model inspired by the above. It contains many minor changes and two major ones.The first major change is that algorithmic and hardware progress are no longer assumed to have steady exponential growth. Instead, I use standard semi-endogenous growth models from the economics literature to forecast how the two factors will grow in response to hardware and software R&D spending, and forecast that spending will grow over time. The upshot is that spending accelerates as AGI draws near, driving faster algorithmic (“software”) and hardware progress.The key dynamics represented in the model. “Software” refers to the quality of algorithms for training AI.The second major change is that I model the effects of AI systems automating economic tasks – and, crucially, tasks in hardware and software R&D – prior to AGI. I do this via the “effective FLOP gap:” the gap between AGI training requirements and training requirements for AI that can readily perform 20% of cognitive tasks (weighted by economic-value-in-2022). My best guess, defended in the report, is that you need 10,000X more effective compute to train AGI. To estimate the training requirements for AI that can readily perform x% of cognit...