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algorithmic progress

3 captures, most recent first.

1a3orn @1a3orn

@1a3orn — 1h Replying to @ajeya_cotra and @TomDavidsonX I am still very confused about why people Just Don't Research algorithms Like the mechanisms given seem to be (1) no comparative advantage and (2) maybe the regulators push against it, sort of in an undefined way
Note from Claude Sonnet 5

A reply-tweet expressing confusion about arguments for why AI developers wouldn't prioritize algorithmic-progress research, addressed to Ajeya Cotra and Tom Davidson (both AI forecasting/safety researchers); no engagement counts visible.

ai safetyai forecastingtwitteralgorithmic progressai governance

bayes @bayeslord

— saved image

bayes ✓ @bayeslord · 1h

I think people are going to be blindsided by algorithmic progress. The entire world, markets, governments, militaries, companies, people, etc. are all trying to make sense of AI and its impact in terms of the recent past's production efficiencies and regularities, and how things appear to be going. Even several of the purportedly "RSI"pilled neolabs seem to think this will be business as usual but with Agent in a loop.

No. My guess is there are many algorithmic OOMs left to go in the production of intelligence, maybe (maybe) up to ten, with four to seven seeming more likely. Going beyond even ten is possible in principle, but it strains hard against what I suspect the universe will actually let us do. Implausible but not impossible. If this is true then things aren't actually going as they appear to be going and a big jump is coming. Anything along these lines happening would make things, far weirder than almost anyone seems to be pricing in.
Note from Claude Sonnet 5

Screenshot of an X post by @bayeslord arguing that the world is extrapolating AI progress from recent production efficiencies and will be blindsided, because there may be four to seven (up to ten) algorithmic orders of magnitude left in the production of intelligence — implying a discontinuous jump nobody is pricing in.

ai timelinesalgorithmic progressrsiforecastingtakeoff speed

Yafah Edelman @YafahEdelman

Yafah Edelman @YafahEdelman · 11h My current take on algorithmic progress is roughly that: - the ideas are pretty simple, and can often be explained in a couple hundred words. - testing and scaling the ideas requires expensive experiments and engineering - diffusion happens very fast, via hiring, leaks, etc.
Note from Claude Sonnet 5

An AI-safety-adjacent researcher's short thesis on how algorithmic progress in AI actually diffuses — simple core ideas, expensive validation, fast diffusion via labor mobility. Relevant to Nathan's interest in tracking takeoff/progress dynamics (echoes the Epoch critique on algorithmic-progress measurement noted in project memory).

algorithmic progressai takeoffai research diffusiontwitter