roon on Cammarata’s essay: ‘this may be remembered as a major moment in scientific history’
roon @tszzlX
Why it matters
Highest-reach reaction to the ‘Already Clean’ essay (~236k views); it drew Eliezer Yudkowsky’s rebuttal.
Summary
roon (OpenAI) quoted Nick Cammarata’s announcement two minutes after it went up. He wrote that Cammarata had been “underselling” his interp breakthroughs, that “this may be remembered as a major moment in scientific history”, and that “we can start making alignment into a ‘normal science’” (~236k views, ~4.8k likes on Oct 15). In a follow-up (https://x.com/tszzl/status/2110758312853832083, ~31k views) he said his understanding of neural nets had been “catapulted into the future by five years or something”. Eliezer Yudkowsky replied (https://x.com/allTheYud/status/2110804218005332178) that no level of interpretability makes aligning ASI “normal science”.
Archived text
when nick told me that he had some interp breakthroughs, he was uh, underselling it. i think this may be remembered as a major moment in scientific history. news so good even eliezer will have to lower his pdoom. we can start making alignment into a ‘normal science’
Quoting @nickcammarata: interpretability might be the most important scientific problem in the world right now, as I don’t think we can solve alignment if we don’t know what we’re building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai’s first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren’t there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
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People in this post
Eliezer Yudkowsky, Founder and research fellow, Machine Intelligence Research Institute (MIRI); Nick Cammarata, Interpretability researcher, Goodfire