Yudkowsky’s ‘Eiffel Tower neuron’ dialogue mocking the reaction to ‘Already Clean’

Me: Mechanistic interpretability sounds like a good idea, yes. If we could learn something about how an AI was thinking, in enough detail to predict in advance what it would think even as the thought departed from human, that would be a bare beginning on the sort of understanding you’d needed to get started on AI alignment that wasn’t just about hammering on an AI from the outside with SFT and RL. AI guys: We found a neuron that lights up when the AI thinks of the Eiffel Tower! Me: Cool. That does sound like it’d be a prerequisite to getting started on the important part eventually, someday. AI guys: Oh NO! There are MULTIPLE neurons that light up when the AI thinks about the Eiffel Tower! And some of those neurons also participate in OTHER concepts! We’re calling this unexpected development ‘polysemanticity’ and ‘superposition’. Me: That sure sounds like a thing that would happen, yes. I cannot say that I am impressed by you acting surprised about it. Let me know when you’ve gotten far enough in mechanistic interpretability to get started on the important part. AI guys: We have found that with BIG AIs you can often point to a SINGLE neuron that lights up when the AI thinks of the Eiffel Tower! Understanding AI thought processes is gonna be EASY PEASY! Me: Cool. Let me know when you’ve advanced to the point of getting started.

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

https://already-clean.goodfire.com/