YHK - yet another hacker news clone

16,000 words into the essay: "What I mean by clean"

...and it still doesn't answer it. What is this actually about?

I think certain classes of artificial neural networks are definitely more interpretable than others.

For example, a trillion-parameter transformer model built using many layers is likely significantly easier to read than one that only has a few million parameters but was constructed using neuroevolution over one gigantic layer.

The degree to which a model can be quantized without losing its performance also likely contributes to interpretability. The redundancy can help humans lock onto patterns. If there is no redundancy at all (everything is a surprise in information theoretical terms), then the stream of parameters will look indistinguishable from cosmic background radiation.

Feels like I'm back in the distill.pub days!

I have yet to fully finish going trough this but I would not rush to say that superposition is completely wrong. I understand that the message is instead of first learning a new representation (e.g. SAEs/transcoders) and then interpreting those learned features, you try to understand the neurons and circuits that already exist in the model.

This approach is part of a way some researchers in the field approach interp, (me including) because I am personally cautious of developing some new representation model that claims to be the way we should represent the network. It is another layer of abstraction that takes us away from core model components, very often this requires training a separate model that was not on its own a part of original model organism during training (SAE).

I would argue that models will likely be further increasing in complexity, not just in size, when model trains continuously and becomes adaptive yet parameter count does not change, it is not hard to imagine that neurons may reorganize themselves to pack more information.

Why does this post have a very laggy city gif thing at the top?

This website crashed my phone - let's not have auto-playing embedded 3D renders, or whatever that was?

Wow, I thought it was a GIF, but it's actually interactive. The website is just too heavy, though.

To cut through some of all this text - here's a link he calls "the first non-trivial neural network I feel I mostly understand:

https://already-clean.goodfire.com/microscope/medium.html?la...

This is the primary reason, to me, that open weights are so important. If OpenAI and Anthropic want these models to be as autonomous as possible they also need to provide the means to understand them fully.

Nick is a force of nature. Highly recommend following him on X if you don't already.

His launch post gives good context not found in the blog:

> 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://x.com/nickcammarata/status/2110757819427574056?s=46

For the layperson who doesn't want to read 20,000 words, many of which are quite redundant due to the way the article was built, this is a version which adds a glossary, preserves the original language (except the added intro and conclusion which I felt was lacking) but removes all repeated material and claims, of which there was a massive amount.

https://pastebin.com/raw/p11vzFnB

I'm not sure I buy the author's arguments for mechanistic interpretability being easy, but it sure is a well-made and very interesting post. Would be better without the completely superfluous laggy 3d city model, though.

I will never stop using Twitter but you will never make me call it "X"

("why keep using it?" - even with all the Nazis and Kim Jong Un stans, it's still way better for tech things than Bluesky and Mastodon and Lobste.rs and Reddit and, frankly, HN)