alt Hacker NewsNeural networks are clean
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The claims seem to be an overreach of what is actually proven. We've known about progressive feature complexity and the impacts of early training structures on later training for a really long time (since CNN's, The Lottery Ticket Hypothesis, etc...) and neither of those phenomenon are mutually exclusive with parameter overloading. The Multimodal Gap work (https://arxiv.org/abs/2203.02053) seems to imply networks have a tendency towards condensed representations that are clearly not semantic and a bunch of other considerations are already on the table for discussion. I don't think these have all been considered sufficiently to start claiming we are around the corner from mechanistic interpretation.
I only skimmed through this post. But I think this is a crux:
> The more we read [the neural network] the way its own readers do, the more interpretable I think it will look. We should be wary of reading it too much like a human, and concluding it’s messy just because it doesn’t fit our preconceived concepts.
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.
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.
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...
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.
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.
Intuitively this makes sense. When you have small models, they need to compress more by leveraging superposition and polysemanticity.
Nick is looking at a 32B model, which certainly has to compress less... Now we have 10 trillion parameter models... do they need to compress at all? As far as I can tell, experiments show that Fable has basically memorized every lat/long position on the map of the earth can can tell you at a glance whether your standing in the ocean or not based on your coordinates
Aye, will we stop hammering nails on God's coffin?
What's next? Maybe in a little bit we will discover that we H. Sapiens suffer from severe polysemanticity in our wetware because our individual neurons are messy and massive like an ENIAC while the corvids in the park have neat and efficient little brains that don't need unfolding ?... How's the pope (may God stop afflicting him with cancer or may Man make him live forever) going to explain that?
There is a philosophical problem hidden in the core of interpretability: Can a mind understand a mind much greater than itself?
> The bad news about modern neural networks is that they're enormous. The good news is that the enormity helps...
I always catch on misuse of enormity
It's now so widespread the misuse has gained despairing acceptance, as with "infer" for "imply"... but that doesn't make it any less wince-inducing.
Reading this made me recognize that better interpretability yields not just better aligned but more compact models too. Which makes a lot of sense in hindsight, and even has some ironic ring to it when considering the unending amount of anthropocentric takes regarding what an "understanding" means.
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.
Wow, I thought it was a GIF, but it's actually interactive. The website is just too heavy, though.
16,000 words into the essay: "What I mean by clean"
...and it still doesn't answer it. What is this actually about?
[flagged]
This website crashed my phone - let's not have auto-playing embedded 3D renders, or whatever that was?
Why does this post have a very laggy city gif thing at the top?
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