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
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
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.
If you actually mean neuroscientist there you need to understand that "neural" network is just a name. The resemblance is faint and the tools are wildly different.
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.
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?
Understanding algorithms beyond a certain complexity is a pipe dream & neural networks are algorithms. The theory says it is not possible & the practice is aligned with the theory. All non-trivial properties of neural networks are undecidable.
What theory says this? Surely not a proven or applicable one, as this post makes it very clear a great deal of understanding is in fact possible, even with highly complex neural networks.
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.
> 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.
Neuroevolution is a discontinuous way to go about building the network. You are evolving the topology itself from zero. You don't know how many parameters you will have until the training is complete (if it ever does). With a transformer model you start with a known-good topology and can calculate how long a training run will take.
This is a Chinese character that can refer to goats, rams, and several other kinds of "medium-sized animal with thick wooly coats and horns". English has no direct equivalent; we have to be too specific when we talk about these animals.
A model with a small number of neurons has to compact its concepts in order to fit within the parameter count while reducing loss. OP's article shows that the compaction is usually generalization: "goat" and "ram" get compressed into "羊". The canonical example of superposition, an image model where "car" feeds into a bunch of "dog" features, was shown to be actually generalization. In a sense, the model was so small that its optimizer didn't have the parameter budget to distinguish between "car" and "dog"; it has a single set of "thing that moves fast" features.
So basically, really small models are merging features into semantic buckets that are so generalized they don't make sense to big human brains unless we really, really squint. The buckets chosen happen to be what has the lowest loss, of course.
Yes, but it is the same person making the same argument many times. There have been large multitudes of articles by others who are arguing the opposite. It could well be true. But it would take a lot more than just one over-wordy, distraction imaged, clearly co-written by AI article to convince me of that.
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.
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.
It means that NNs don't (as previously thought) smoosh unrelated concepts together into the same neurons, which would have made interpretability very difficult.
For instance, if you had a single neuron that fires for "puppies" and "murder", it would be much more difficult to tell if the model is thinking something nice or something not so nice.
But they don't. That's the whole reason they work in the first place.
Every neuron in the layer encodes fractionally something about the concepts embedded in the whole thing. It's more like a hologram than a series of connected facts.
Orthogonal means something different in mechinterp, so it'd be an overloaded term. Usually orthogonal would imply that there are actual vectors under consideration, which isn't the underlying claim.
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)
It starts with "The Short Version", but even that looks like several pages of text. In the olden days, there was something called an "Abstract" that worked pretty well - the author could summarize their own work to say what the paper was about.
Unfortunately not all ideas can be meaningfully compressed into just a few sentences without completely destroying the important parts, and this is especially the case in higher-level math, stats, ML, and deep learning.
Also, IMO there is an effective abstract anyway under the title text:
The cause of polysemanticity in early model organisms wasn’t superposition, but several ordinary, mostly fixable issues. We can now understand most neurons in models up to trillions of parameters, and think the outlook for mechanistic interpretability may be far better than we thought.
But you are right the article needs significant editing down.
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
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
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?
https://already-clean.goodfire.com/microscope/medium.html?la...
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.
Why do you believe this?
This is a Chinese character that can refer to goats, rams, and several other kinds of "medium-sized animal with thick wooly coats and horns". English has no direct equivalent; we have to be too specific when we talk about these animals.
A model with a small number of neurons has to compact its concepts in order to fit within the parameter count while reducing loss. OP's article shows that the compaction is usually generalization: "goat" and "ram" get compressed into "羊". The canonical example of superposition, an image model where "car" feeds into a bunch of "dog" features, was shown to be actually generalization. In a sense, the model was so small that its optimizer didn't have the parameter budget to distinguish between "car" and "dog"; it has a single set of "thing that moves fast" features.
So basically, really small models are merging features into semantic buckets that are so generalized they don't make sense to big human brains unless we really, really squint. The buckets chosen happen to be what has the lowest loss, of course.
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.
https://pastebin.com/raw/p11vzFnB
...and it still doesn't answer it. What is this actually about?
For instance, if you had a single neuron that fires for "puppies" and "murder", it would be much more difficult to tell if the model is thinking something nice or something not so nice.
https://www.goodfire.com/blog/a-practical-guide-to-saes
Every neuron in the layer encodes fractionally something about the concepts embedded in the whole thing. It's more like a hologram than a series of connected facts.
("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)
(We detached this comment from https://news.ycombinator.com/item?id=50107254 though)
Also, IMO there is an effective abstract anyway under the title text:
But you are right the article needs significant editing down.