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
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.
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.
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.
Yes. In that sense it is equivalent to a compression method that uses K-means clustering to do the partitioning. But because there are many more bits of knowledge out there than there are bits in the models you end up with this amorphous smear. I'm sure some of those bits carry more information about some of the concepts but in general - and that's the way it is explained in just about all the papers I've read that I can still comprehend - every weight contributes to the output in a dense model. In an MOE things get more complicated but there within every 'MOE' column the same principle applies.
I'm fine with taking this article at the high level conceptual level, that's effectively the kind of thinking that led to MOE to begin with. But I don't see that as a pathway to being able to 'explain' a model. Because if you could actually explain a model you could engineer the weights, you wouldn't need any training because it implies that you can improve on the model by simply performing surgery on the weights to get it to do what it does but better. Now, obviously, unlike the author I'm a complete nobody but for the last two years or so I've been trying very hard to understand what is going on under the hood.
It's akin to feature engineering - or rather, the lack thereof - and to see a strong suggestion that we maybe be able to reverse that is interesting, but absent hard proof at how a particular model reached a particular conclusion it is difficult to give that a lot of weight right now. It is possible that such proof is buried somewhere in TFA and I overlooked it during reading. But I think this is speculative rather than that it proves anything and before I'm going to be convinced I would like a little more evidence.
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.
GP and the article both make clear arguments for WHY they believe this. You just asked a lazy drive-by question without providing any reasoning at all for your position. If you expect a good response, you need to provide something meaningful to respond to, as it is it just looks like you are providing knee-jerk skepticism without having engaged with the material.
> There have been large multitudes of articles by others who are arguing the opposite
Also, Nick's entire article is framed such that, yes, plenty of others have argued this, and even HE used to believe the opposite, but now does not. So your point about other articles here seems to me to be a sort of non-sequitur.
I just - politely - asked you a simple question because your statement made me curious. I have also posted a longer explanation of my own thinking on this in another part of this thread.
Yes, that comment is much better and appreciated. I agree post-hoc description is not the same thing as mechanistic explainability at all. The article is indeed flawed in sort of muddying the distinction between these two.
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.
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.
> All non-trivial properties of neural networks are undecidable.
hey, I don't know how to say this less directly, but: you're talking a lot about Rice's theorem in your comments, and I think you're using it way too broadly (look, I get it. It's a wild, mindblowing theorem). But it only applies when you take the claim in the piece to be much more general than it has to be, and when you take the narrow claim of the paper (current specific LLMs can be fruitfully analyzed with the tools we have), Rice's theorem just isn't at play.
The math analog is: you're right that the Halting problem says there are many unprovable things, but Lean + mathlib are still incredibly useful, and the halting theorem doesn't actually stop that.
> neural networks are algorithms
Again, that invites an interpretation that is too broad. Very concretely: any one neural network is a _single finite network that you can write as a single formula_. (Another analogy: we can't have an algorithm that solves the general quintic, but we can prove useful things about many specific quintics.)
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.
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
> far as I can tell, experiments show that Fable has basically memorized every lat/long position on the map of the earth … your standing in the ocean or not based on your coordinates
Memorising a small shape file of the earth’s continents can allow you to determine if a person is on land or in water.
All functional knowledge is compression and contextual decompression. Sometimes this compression can be lossless or nearly so. Compression doesn't automatically mean loss of information.
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?
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.
Adding a glossary doesn't really make the material approachable to laypeople. I have a university degree and I've been spending quite a bit of the last couple of years trying to understand these things by osmosis, and I'm still lost. I don't think I even have a reference point to understand the problem that the author has stopped worrying so much about.
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.
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.
A lot of the mech interp techniques are glorified notions of probing. If models were generally interpretable with respect to the things we cared about, we'd know know when they're hallucinating, for example.
Identifying that a common neuron or set of neurons fires for golden gate bridge can be done only because you already know that you're looking for golden gate bridge. If models had recovered some new truth (e.g., the silver door road), you wouldn't be able to elicit it without knowing to look for it a priori.
This was one of my intuitive concerns reading this post.
I've done work with trying to train explainable models for e.g. making diagnoses based on neuroimaging, and it is not clear to me that if you applied these techniques to e.g. vision models trained exclusively on such brain images, that these visual representations or conceptual clusters and relations would really help understanding at all.
The main issue would be that you'd have no clear way to verify any generated explanation for such training data. I.e. we can "verify" some of the explainability shown in the article because clear and recognizable image clusters and/or semantic groupings pop out, because we already understand the domains. But if this were fMRI or e.g. EEG signals, we really wouldn't have that, so the explanations would themselves just be more hypotheses.
Perhaps if we can build on interpretability of vision and language models in domains we understand, we can eventually generalize this to other domains, but I don't think that clearly follows from anything I am reading here.
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.
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
> 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.
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?
I'm fine with taking this article at the high level conceptual level, that's effectively the kind of thinking that led to MOE to begin with. But I don't see that as a pathway to being able to 'explain' a model. Because if you could actually explain a model you could engineer the weights, you wouldn't need any training because it implies that you can improve on the model by simply performing surgery on the weights to get it to do what it does but better. Now, obviously, unlike the author I'm a complete nobody but for the last two years or so I've been trying very hard to understand what is going on under the hood.
It's akin to feature engineering - or rather, the lack thereof - and to see a strong suggestion that we maybe be able to reverse that is interesting, but absent hard proof at how a particular model reached a particular conclusion it is difficult to give that a lot of weight right now. It is possible that such proof is buried somewhere in TFA and I overlooked it during reading. But I think this is speculative rather than that it proves anything and before I'm going to be convinced I would like a little more evidence.
> There have been large multitudes of articles by others who are arguing the opposite
Also, Nick's entire article is framed such that, yes, plenty of others have argued this, and even HE used to believe the opposite, but now does not. So your point about other articles here seems to me to be a sort of non-sequitur.
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.
https://already-clean.goodfire.com/microscope/medium.html?la...
hey, I don't know how to say this less directly, but: you're talking a lot about Rice's theorem in your comments, and I think you're using it way too broadly (look, I get it. It's a wild, mindblowing theorem). But it only applies when you take the claim in the piece to be much more general than it has to be, and when you take the narrow claim of the paper (current specific LLMs can be fruitfully analyzed with the tools we have), Rice's theorem just isn't at play.
The math analog is: you're right that the Halting problem says there are many unprovable things, but Lean + mathlib are still incredibly useful, and the halting theorem doesn't actually stop that.
> neural networks are algorithms
Again, that invites an interpretation that is too broad. Very concretely: any one neural network is a _single finite network that you can write as a single formula_. (Another analogy: we can't have an algorithm that solves the general quintic, but we can prove useful things about many specific quintics.)
Now or in the near future we will be studying minds much greater than our own, and we will find out how well we can understand them.
If they don't kill us all before then, of course.
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
Memorising a small shape file of the earth’s continents can allow you to determine if a person is on land or in water.
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?
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.
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.
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
A lot of the mech interp techniques are glorified notions of probing. If models were generally interpretable with respect to the things we cared about, we'd know know when they're hallucinating, for example.
Identifying that a common neuron or set of neurons fires for golden gate bridge can be done only because you already know that you're looking for golden gate bridge. If models had recovered some new truth (e.g., the silver door road), you wouldn't be able to elicit it without knowing to look for it a priori.
I've done work with trying to train explainable models for e.g. making diagnoses based on neuroimaging, and it is not clear to me that if you applied these techniques to e.g. vision models trained exclusively on such brain images, that these visual representations or conceptual clusters and relations would really help understanding at all.
The main issue would be that you'd have no clear way to verify any generated explanation for such training data. I.e. we can "verify" some of the explainability shown in the article because clear and recognizable image clusters and/or semantic groupings pop out, because we already understand the domains. But if this were fMRI or e.g. EEG signals, we really wouldn't have that, so the explanations would themselves just be more hypotheses.
Perhaps if we can build on interpretability of vision and language models in domains we understand, we can eventually generalize this to other domains, but I don't think that clearly follows from anything I am reading here.
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.
(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.