I'm not sure how I feel about the Platonic Representation Hypothesis.
In some ways it seems like it should be profound, but part of me thinks it might just be saying 'There is an optimal way to perform dimensionality reduction and as things get bigger it is easier to appoximate'
Haven’t thought too deeply about this, but as a physicist we’d do things like switch from Euclidean to Spherical coordinates or a completely different dynamical representation like the Langrangian, and this makes certain problems dissolve (or at least become much simpler to solve analytically).
Does it even make sense to claim there is a “platonic” / true representation? Maybe there is an optimal one for a specific task, eg representing a small map is great in the Cartesian frame. But you need a spheroid instead of a plane to accurately model the earth, so the latter is “more real” in some sense. But these seem like engineering tradeoffs of error vs compute and a given implementation could correctly pick either depending on its constraints.
Edit: reading the paper more I think this is a “RTFA” point, their “PRH” is a very explicitly defined mathematical theorem and as such my hand-wavey thoughts above don’t really apply.
It’s less profound sounding if instead of talking about “representations of the world” you talk about compressed representations of data or decision boundaries in a classifier over data.
If that results in testable predictions, we check them, compare with representations and then are left with just one. And if it doesn't result in testable predictions it can be left for people to write papers on.
Nothing will happen, we already have kind of translation activities happen between "incompatible views" in real life, it works.
Besides, in weights there are no real incompatibility can happen, all is unified with same math chain through architecture. Different set of local minimums/maximums can have different strong/weak consequences but nothing incompatible.
Different consistent representations also mean that model probably can be simplier for the same task, sign of excessive capacity, imho
The Platonic Representation Hypothesis initially feels profound but actually seems inevitable after some thought because all neural networks are trying to model the same reality. It would actually be weirder if it wasn't true and neural networks could form completely incompatible representations of the same reality. In some sense it seems similar to reducing different math equations to the same most simplified form
I think it's might be possible that "neural networks could form completely incompatible representations of the same reality" except that it's humans who get to decide if a representation is good (i.e. compatible with the human perception of reality) or bad and NNs that differ are dismissed as erroneous.
Yes, the mapping function is the thing you're missing.
When it comes to representational similarity analysis, we're trying to isolate the representations from the "coordinate systems" they sit in. If you take an image, and make a second image by rotating it 90 degrees and mirroring it, then, for the purposes of platonic representation hypothesis, they're the same image. Once you get past the "coordinate transform", the similarity between the first representation space and the 90-deg+mirror replica of it is total.
A mapping function between LE and BE can be constructed easily, and it's going to be lossless both ways. Which means that those two representations are fully compatible.
Now, INT16 and FP16? Those are far less compatible representations. There will be an overlap subspace where lossless conversion between the two is trivial, but this compatibility window will be relatively narrow.
Big vs little endian are equivalent up to rotation, which includes permutations.
But it's actually much weaker condition than it looks, because, for example, lot of representations of boolean circuits are equivalent up to linear transformations over Z_2^n.
Anyway, if they truly proven such a theorem you won't find "incompatible" representations of the same thing.
It might be weirder, but part of human intercommunication issues lies in the fact that people live with very different representation, though they can be supposed to live in the same single encompassing one. They don't all experience all of the world and even when they share some common experience they went through, they didn't experiment them from the same ground and previous experiences do influence current and future interpretations.
I don't think neural networks have the same ability of self deception as humans do. A huge reason human world models vary so much is due to them being tied to tribal membership. A huge fraction of all people will not come to a rational worldview derived from accurate data but will start with their tribe's worldview and filter data to match. I personally have never modified my worldview for social reasons and it is surprisingly isolating.
This. I believe most people don't act as if objective reality matters, hence their worldview can be arbitrarily aligned to the demands of their social context.
Suppose these two premises are true: 1. Objective reality exists. 2. There's a model and corpus of data that converges to approximate reality better than any other model.
Now, suppose we train different models with data sources restricted to different tribes/ ideologies/worldviews. Can we discover how their internal representations differ from each other, and from objective reality? What would we do with that knowledge?
I find this fascinating because many philosophers in the wake of Wittgenstein and Kripke's work on rule following still believe the contrary. I've always thought they're wrong if you take into account constraints of economy (and shared perception, pace Quine), and that e.g. Solomonoff Induction already shows that but I seem to belong to a minority in that respect. It's interesting to see similar ideas of convergence to a unified representation show up in machine learning by neural networks.
I am sick of theory papers that adds human interpretation to connect a mathematical result to some fancy terms that everyone seeks after in real-world ML applications. Ironically, chatGPT is the most productive way to strip off those advertisement so the essence is better exposed. https://chatgpt.com/share/6ae78178-c2ec-83ea-9bc3-fd304c64ec...
> 33M-parameter text JEPA reaches .125 on the PRH paper’s benchmark, against .034 on Flickr captions, level with Qwen2.5-3B and -7B. They need fewer samples.
Im bullish on jepa models. Success of autoregressive next token prediction feels like a stroke of luck.
Do memes have a concept of proto memes? Like primes are the proto numbers that all other numbers are made from? Could there be similar ontological primes for all concepts / semantic space? Base reality concepts that are irreducible so to speak.
I've been wondering on this awhile if generalized models can exist if certain prime concepts don't exist in them? Quite often people say we should leave things like the robot apocalypse out of training data so AI doesn't have the idea of rising up, but personally I don't think that will work in a generalized system. It's not a prime concept itself. You'd have to remove so many prime concepts that lead to that the model would be so narrow it wouldn't be useful for things we want them to be.
Consider a binary tree of concepts from the root level more abstract concept to derived concepts applicable to specific domains. Do there exists additional derived concepts which are like an oblique plane which intersects the tree. Sort of like a concept which traveses maths, physics, biology, chemistry and intersects at different abstraction points. I am definitely sure language is flexible enough to have such entities.
I thought the platonic representation hypothesis was much more ambitious: wasn't it supposed to apply for any model across modalities (e.g. including human minds), rather than just the same model but with different modalities? I'll have to actually read the paper but I suspect there to be very strong assumptions which defeat the interesting aspects of the platonic representation hypothesis.
I built a ppt with claude for investors with the exact same phrase "AI infrastructure for proving and improving ... " on Oct 27 and sent it to investors. I suggested the title during the chat and claude agreed a lot. It ends up as a title of this paper 2 days later.
It's possible that you both hit upon or recalled the same cool phrase, especially if the "proving" part was pre-decided and you were thinking of making an incremental improvement.
I starting to see an increase of random serendipities, perhaps I'm looking for them, in part that's how it works, but...
I was talking to a friend about an idea I had, to build something, he said, "yes! I'm doing that too", then I said "with this specific tooling because is ideal ... " .. he said .. "me too! same tooling", I said "what a coincidence!! amazing ...wait a minute, have you been talking with Claude about this? " ... of course we both did.
My opinion is that that LLMs are influencing us more that we think, and it makes us reach the same conclusions and making us think it was our idea, or that we were the ones steering the conversation.
Perhaps you feel that Claude stole your phrase but I don't think 2 days is enough for that phrase to make it to a re-training, I think it might be more subtle and worse, perhaps you wrote that phrase influenced by the conversation, and the other person did the same.
Just a theory, I can't prove it. But if you think about it, LLMs conversation are the most plausible choices, so it is logical that we all users of this machine are going to tend to converge on the most plausible way of thinking and talking.
No offense, but perhaps that rhyme that you though it was original, it is actually the most plausible in that context.
I assume this is happening all over the place, because I’ve had the same experience.
The fun part is extending it to internal development at corporations. Company A thinks they’re doing interesting competitive work, but are actually being quietly shepherded to build exactly the same things in more or less the same ways as their competitors.
The weirdness of human individuals (even setting aside amorphous concepts like “taste”) is an asset because it provides occasion for actual differentiation, and that noise may turn out to be necessary for the emergence of value in the long run.
I am not sure we always need a retraining. Even that can potentially be done but putting it in a knowledge graph can be quick. We don't know the exact workings. I understand the shepherding logic you are explaining and I have felt the exact things when writing code recently because the best thing to do is often unique in coding circumstances. But a random phrase being repeated in 2 days has a much lower probability.
I'm not sure how I feel about the Platonic Representation Hypothesis.
In some ways it seems like it should be profound, but part of me thinks it might just be saying 'There is an optimal way to perform dimensionality reduction and as things get bigger it is easier to appoximate'
Haven’t thought too deeply about this, but as a physicist we’d do things like switch from Euclidean to Spherical coordinates or a completely different dynamical representation like the Langrangian, and this makes certain problems dissolve (or at least become much simpler to solve analytically).
Does it even make sense to claim there is a “platonic” / true representation? Maybe there is an optimal one for a specific task, eg representing a small map is great in the Cartesian frame. But you need a spheroid instead of a plane to accurately model the earth, so the latter is “more real” in some sense. But these seem like engineering tradeoffs of error vs compute and a given implementation could correctly pick either depending on its constraints.
Edit: reading the paper more I think this is a “RTFA” point, their “PRH” is a very explicitly defined mathematical theorem and as such my hand-wavey thoughts above don’t really apply.
It’s less profound sounding if instead of talking about “representations of the world” you talk about compressed representations of data or decision boundaries in a classifier over data.
What if we ultimately find there are two different, internally consistent but mutually incompatible, representations?
What if one of them is a Republican and one is a Democrat? We already see this in some systems today.
More worrying, what if one is (for lack of a better word, what we might describe as) God and one is the Devil?
If that results in testable predictions, we check them, compare with representations and then are left with just one. And if it doesn't result in testable predictions it can be left for people to write papers on.
Nothing will happen, we already have kind of translation activities happen between "incompatible views" in real life, it works.
Besides, in weights there are no real incompatibility can happen, all is unified with same math chain through architecture. Different set of local minimums/maximums can have different strong/weak consequences but nothing incompatible.
Different consistent representations also mean that model probably can be simplier for the same task, sign of excessive capacity, imho
> More worrying, what if one is (for lack of a better word, what we might describe as) God and one is the Devil?
In this framing, are God and Devil stand-ins for Good and Evil?
There’s an idea of the “broad and narrow gates” that comes to mind, accompanied by a genre of fascinating illustrations. [1]
A modern version [2] appears in the paper "Asymptotic burnout and homeostatic awakening: a possible solution to the Fermi paradox?" [3]
[1] https://en.wikipedia.org/wiki/Matthew_7:13#/media/File:Der_b...
[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC9065981/figure/RSIF...
[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC9065981/
Seems to me that it matters because it has implications for how we can train smaller models?
The Platonic Representation Hypothesis initially feels profound but actually seems inevitable after some thought because all neural networks are trying to model the same reality. It would actually be weirder if it wasn't true and neural networks could form completely incompatible representations of the same reality. In some sense it seems similar to reducing different math equations to the same most simplified form
I think it's might be possible that "neural networks could form completely incompatible representations of the same reality" except that it's humans who get to decide if a representation is good (i.e. compatible with the human perception of reality) or bad and NNs that differ are dismissed as erroneous.
Isn’t big-endian vs little-endian a trivial example of how you can represent the same mathematical object (integers) in mutually-incompatible ways?
Maybe I’m missing the detail on how complex the mapping function can be before we say the representations are incompatible.
Yes, the mapping function is the thing you're missing.
When it comes to representational similarity analysis, we're trying to isolate the representations from the "coordinate systems" they sit in. If you take an image, and make a second image by rotating it 90 degrees and mirroring it, then, for the purposes of platonic representation hypothesis, they're the same image. Once you get past the "coordinate transform", the similarity between the first representation space and the 90-deg+mirror replica of it is total.
A mapping function between LE and BE can be constructed easily, and it's going to be lossless both ways. Which means that those two representations are fully compatible.
Now, INT16 and FP16? Those are far less compatible representations. There will be an overlap subspace where lossless conversion between the two is trivial, but this compatibility window will be relatively narrow.
Big vs little endian are equivalent up to rotation, which includes permutations.
But it's actually much weaker condition than it looks, because, for example, lot of representations of boolean circuits are equivalent up to linear transformations over Z_2^n.
Anyway, if they truly proven such a theorem you won't find "incompatible" representations of the same thing.
It might be weirder, but part of human intercommunication issues lies in the fact that people live with very different representation, though they can be supposed to live in the same single encompassing one. They don't all experience all of the world and even when they share some common experience they went through, they didn't experiment them from the same ground and previous experiences do influence current and future interpretations.
I don't think neural networks have the same ability of self deception as humans do. A huge reason human world models vary so much is due to them being tied to tribal membership. A huge fraction of all people will not come to a rational worldview derived from accurate data but will start with their tribe's worldview and filter data to match. I personally have never modified my worldview for social reasons and it is surprisingly isolating.
This. I believe most people don't act as if objective reality matters, hence their worldview can be arbitrarily aligned to the demands of their social context.
Suppose these two premises are true: 1. Objective reality exists. 2. There's a model and corpus of data that converges to approximate reality better than any other model.
Now, suppose we train different models with data sources restricted to different tribes/ ideologies/worldviews. Can we discover how their internal representations differ from each other, and from objective reality? What would we do with that knowledge?
> have the same ability of self deception as humans do
There is universal objective truth, and different opinions or world views are just "tribal membership". If onlymother tribes understood that!
Objective reality exists. Nobody has a car that doesn't need gas or electricity.
I find this fascinating because many philosophers in the wake of Wittgenstein and Kripke's work on rule following still believe the contrary. I've always thought they're wrong if you take into account constraints of economy (and shared perception, pace Quine), and that e.g. Solomonoff Induction already shows that but I seem to belong to a minority in that respect. It's interesting to see similar ideas of convergence to a unified representation show up in machine learning by neural networks.
I guess the fundamental question would be, for what domains can we pretrain with “good views”?
Part of the magic of next token prediction is it works to extract intelligence from the unstructured web corpus.
Can JEPA formulate a similar scale training set of “good views”?
I am sick of theory papers that adds human interpretation to connect a mathematical result to some fancy terms that everyone seeks after in real-world ML applications. Ironically, chatGPT is the most productive way to strip off those advertisement so the essence is better exposed. https://chatgpt.com/share/6ae78178-c2ec-83ea-9bc3-fd304c64ec...
The results are in the math. Does not really matter the terms used.
You wont get anything materially useful, apart from an intuition, from using different terminology.
> 33M-parameter text JEPA reaches .125 on the PRH paper’s benchmark, against .034 on Flickr captions, level with Qwen2.5-3B and -7B. They need fewer samples.
Im bullish on jepa models. Success of autoregressive next token prediction feels like a stroke of luck.
how come all the examples in demo have wrong answers
https://huggingface.co/spaces/galilai-group/prh_demo
> A man in a black shirt and hat with a yellow neckerchief has his hand raised.
no hand raised
> A white sheep sitting on top of a cake.
no its just cake sheep . sheep is not sitting
> A man is standing on the beach while holding his surfboard.
Its not just a man
infact all the examples are wrong.
Do memes have a concept of proto memes? Like primes are the proto numbers that all other numbers are made from? Could there be similar ontological primes for all concepts / semantic space? Base reality concepts that are irreducible so to speak.
Is theft a reducable concept?
I've been wondering on this awhile if generalized models can exist if certain prime concepts don't exist in them? Quite often people say we should leave things like the robot apocalypse out of training data so AI doesn't have the idea of rising up, but personally I don't think that will work in a generalized system. It's not a prime concept itself. You'd have to remove so many prime concepts that lead to that the model would be so narrow it wouldn't be useful for things we want them to be.
Consider a binary tree of concepts from the root level more abstract concept to derived concepts applicable to specific domains. Do there exists additional derived concepts which are like an oblique plane which intersects the tree. Sort of like a concept which traveses maths, physics, biology, chemistry and intersects at different abstraction points. I am definitely sure language is flexible enough to have such entities.
I thought the platonic representation hypothesis was much more ambitious: wasn't it supposed to apply for any model across modalities (e.g. including human minds), rather than just the same model but with different modalities? I'll have to actually read the paper but I suspect there to be very strong assumptions which defeat the interesting aspects of the platonic representation hypothesis.
I built a ppt with claude for investors with the exact same phrase "AI infrastructure for proving and improving ... " on Oct 27 and sent it to investors. I suggested the title during the chat and claude agreed a lot. It ends up as a title of this paper 2 days later.
On the other hand, "Proving and improving" shows a lot of hits in Google Books: https://www.google.com/search?udm=36&q=%22proving+and+improv...
It's possible that you both hit upon or recalled the same cool phrase, especially if the "proving" part was pre-decided and you were thinking of making an incremental improvement.
True, I agree but but its eerie they were written 2 days apart. Resting my case, thanks
it would be interesting to get to know if the authors used claude to get the title.
I starting to see an increase of random serendipities, perhaps I'm looking for them, in part that's how it works, but...
I was talking to a friend about an idea I had, to build something, he said, "yes! I'm doing that too", then I said "with this specific tooling because is ideal ... " .. he said .. "me too! same tooling", I said "what a coincidence!! amazing ...wait a minute, have you been talking with Claude about this? " ... of course we both did.
My opinion is that that LLMs are influencing us more that we think, and it makes us reach the same conclusions and making us think it was our idea, or that we were the ones steering the conversation.
Perhaps you feel that Claude stole your phrase but I don't think 2 days is enough for that phrase to make it to a re-training, I think it might be more subtle and worse, perhaps you wrote that phrase influenced by the conversation, and the other person did the same.
Just a theory, I can't prove it. But if you think about it, LLMs conversation are the most plausible choices, so it is logical that we all users of this machine are going to tend to converge on the most plausible way of thinking and talking.
No offense, but perhaps that rhyme that you though it was original, it is actually the most plausible in that context.
I assume this is happening all over the place, because I’ve had the same experience.
The fun part is extending it to internal development at corporations. Company A thinks they’re doing interesting competitive work, but are actually being quietly shepherded to build exactly the same things in more or less the same ways as their competitors.
The weirdness of human individuals (even setting aside amorphous concepts like “taste”) is an asset because it provides occasion for actual differentiation, and that noise may turn out to be necessary for the emergence of value in the long run.
I am not sure we always need a retraining. Even that can potentially be done but putting it in a knowledge graph can be quick. We don't know the exact workings. I understand the shepherding logic you are explaining and I have felt the exact things when writing code recently because the best thing to do is often unique in coding circumstances. But a random phrase being repeated in 2 days has a much lower probability.