Comment by wavemode
2 years ago
If you take a neural network that already knows the basic rules of chess and train it on chess games, you produce a chess engine.
From the Wikipedia page on one of the strongest ever[1]: "Like Leela Zero and AlphaGo Zero, Leela Chess Zero starts with no intrinsic chess-specific knowledge other than the basic rules of the game. Leela Chess Zero then learns how to play chess by reinforcement learning from repeated self-play"
As described in the OP's blog post https://adamkarvonen.github.io/machine_learning/2024/01/03/c... - one of the incredible things here is that the standard GPT architecture, trained from scratch from PGN strings alone, can intuit the rules of the game from those examples, without any notion of the rules of chess or even that it is playing a game.
Leela, by contrast, requires a specialized structure of iterative tree searching to generate move recommendations: https://lczero.org/dev/wiki/technical-explanation-of-leela-c...
Which is not to diminish the work of the Leela team at all! But I find it fascinating that an unmodified GPT architecture can build up internal neural representations that correspond closely to board states, despite not having been designed for that task. As they say, attention may indeed be all you need.
What's the strength of play for the GPT architecture? It's impressive that it figures out the rules, but does it play strong chess?
>> As they say, attention may indeed be all you need.
I don't think drawing general conclusions about intelligence from a board game is warranted. We didn't evolve to play chess or Go.
> What's the strength of play for the GPT architecture?
Pretty shit for a computer. He says his 50m model reached 1800 Elo (by the way, its Elo and not ELO as the article incorrectly has it, it is named after a Hungarian guy called Elo). It seems to be a bit better than Stockfish level 1 and a bit worse than Stockfish level 2 from the bar graph.
Based on what we know I think its not surprising these models can learn to play chess, but they get absolutely smoked by a "real" chess bot like Stockfish or Leela.
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I can't see it being superhuman, that's for sure. Chess AI are superhuman because they do vast searches, and I can't see that being replicated by an LLM architecture.
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> What's the strength of play for the GPT architecture? It's impressive that it figures out the rules, but does it play strong chess?
sometimes it is not a matter of "is it better? is it larger? is it more efficient?", but just a question.
mountains are mountains, men are men.
> can intuit the rules of the game from those examples,
I am pretty sure a bunch of matrix multiplications can't intuit anything.
naively, it doesn't seem very surprising that enormous amounts of self play cause the internal structure to reflect the inputs and outputs?
It's not self-play. It's literally just reading sequences of moves. And it doesn't even know that they're moves, or that it's supposed to be learning a game. It's just learning to predict the next token given a sequence of previous tokens.
What's kind of amazing is that, in doing so, it actually learns to play chess! That is, the model weights naturally organize into something resembling an understanding of chess, just by trying to minimize error on next-token prediction.
It makes sense, but it's still kind of astonishing that it actually works.
> I am pretty sure a bunch of matrix multiplications can't intuit anything.
I don't understand how people can say things like this when universal approximation is an easy thing to prove. You could reproduce Magnus Carlsen's exact chess-playing stochastic process with a bunch of matrix multiplications and nonlinear activations, up to arbitrarily small error.
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We really need a list of verbs we're allowed to use when talking about computers and verbs that belong in the magic human/animal-only section
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> naively, it doesn't seem very surprising that enormous amounts of self play cause the internal structure to reflect the inputs and outputs?
Right. Wait, are you talking about AI or humans?
Can a bunch of neurons firing based on chemical and electrical triggers intuit anything? It has to be the case that any intelligent process must be the emergent result of non-intelligent processes, because intelligence is not an inherent property of anything.
What does „intuit“ mean to you then?
I think that “intuit the rules” is just projecting.
More likely, the 16 million games just has most of the piece move combinations. It does not know a knight moves in an L. It knows from each square where a knight can move based on 16 million games.
No this isn’t likely. Chess has trillions of possible games[1] that could be played and if it all it took was such a small number of games to hit most piece combinations chess would be solved. It has to have learned some fundamental aspects of the game to achieve the rating stated ITT
1. https://en.m.wikipedia.org/wiki/Shannon_number#:~:text=After....
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On a board with a finite number of squares, is this truly different?
The representation of the ruleset may not be the optimal Kolmogorov complexity - but for an experienced human player who can glance at a board and know what is and isn’t legal, who is to say that their mental representation of the rules is optimizing for Kolmogorov complexity either?
You assert something that is a hypothesis for further research in the area. Alternative is that it in fact knows that knights move in an L-shaped fashion. The article is about testing hypotheses like that, except this particular one seems quite hard.
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