zlacker

>>homarp+(OP)
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"

[1]: https://en.wikipedia.org/wiki/Leela_Chess_Zero

>>wavemo+tm1
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.

>>btown+Np1
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.

>>goatlo+dv1
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.

>>foota+AD1
The apples-to-apples comparison would be comparing an LLM with Leela with search turned off (only using a single board state)

According to figure 6b [0] removing MCTS reduces Elo by about 40%, scaling 1800 Elo by 5/3 gives us 3000 Elo which would be superhuman but not as good as e.g. LeelaZero.

[0]: https://gwern.net/doc/reinforcement-learning/model/alphago/2...