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
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.

>>wredue+Q32
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....