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
> 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?

>>banana+lC1
What does „intuit“ mean to you then?