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[return to "Chess-GPT's Internal World Model"]
1. sinuhe+4Z[view] [source] 2024-01-07 00:58:10
>>homarp+(OP)
World model might be a too big word here. When we talk of a world model (in the context of AI motels), we refer to its understanding of the world, at least in the context we trained it. But what I see is just a visualization of the output in a fashion similar to a chess board. A stronger evidence would be a for example a map of the next move, which will show whether it truly understood the game’s rules. If it show probability larger than zero on illegal board fields, it will show us why it sometimes makes illegal moves. And obviously, it didn’t fully understand the rules of the game.
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2. mitthr+n61[view] [source] 2024-01-07 02:09:45
>>sinuhe+4Z
> probability larger than zero

Strictly speaking, it should be a mistake to assign a probability equal to zero to any moves, even for illegal board moves, but especially for an AI that learns by example and self-play. It never gets taught the rules, it only gets shown the games -- there's no reason that it should conclude that the probability of a rook moving diagonally is exactly zero just because it's never seen it happen in the data, and gets penalized in training every time it tries it.

But even for a human, assigning probability of exactly zero is too strong. It would forbid any possibility that you misunderstand any rules, or forgot any special cases. It's a good idea to always maintain at least a small amount of epistemic humility that you might be mistaken about the rules, so that sufficiently overwhelmingly strong evidence could convince you that a move you thought was illegal turns out to be legal.

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3. goatlo+Av1[view] [source] 2024-01-07 07:21:29
>>mitthr+n61
The rules of chess are small and well known. For example, rooks can't go diagonal no matter the situation. There's no need for epistemic humility.
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