3 ms·

The fact that LLMs can play chess at any level is a strong indication we are in AGI.

• recursive 17d ago
  Can they if they frequently make illegal moves?
  • lostmsu 17d ago
    Do they?
• bigstrat2003 17d ago
  No it isn't. Computers could play chess long before LLMs, better than LLMs can in fact. That didn't make them AGI.
  • lostmsu 17d ago
    You are saying "No it is not" without an argument. The fact that computer systems could play chess yet not being AGI has no relevance to LLMs' ability to play chess being AGI, because the point is about G, not I. There's little doubt about A or I parts.
• zahlman 17d ago
  This is roughly comparable to observing a cat batting a ball away with its paw and taking this as a "strong indication" that cats can play any sport.
  • lostmsu 17d ago
    Yes, a good analogy. Except the cat actually follows the football rules and can beat some humans. And has no physical limitations to play other kinds of sport that you might imply.
• HarHarVeryFunny 16d ago
  It would be more impressive if they could play chess (or do anything they haven't been custom RLVR trained for) by reasoning, rather than just "have a go at it" prediction which is closer to memorization. HOW you do it makes a big difference in how you should assess the capability of the thing doing it. Stockfish will trounce any LLM, and any human, at chess, so should we say that Stockfish is smarter than both?
  • lostmsu 16d ago
    They can't possibly remember even a few positions. Don't you know the legend about rice grains on a chess board? The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent.
    • HarHarVeryFunny 16d ago
      > They can't possibly remember even a few positions. Sure they could, but that's irrelevant. A chess position is just a matter of remembering what piece number is on each square - just a list of 64 numbers. A trained model may store a trillion numbers (weights). It could store a TON of chess positions if it needed to. However, that's not how LLMs work. They don't memorize inputs - they predict them, based on discovering predictive patterns, and those predictive patterns are not input patterns (e.g. board positions). They are deep patterns (maybe 100 layers of abstraction removed from the input), representing partial inputs, generalized across many training samples. > Don't you know the legend about rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data. > The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent. Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If I say "E=mc^2", does that make you think I am Einstein?
      • lostmsu 16d ago
        > prediction which is closer to memorization > don't memorize inputs - they predict them I feel some tension here. > rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data. > just a list of 64 numbers > remember even a few positions? Sure they could, but that's irrelevant. I don't think you do. Or rather you do know the legend but for some funny reason seem to be unable to apply its lesson here, because you are talking about enormous terabytes of training data. > Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If for you it is about intelligence, I am out of this discussion.