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I disagree with The Bitter Lesson, or at least one particular example that the essay gives: chess programming. Modern engines would crush Deep Blue with fewer

I disagree with The Bitter Lesson, or at least one particular example that the essay gives: chess programming. Modern engines would crush Deep Blue with fewer resources and even without NNUE, and this is because of specific search techniques and search improvements that we've discovered. For example: formulas that work well for late-move reduction; continuation history tables and the update formulas that work well for them; internal iterative reductions; refinements to futility pruning, reverse futility pruning, delta pruning, multi-probcut, and so on. None of these were found by meta methods and none of these emerge from scaling computation. But it's also the case that none of them resemble anything that a human does when they play chess. So I don't think I agree with the first point of the bitter lesson, that general methods beat specific methods, and that scaling beats efficiency. But I do agree with the second point, that performant systems often behave in ways that don't resemble human thinking, and we're often hopeless to personally understand performant systems' complexity.

sebzim4500 4y ago
>Modern engines would crush Deep Blue with fewer resources and even without NNUE The point of the essay is that modern engines with NNUE beat modern engines without NNUE. So all those human coded features that used to be used to evaluate positions are no longer necessary.
cypress66 4y ago
Chess engines aren't a done deal. Besides NNUE (which I would consider more general) that are outright better than handcrafted eval functions, the dust hasn't settled yet between the more specific AB pruning stockfish vs the more general MCS lc0. Yes, stockfish has been generally slightly stronger, but in the near future with better NN architectures I could easily see this changing. And even then, chess is probably an example of something very narrow that you can deal with specific methods. Humanoid robots are probably the opposite, and you probably need AGI for them.
GenCuriosity 4y ago
I'm not an expert in this, so question... Doesn't crush the AI AlphaZero the software Stockfish? Yes, AlphaZero needs more resources - especially for training. But it learns chess from scratch by self playing - in a few hours, without any data from over 1000 years of humankind chess experience. Deep Blue was more like Stockfish than AlphaZero. They called it AI - but its not an AI in the sense of AlphaZero, it was an expert system like Stockfish with special search algorithms. They just needed such a big machine, because this is just like CPU power developed. So I think the term "Bitter Lesson" is a very good match for chess too. A modern AI plays 4 hours from level "very stup... beginner" to "better than any human or machine in the world". It doesn't need a bunch of human super experts, that inject the complete human chess knowledge into specialized databases. This is bitter - isn't it?

sebzim4500 4y ago

>Modern engines would crush Deep Blue with fewer resources and even without NNUE The point of the essay is that modern engines with NNUE beat modern engines without NNUE. So all those human coded features that used to be used to evaluate positions are no longer necessary.

cypress66 4y ago

Chess engines aren't a done deal. Besides NNUE (which I would consider more general) that are outright better than handcrafted eval functions, the dust hasn't settled yet between the more specific AB pruning stockfish vs the more general MCS lc0. Yes, stockfish has been generally slightly stronger, but in the near future with better NN architectures I could easily see this changing. And even then, chess is probably an example of something very narrow that you can deal with specific methods. Humanoid robots are probably the opposite, and you probably need AGI for them.

GenCuriosity 4y ago

I'm not an expert in this, so question... Doesn't crush the AI AlphaZero the software Stockfish? Yes, AlphaZero needs more resources - especially for training. But it learns chess from scratch by self playing - in a few hours, without any data from over 1000 years of humankind chess experience. Deep Blue was more like Stockfish than AlphaZero. They called it AI - but its not an AI in the sense of AlphaZero, it was an expert system like Stockfish with special search algorithms. They just needed such a big machine, because this is just like CPU power developed. So I think the term "Bitter Lesson" is a very good match for chess too. A modern AI plays 4 hours from level "very stup... beginner" to "better than any human or machine in the world". It doesn't need a bunch of human super experts, that inject the complete human chess knowledge into specialized databases. This is bitter - isn't it?