Computers win at chess by brute force: searching possible moves much further ahead than humans, evaluating millions of possibilities.
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Replying to @Meaningness
It has always been known that brute-force tree search won’t work for Go; there are too many possible moves. So, you need a better evaluator.
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Replying to @Meaningness
The standard analysis of Go has always been that grandmasters see regional patterns on the board that are good or bad. That’s evaluation.
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Replying to @Meaningness
What Google did was spend incredible quantities of computer time playing vast numbers of games—noting which board configurations led to wins
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Replying to @Meaningness
It is hard to see how this strategy could have not-worked.
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Replying to @Meaningness
Only question is whether the AlphaGo “neural” network did any non-obvious generalization. I don’t have access to the journal article, but >
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Replying to @Meaningness
> nothing I have read suggests it did. Generally, when analyzing “neural” learning results, I’ve found they didn’t do anything interesting.
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Replying to @Meaningness
@Meaningness it seriously seems like your standards are too high here1 reply 0 retweets 0 likes -
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Replying to @Meaningness
@Meaningness like, you're complaining that the generalizations found are not "surprising/deep" but come on, what is even depth2 replies 0 retweets 2 likes
@admittedlyhuman Well, in one case I analyzed, it turned out that replacing the NN with a linear evaluator worked better.
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Replying to @Meaningness
@admittedlyhuman If it turned out AlphaGo was just learning a linear combination of features, would you agree it was uninteresting?2 replies 0 retweets 1 like -
Replying to @Meaningness
@Meaningness no, because finding the right mix of features and weights to linearally combine is still an achievement2 replies 0 retweets 1 like - 15 more replies
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