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> only to turn around and tell us that it is amazing that it can also win at Chess. What they're demoing here is a single, general formula for mastering multip

> only to turn around and tell us that it is amazing that it can also win at Chess. What they're demoing here is a single, general formula for mastering multiple games. Start with empty AG0, then teach it chess from scratch until it is the strongest player on the planet. Go back to an empty slate, with the same exactly "untrained" AG0, and now teach it Go, to the same result. No fine-tuning for the domain of the game you are training -- it is general(ized). That's the gist I'm getting from this. question for someone who has time to read the paper: can you train it to master chess and go at the same time? or is it one or the other? I'm assuming the latter. edit: check out the graph on the 4th page. AlphaZero, which can master chess and shogi, can beat AlphaGo Zero, the implementation specifically designed for Go, at its own game.

visarga 9y ago
Question: do you think you are using the same parts of the brain to play chess and Go? What counts is not using the same neurons, but using the same neural algorithm.
gwern 9y ago
> question for someone who has time to read the paper: can you train it to master chess and go at the same time? or is it one or the other? I'm assuming the latter. I'm sure you could with a multi-headed NN. But what would be the point? There's very little transfer of knowledge between the games, especially once you get past the very most basics.
- thanatropism 9y ago
  The point is that real problem domains are not neatly partitioned and labeled. I don't know what kind of input the NN itself gets, but computer vision is enough to translate a photo of a chessboard to a usable symbolic representation. But it would be nice to already have a black box-ish computer program that figures out what's the game at hand and how to play it. The next variation is have the adversary start playing a chess variant and have the machine recognize it (assuming honesty) and play it to significant skill. Then "real life Pong" where the size and aerodynamics of the ball are unknown to it. This is the gist of human intelligence: answering questions is significantly easier than figuring out what the question is.
jmmcd 9y ago
> Go back to an empty slate, with the same exactly "untrained" AG0, and now teach it Go, to the same result. No fine-tuning for the domain of the game you are training -- it is general(ized). Not quite -- different input features, which implies slightly different network architecture at least at the front.

visarga 9y ago

Question: do you think you are using the same parts of the brain to play chess and Go? What counts is not using the same neurons, but using the same neural algorithm.

gwern 9y ago

> question for someone who has time to read the paper: can you train it to master chess and go at the same time? or is it one or the other? I'm assuming the latter. I'm sure you could with a multi-headed NN. But what would be the point? There's very little transfer of knowledge between the games, especially once you get past the very most basics.

thanatropism 9y ago
The point is that real problem domains are not neatly partitioned and labeled. I don't know what kind of input the NN itself gets, but computer vision is enough to translate a photo of a chessboard to a usable symbolic representation. But it would be nice to already have a black box-ish computer program that figures out what's the game at hand and how to play it. The next variation is have the adversary start playing a chess variant and have the machine recognize it (assuming honesty) and play it to significant skill. Then "real life Pong" where the size and aerodynamics of the ball are unknown to it. This is the gist of human intelligence: answering questions is significantly easier than figuring out what the question is.

jmmcd 9y ago

> Go back to an empty slate, with the same exactly "untrained" AG0, and now teach it Go, to the same result. No fine-tuning for the domain of the game you are training -- it is general(ized). Not quite -- different input features, which implies slightly different network architecture at least at the front.