11–20 of 282 posts
Very happy to see this result. It's like a moral victory for humans, as alphago is more human like (discounting montecarlo search) than stockfish. Maybe deep learning will give us the next Euler, Newton, or Einstein.
Not that there's anything wrong with that; AlphaGo Zero supposedly optimized for the "just enough" win rather than the crushing win. It doesn't even mean Stockfish is doomed--I suspect Stockfish could beat it in a future heads up match provided that Zero didn't have time to retrain, but that a retrained Zero (having the benefit of optimizing against a new Stockfish) would be able to supersede it once again.
Certainly a significant achievement. Also, kind of interesting that the AlphaGo team spent a lot of energy to convince us Go is much harder than Chess, only to turn around and tell us that it is amazing that it can also win at Chess.
> 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…
A lot of the graphs in the paper seem to level out as they hit the level of the opponent. It makes me wonder to what extent AlphaGo Zero is merely optimizing to beat flaws in existing opponents' current implementations (even if "existing opponents" == all available opponents' data and algorithms today) rather than generalizable insights into the underlying game. Because wouldn't you expect that unless we are at the t…
A lot of the graphs in the paper seem to level out as they hit the level of the opponent. It makes me wonder to what extent AlphaGo Zero is merely optimizing to beat flaws in existing opponents' current implementations (even if "existing opponents" == all available opponents' data and algorithms today) rather than generalizable insights into the underlying game. Because wouldn't you expect that unless we are at the t…
DM is no longer investing much in the AG research program; Silver said the team has been disbanded already. If you look at the Go graph in this or the first AG0 paper, Zero was still getting better at Go when they shut it down, it hadn't converged. They just didn't want to tie up the TPUs. I don't think it's a coincidence that the graphs tend to stop after they reach superiority.
(Also, as Houshalter says, one of the critical aspects is that this is pure self-play ie the NNs never play against the existing engines except for evaluation. So it's all independent from-scratch reinvention.)
Certainly a significant achievement. Also, kind of interesting that the AlphaGo team spent a lot of energy to convince us Go is much harder than Chess, only to turn around and tell us that it is amazing that it can also win at Chess.
> 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…
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.
I wonder if being an expert at one game makes it easier to be an expert at another. If so, then maybe the examples are datasets, and convergence would be able to complete new tasks after a few examples.
[1] https://en.wikipedia.org/wiki/List_of_chess_variants
The answer may be that it is hard enough to become an expert at anything, but there may be some serendipitous (how to make this precise?) overlap.
A lot of the graphs in the paper seem to level out as they hit the level of the opponent. It makes me wonder to what extent AlphaGo Zero is merely optimizing to beat flaws in existing opponents' current implementations (even if "existing opponents" == all available opponents' data and algorithms today) rather than generalizable insights into the underlying game. Because wouldn't you expect that unless we are at the t…