101–110 of 282 posts
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> So in a way, the real triumph of the AlphaGo series was the TPU and GPU army. Eh. It's still searching many fewer positions than Stockfish is.
It searches fewer positions because it decides where to search using 4 TPUs, which are 180 teraflops each according to Google.
Speed probably made the initial self play training quicker though.
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Are there popular games where the best human players are not near the limit of what is possible? Obviously you can construct one to be hard for humans (large 3SAT problems, or even big arithmetic problems), but I wonder if there is one that people enjoy.
Humans are nowhere near the limit of what is possible in chess, as evidenced by how much better computers are at the game.
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It's not. It learns entirely through self play and never learns from playing it's opponent. Diminishing returns isn't unusual and happens in every domain. These AIs are probably playing close to the limit of what is possible, just not quite there yet.
Are there popular games where the best human players are not near the limit of what is possible? Obviously you can construct one to be hard for humans (large 3SAT problems, or even big arithmetic problems), but I wonder if there is one that people enjoy.
I smell a rat. The paper says: 'AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi' In the first game Stockfish's, 9. Qe1 is one of the strangest moves I've ever seen, which would never be considered by a human, let alone a superhuman. 11. Kh1 also makes little sense, but is not as bad. My Stockfish sees it as losing 0.2 pawns, which makes it highly suspect in such a positio…
This is an incredible demonstration that the AG Zero expert iteration method is a general method. If you go back to the discussions of AG Zero lo a month ago, there was a lot of skepticism that NNs would ever challenge Stockfish et al - they are just too good, too close to perfection, and chess not well suited for MCTS and NNs. Well, it turns out that AG Zero doesn't work as well in chess: it works better as it only…
>it only takes 4 hours of training to beat Stockfish In that time I figure they used the equivalent of about 1000 cpu-years. Imagine the things we'll be able to achieve as we can do more and more computation in less and less time.
Some scientist say the brain has a power of several petaflops, so if you use this, I guess the design of stockfish was way less efficient.
You can't really compare things to cpu years, it doesn't make sense. Power consumption would be a better metric I think.
Maybe I'm missing some things but:
- Are 1st gen TPUs even accessible ? You have to fill out a form to learn more about those second generation TPUs: https://cloud.google.com/tpu/
- I can't find the source code
This does not look like a scientific paper, but a (very impressive) tech demo.
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There's definitely nothing fishy going on, although it'd be nice to see a fully loaded Stockfish on its full complement of 512 cores and a proper endgame tablebase to really slog it out with AlphaZero.
Back to zero at 41 ply... I shan't give a running commentary anymore.
This is an incredible demonstration that the AG Zero expert iteration method is a general method. If you go back to the discussions of AG Zero lo a month ago, there was a lot of skepticism that NNs would ever challenge Stockfish et al - they are just too good, too close to perfection, and chess not well suited for MCTS and NNs. Well, it turns out that AG Zero doesn't work as well in chess: it works better as it only…
Let's break this down and consider things carefully. To informed researchers, what is most surprising here is not that the AlphaGo Zero algorithm beat stockfish but that MCTS managed to outperform Alpha-beta search. I'll venture a hypothesis as to why this was. Informed skepticism would have discounted MCTS against alpha-beta search but wouldn't have put much stock into the idea that Neural Networks couldn't learn be…
I know it's complicated, between the hardware differences, search method used, etc. But when claiming that NNs beat hand crafted evaluation functions, keep in mind that Stockfish is probably are not the best choice to compare, since it has made different tradeoff choices to get more depth (which goes back to search method and hardware choices).
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Shogi, chess and Go are "perfect information games", meaning you can see the whole game state. It's a whole different thing to be able to solve games where you don't see everything (based on uncertainty).
Is it really though? A big class of imperfect information games can be modeled by having a record of everything the agent has seen so far. Then it has exactly the same, if not more, information available than a human player in the same position. We know that with equal information AIs can make better decisions than humans (see also, AlphaGo :] ) so at that point the AI could reasonably be expected to achieve superhum…
Earlier this year, DeepStack, a system combining neural nets with search, competed live against humans without any side being dominant. Search policy guided training might improve its results, which are impressive compared to even 5 years ago, but this highlights how much more demanding imperfect information games are.