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I'm guessing that AlphaGo has been doing more learning. Studying Sedol's games is probably a good platform but certainly not a plan to win - Jie needs to go in
by zamalek 10y ago
I'm guessing that AlphaGo has been doing more learning. Studying Sedol's games is probably a good platform but certainly not a plan to win - Jie needs to go in expecting the unexpected. Just look at how different the games were between Hui and Sedol.
To answer your question more directly: giving AlphaGo more hardware would only give it more CPU cycles to work with (faster decisions, not necessarily good ones). The true improvement will likely come from the very same source that humans get it: practice and mastery.
- kough 10y agoDoesn't faster hardware allow it to navigate more of the search tree, in effect giving it more time to find better decisions?
- _delirium 10y agoYes, the online hardware used during the game, vs. the offline hardware used for training the neural network, is mainly there to run a ton of monte carlo tree search (MCTS) iterations. MCTS tends to produce better estimates as you throw more compute power at it. In perfect-information, deterministic games, it theoretically converges on optimal play given infinite compute power, although in games with very large state spaces in practice that guarantee isn't too useful on its own, and the heuristic guidance provided by the NN is crucial.
- thomasahle 10y agoIn the paper they state that they tried giving it more power than the version that played Sedol, but found that it had rapidly decreasing marginal value, so they decided on a level that was pretty close to optimal.