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>And let's take a look at AlphaGo, how would you do that with SVMs or decision trees? This is a poor example. There are Go programs that work very well and use
by romaniv 10y ago
>And let's take a look at AlphaGo, how would you do that with SVMs or decision trees?
This is a poor example. There are Go programs that work very well and use exclusively MCTS without any neural networks.
- deepnotderp 10y agoThis is a fallacy, AlphaGo didn't increase very much with additional hardware at all, it's elo would still have been higher than lee se-dol's elo. And yes, MCTS worked well for Go, but notice how the combination boosted it to new levels? And deep nets without MCTS also worked very well on Go, outperforming MCTS only approaches (by FAIR and was the top before being dethroned by AlphaGo).
- romaniv 10y agoI edited the post because I thought someone will nitpick on my comment about hardware, but it seems you caught the older version. > This is a fallacy, AlphaGo didn't increase very much with additional hardware at all It did. Please take a look at the page 36 of the original paper. (https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf) The performance did not grow linearly, but the increases were vert significant. >it's elo would still have been higher than lee se-dol's elo I seriously doubt it would win that match. One-GPU version lost to 8-GPU version around 95% of all games. The paper is somewhat misleading in estimating relative performance of AlphaGo compared to older programs, because (as far as I know) while all of them were run on the same hardware, only AlphaGo used GPU acceleration.