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The proof of the pudding will be in the applications that come from this specific line of research. I predict that there will be folks pointing to anything with

by sgt101 11y ago

The proof of the pudding will be in the applications that come from this specific line of research. I predict that there will be folks pointing to anything with an activation network, or a search algorithm, and shouting "look it works like alpha-go". What is the class of problems beyond go and closed world games which this work attacks?

whitegrape 11y ago
More like anything with a guided search algorithm will be said to be like AlphaGo. In general I think the field may be warming up to the idea that "whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought."[1] MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. [1] http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/ http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/
- gcp 11y ago
  MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. The tricky part is that using the "best (rather than better) than random guidance" in the Monte Carlo simulations makes the performance worse. We don't understand very deeply why that is.
gcp 11y ago
The proof of the pudding will be in the applications that come from this specific line of research. What is "this specific line of research" though? Monte Carlo simulations with tree search? This is a bit tricky because simulation the playout requires a "fixed" world or something that can be simplified to it. Deep Convolutional Neural Networks? Those are very broadly applicable, most famously in image recognition, in fact one reason why Google and Facebook were on this is because they are probably using them a ton...

by sgt101 11y ago

whitegrape 11y ago

More like anything with a guided search algorithm will be said to be like AlphaGo. In general I think the field may be warming up to the idea that "whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought."[1] MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. [1] http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/ http://lesswrong.com/lw/vq/the_weighted_majority_algorithm/

gcp 11y ago
MCTS does exceptionally well at Go all by itself. When coupled with something (in this case trained neural nets) that can give it better-than-random guidance in exploring the state space, it's no surprise it does even better. The tricky part is that using the "best (rather than better) than random guidance" in the Monte Carlo simulations makes the performance worse. We don't understand very deeply why that is.

gcp 11y ago

The proof of the pudding will be in the applications that come from this specific line of research. What is "this specific line of research" though? Monte Carlo simulations with tree search? This is a bit tricky because simulation the playout requires a "fixed" world or something that can be simplified to it. Deep Convolutional Neural Networks? Those are very broadly applicable, most famously in image recognition, in fact one reason why Google and Facebook were on this is because they are probably using them a ton...