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I don't know. I see a lot of papers which are getting great results on tough domains like program writing using the idea of tree search guided by NNs which may
by gwern 10y ago
I don't know. I see a lot of papers which are getting great results on tough domains like program writing using the idea of tree search guided by NNs which may be very old but AlphaGo brought to everyone's attention by demonstrating what modern NNs could do in it. Any domain which is tree or DAG structured and you have a simulator for can be tackled much more effectively now.
- nickpsecurity 10y agoHeuristic search is a classic technique for getting things done in AI. Just wasn't good on recognizing the value of some moves. Aka scoring. Today's AI people have ANN's that are great at scoring. Their tunnel vision just made them focus almost exclusively using such tech. Then, a team combines two proven methods to do what each are good at. Big results follow. These fads and tunnel vision happen all the time. The most interesting stuff often happens when a talented person inside the bubble opens their mind to bring in something outside it.
- feral 10y agoI agree with this; I've seen people writing off AlphaGo as if it was very little new conceptually over MCTS (but just with more hardware). But the power of the NN was surprising. There's a paragraph from the AlphoGo paper that I think speaks to this: "We also tested against the strongest open-source Go program, Pachi, a sophisticated Monte-Carlo search program, ranked at 2 amateur dan on KGS, that executes 100,000 simulations per move. Using no search at all, the RL policy network won 85% of games against Pachi."