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Regardless of how good the deep learning network is on its own, the algorithm described in the DeepMind paper is an improved tree search.
by panic 11y ago
Regardless of how good the deep learning network is on its own, the algorithm described in the DeepMind paper is an improved tree search.
- Eliezer 11y agoI don't think it's accurate to call AlphaGo 'improved tree search' the way that Deep Blue was improved tree search. You could with equal justice call it an improved neural net.
- argonaut 11y agoThe correct characterization is that it's a hybrid of deep learning and tree search. It's also a hybrid in the feature representation for the algorithm. There are raw features (board state), but there are also handcrafted features for the tree search.
- panic 11y agoI'd say both characterizations are accurate. Neither the tree search nor the neural net could have accomplished this on their own. But the essential interface to the algorithm is the tree search: it's picking the best move from a set of legal moves determined by formal game rules. The real world doesn't follow formal game rules. I find it difficult to see the progression from this victory to some kind of real-world AI takeover.
- igravious 11y ago> The real world doesn't follow formal game rules. Really? Why not?
- argonaut 11y agoInfinite state/belief/world space. Infinite action space. It's not so much that there aren't rules (there are - physics), it's that the complexity of the full set of rules is exponential or super-exponential.
- ogrisel 11y agoYou can do math with continuous and infinite dimensional spaces.
- argonaut 11y agoAnd? This does not address my argument that the complexity is beyond-combinatorially explosive (infinite spaces). I'm not talking about the space of possible board states. I'm talking about merely the set of all possible actions. EDIT: clarified my language to address below reply.
- eru 11y agoGo is combinatorially explosive, too.
- deleted 11y ago[deleted]
- ogrisel 11y ago...and it's possible to train learning agents to sense and interact with a world described by high dimensional continuous vector spaces, for instance using conv nets (for sensing audio / video signals) and actor-critic to learn an continuous policy: http://arxiv.org/abs/1509.02971 http://arxiv.org/abs/1509.02971 The fact that the (reinforcement) learning problem is hard or not is not directly related to whether the observation and action spaces are discrete or continuous.
- kqr 11y agoSure, but when you say "tree search" people think of the traditional kind of expert system-based tree search. Traditional tree search methods prune trees by approximating subtrees according to some very specific rules decided on by human experts. This means in a tree search system, the computer cannot actually evaluate a position any better than the humans that designed it. The way it performs better is by running very many of these evaluations deep down in the tree. When you're talking about some other kind of evaluation function, such as Monte Carlo rollouts, you usually prefix that to the tree search (in the case of Monte Carlo rollouts, "Monte Carlo tree search" or MCTS) to indicate that besides the basic fundamental task common to almost all AIs (finding the optimal branches in a decision tree) it functions completely differently from the expert systems. So is the case with this program, which (in a first pass) approximates subtrees by a trained neural net, rather than Monte Carlo rollouts or an expert system. So using terminology that suggests classical expert system tree search is bound to cause confusion (as you noticed).
- brian_cloutier 11y agoForest for the trees. Monte Carlo Tree Search was necessary and itself a massive improvement over minimax but not sufficient for creating a Go program to challenge professional players. The true innovation here is the neural networks. Without those networks to guide it AlphaGo plays far worse than existing programs. The fact that those networks are sufficient is pretty incredible. We already knew that by inventing them we had created a very pure form of pattern recognition, but it's surprising that the pattern recognition coupled with some tree search seems to be all you need to play Go as well as humans. It's not impressive to you that we've now reproduced a piece of human intelligence, "intuition", which was previously considered out of reach?
- panic 11y agoIt's totally impressive! I'm excited for the future of DeepMind and to see what other kinds of things we can build from neural networks. I'm just saying that we have to be careful with what kind of intelligence we ascribe to an algorithm like this. An AI is not going to take over the world by impressing us with its game-playing skills.
- nl 11y agoDeepMind is the company/lab Google bought. The algorithm is more properly called AlphaGo.
- JabavuAdams 11y ago> An AI is not going to take over the world by impressing us with its game-playing skills. What if you train an AI to play an RTS where matter, energy, and time are the resources and the goal is to take over the world?
- argonaut 11y agoI'm not going to fear an AI whose idea of "the world" is a computer game, an AI that isn't even aware of the existence of the real world, and isn't even aware of the existence of the set of real world actions.
- zodiac 11y agoWell, by that logic, why can't you describe Lee Sedol as applying an "improved tree search" that happens to have really good move selection heuristics and static board evaluation function as well? After all, all good human Go players read ahead a few moves.