DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.
Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history
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But one game has thwarted A.I. research thus far: the ancient game of Go.
His argument proves too much.
You could easily transpose it for the time when Checkers or Chess programs beat professional players: back then the "keystone, foundational aspect" of intelligence was thought to be the ability to do combinatorial search in large solution spaces, and scaling up to AGI was "just" a matter of engineering better heuristics. Sure, it didn't work on Go yet, but Go players were not using a different cortical algorithm than Chess players, were they?
Or you could transpose it for the time when MCTS Go programs reached "dan" (advanced amateur) level. They still couldn't beat professional players, but professional players were not using a different cortical algorithm than advanced amateur players, were they?
AlphaGo succeded at the current achievement by using artificial neural networks in a regime where they are know to do well. But this regime, and the type of games like Go, Chess, Checkers, Othello, etc. represent a small part of the range of human cognitive tasks. In fact, we probably find this kind of board games fascinating precisely because they are very different than the usual cognitive stimuli we deal with in everyday life.
It's tempting to assume that the "keystone, foundational aspect" of intelligence is learning essentially the same way that artificial neural networks learn. But humans can do things like "one-shot" learning, learning from weak supervision, learning in non-stationary environments, etc. which no current neural network can do, and not just because a matter of scale or architectural "details". Researchers generally don't know how to make neural networks, or really any other kind of machine learning algorithm, do these things, except with massive task-specific engineering. Thus I think it's fair to say that we still don't know what the foundational aspects of intelligence are.
In the brain, the same circuitry that is used to solve vision is used to solve most of the rest of cognition - vision is 10% of the cortex. Going from superhuman vision to superhuman Go suggests superhuman anything/everything is getting near.
The reason being that strong Go requires both deep slow inference over huge data/time (which DL excels in, similar to what the cortex/cerebellum specialize in), combined with fast/low data inference (the MCTS part here). There is still much room for improvement in generalizing beyond current MCTS techniques, and bet... (read more)