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.
The difference with Markov models is they tend to overfit at that level. At 20 characters deep, you are just copy and pasting large sections of existing code and language. Not generating entirely unseen samples. You can do a similar thing with RNNs, by training them only on one document. They will be able to reproduce that document exactly, but nothing else.
To properly compare with a markov model, you'd need to first tune it so it doesn't overfit. That is, when it's looking at an entirely unseen document, it's guess of what the next character should be is most likely to be correct. The best setting for that is probably only 3-5 characters, not 20. And when you generate from that, the output will be much less legible. (And even that's kind of cheating, since markov models can't give any prediction for sequences it's never seen before.)
Generating samples is just a way to see what patterns the RNN has learned. And while it's far from perfect, it's still pretty impressive. It's learned a lot about syntax, a lot about variable names, a lot about common programming idioms, and it's even learned some English from just code comments.
In NLP applications where Markov language models are used, such as speech recognition and machine translation, the typical setting is 3 to 5 words. 20 characters correspond to about 4 English words, which is in this range.
Anyway, I agree that in this case the order-20 Markov model seems to overfit (Googling some lines from the snippets in the post often locates them in an original source file, which doesn't happen as often with the RNN snippets). This may be due to the lack of regulariza... (read more)