[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning

ESRogs

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.

http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html

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

[...]

But one game has thwarted A.I. research thus far: the ancient game of Go.

http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html

Yes, but as I wrote above, the problems of credit assignment, reward delay and noise are non-existent in this setting, and hence their work does not contribute at all to solving AI.

Credit assignment and reward delay are nonexistent? What do you think happens when one diffs the board strength of two potential boards?

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[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning

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[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning

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