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.
This might actually be the most interesting thing about AlphaGo. Domain experts who have looked at its games have marveled most at how truly "book-smart" it is. Even though it has not shown a lot of creativity or surprising moves (indeed, it was comparatively weak at the start of Game 1), it has fully internalized its training and can always come up with the "standard" play.
Not necessarily - there might be a speed vs. energy-per-op tradeoff, where neurons specialize in quick but energy-intensive computation, while neuroglia just chug along in the background. We definitely see such a tradeoff in silicon devices.
Do you have links to such analyses? I'd be interested in reading them.
EDIT: Ah, I guess you were referring to this: https://www.reddit.com/r/MachineLearning/comments/43fl90/synopsis_of_top_go_professionals_analysis_of/