[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

[...]

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

I included the word "sufficient" as an ass-covering move, because one facet of the problem is we don't really know what will serve as a "sufficient" amount of training data in what context.

But, what specific types of tasks do you think machines still can't do, given sufficient training data? If your answer is something like "physics research," I would rejoinder that if you could generate training data for that job, a machine could do it.

I don't see how we anything like know that deep NNs with ‘sufficient training data’ would be sufficient for all problems. We've seen them be sufficient for many different problems and can expect them to be sufficient for many more, but all?

5Lumifer10y

Grand pronouncements with an ass-covering move look silly :-) One obvious problem is that you are assuming stability. Consider modeling something that changes (in complex ways) with time -- like the economy of the United States. Is "training data" from the 1950s relevant to the currrent situation? Generally speaking, the speed at which your "training data" gets stale puts an upper limit on the relevant data that you can possibly have and that, in turn, puts an upper limit on the complexity of the model (NNs included) that you can build on its basis.

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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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