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It is typical for ML systems to surpass human performance while having very different characteristics in what it got right/wrong. For example in ImageNet, DCNN got a lot of points from distinguishing different breeds of dogs with subtle visual differences which are hard for human without training. I think AlphaGo is also demonstrating some of these non-human characteristics as a consequence of the Monte Carlo Tree Search and optimization objective such as the brilliant move as well as the obvious slack moves/mistakes mentioned by the commentators. I suspect that we are not close to the perfect game, as proving a perfect play requires expanding the enormous search tree and we do not have any analytical solutions nor brute force solutions.

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