I agree. I don't find this result to be any more or less indicative of near-term AI than Google's success on ImageNet in 2012. The algorithm learns to map positions to moves and values using CNNs, just as CNNs can be used to learn mappings from images to 350 classes of dog breeds and more. It turns out that Go really is a game about pattern recognition and that with a lot of data you can replicate the pattern detection for good moves in very supervised ways (one could call their reinforcement learning actually supervised because the nature of the problem gives you credit assignment for free).
I think what this result says is thus: "Any tasks humans can do, an AI can now learn to do better, given a sufficient source of training data."
Games lend themselves to auto-generation of training data, in the sense that the AI can at the very least play against itself. No matter how complex the game, a deep neural net will find the structure in it, and find a deeper structure than human players can find.
We have now answered the question of, "Are deep neural nets going to be sufficient to match or exceed task-specific human performance at any...
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.