Very impressive. Since there is a ton of hype about this and many media stories (at least NYTimes, with no citation at all) saying that this came 'a decade early', I think its worth looking over Yann LeCun retrospective on research in this area (https://www.facebook.com/yann.lecun/posts/10153340479982143). Clearly he was saying all this to preface the results of Facebook research in comparison to Google's, but I still think it is a very good overview of the history and shows the ideas did not come about suddenly. Quoting a few key things since the whole things is very long:
"The idea of using ConvNet for Go playing goes back a long time. Back in 1994, Nicol Schraudolph and his collaborators published a paper at NIPS that combined ConvNets with reinforcement learning to play Go. But the techniques weren't as well understood as they are now, and the computers of the time limited the size and complexity of the ConvNet that could be trained. More recently Chris Maddison, a PhD student at the University of Toronto, published a paper with researchers at Google and DeepMind at ICLR 2015 showing that a large ConvNet trained with a database of recorded games could do a pretty good job at predicting moves. The work published at ICML from Amos Storkey's group at University of Edinburgh also shows similar results. Many researchers started to believe that perhaps deep learning and ConvNets could really make an impact on computer Go.
...
Clearly, the quality of the tactics could be improved by combining a ConvNet with the kind of tree search methods that had made the success of the best current Go bots. Over the last 5 years, computer Go made a lot of progress through Monte Carlo Tree Search. MCTS is a kind of “randomized” version of the tree search methods that are used in computer chess programs. MCTS was first proposed by a team of French researchers from INRIA. It was soon picked up by many of the best computer Go teams and quickly became the standard method around which the top Go bots were built. But building an MCTS-based Go bots requires quite a bit of input from expert Go players. That's where deep learning comes in.
...
A good next step is to combine ConvNets and MCTS with reinforcement learning, as pioneered by Nicol Schraudolph's work. The advantage of using reinforcement learning is that the machine can train itself by playing many games against copies of itself. This idea goes back to Gerry Tesauro's “NeuroGammon,” a computer backgammon player that combined neural nets and reinforcement learning that beat the backgammon world champion in the early 1990s. We know that several teams across the world are actively working on such systems. Ours is still in development.
"The idea of using ConvNet for Go playing goes back a long time. Back in 1994, Nicol Schraudolph and his collaborators published a paper at NIPS that combined ConvNets with reinforcement learning to play Go. But the techniques weren't as well understood as they are now, and the computers of the time limited the size and complexity of the ConvNet that could be trained. More recently Chris Maddison, a PhD student at the University of Toronto, published a paper with researchers at Google and DeepMind at ICLR 2015 showing that a large ConvNet trained with a database of recorded games could do a pretty good job at predicting moves. The work published at ICML from Amos Storkey's group at University of Edinburgh also shows similar results. Many researchers started to believe that perhaps deep learning and ConvNets could really make an impact on computer Go.
...
Clearly, the quality of the tactics could be improved by combining a ConvNet with the kind of tree search methods that had made the success of the best current Go bots. Over the last 5 years, computer Go made a lot of progress through Monte Carlo Tree Search. MCTS is a kind of “randomized” version of the tree search methods that are used in computer chess programs. MCTS was first proposed by a team of French researchers from INRIA. It was soon picked up by many of the best computer Go teams and quickly became the standard method around which the top Go bots were built. But building an MCTS-based Go bots requires quite a bit of input from expert Go players. That's where deep learning comes in.
...
A good next step is to combine ConvNets and MCTS with reinforcement learning, as pioneered by Nicol Schraudolph's work. The advantage of using reinforcement learning is that the machine can train itself by playing many games against copies of itself. This idea goes back to Gerry Tesauro's “NeuroGammon,” a computer backgammon player that combined neural nets and reinforcement learning that beat the backgammon world champion in the early 1990s. We know that several teams across the world are actively working on such systems. Ours is still in development.
...
This is an exciting time to be working on AI."