Google DeepMind's AlphaGo: How it works

Between 9 and 15 March 2016, a five game competition took place between Lee Sedol, the second-highest ranking professional Go player, and AlphaGo, a computer program created by Google's DeepMind subsidiary. The competition was high-stake: a prize of one million dollars was put up by Google. How exactly did AlphaGo manage to do it? All I could figure out was that machine learning was involved. Having a PhD in machine learning myself, I decided to go through the trouble and read the paper that DeepMind published on the subject. I will do my best to explain how it works in this blog post. I also read different opinions of how much a big deal this win is, and I will have some things to say about that myself (spoiler: I think it's a pretty big deal). Go and chess are very popular board games, which are similar in some respects: both are played by two players taking turns, and there is no random element involved (no dice rolling, like in backgammon). In 1997, Garry Kasparov was defeated by Deep Blue, a computer program written by IBM, running on a supercomputer. This was the first time that a reigning world chess champion was defeated by a computer program in tournament conditions.