AlphaGo taught itself how to win, but without humans it would have run out of time

South Korean professional Go player Lee Sedol after finishing the fourth match of the Google DeepMind Challenge Match. Photograph: Lee Jin-man/AP AlphaGo, the board-game-playing AI from Google’s DeepMind subsidiary, is one of the most famous examples of deep learning – machine learning using neural networks – to date. So it may be surprising to learn that some of the code that led to the machine’s victory was created by good old-fashioned humans. The software, which beat Korean Go Champion Lee Sedol 4–1 in March, taught itself to play the ancient Asian game by running millions of simulations against itself. AlphaGo is one of two neural networks, taught by a mixture of supervised learning (studying previous games played by humans) and reinforcement learning (playing against itself and learning from its mistakes).…


Link to Full Article: AlphaGo taught itself how to win, but without humans it would have run out of time