Alpha Go
AlphaGo is a computer program developed by DeepMind Technologies which uses deep neural networks to play the board game Go. It was the first program to defeat a human professional Go player, and went on to defeat the world champion, Lee Sedol, in a five-game match. AlphaGo's success was due to its ability to learn from its mistakes and improve its gameplay through reinforcement learning. The program was trained on a large dataset of expert Go games, and used a combination of Monte Carlo tree search and deep neural networks to make its moves. AlphaGo's success has had a significant impact on the field of artificial intelligence, demonstrating the potential of deep learning and reinforcement learning to solve complex problems.
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