AlphaGo

AlphaGo is a computer program developed by DeepMind, a subsidiary of Google, designed to play the board game Go. It is notable for being the first computer program to defeat a professional human Go player. The game of Go is known for its immense complexity, with a vastly larger number of possible moves than chess. AlphaGo achieved its breakthrough by combining deep neural networks with a Monte Carlo tree search algorithm. The neural networks were trained on millions of historical Go games played by humans, learning to evaluate board positions and predict promising moves. The Monte Carlo tree search then used these evaluations to explore possible game continuations and select the best move.

In 2016, AlphaGo famously defeated European Go champion Fan Hui. This was followed by an even more significant victory in 2016 against Lee Sedol, one of the world's top Go players, in a highly publicized match. This event was widely seen as a major milestone in artificial intelligence research. Subsequent versions of AlphaGo, such as AlphaGo Zero and AlphaZero, were developed which learned to play Go (and other games like chess and shogi) solely through self-play, without any human game data, further demonstrating the power of deep learning and reinforcement learning. AlphaGo's success has had a profound impact on the field of AI, showcasing the potential for machine learning to tackle complex strategic problems.