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AlphaGo

AlphaGo is an artificial intelligence (AI) agent that is specialized to play Go, a Chinese strategy board game, against human competitors. AlphaGo is a Google DeepMind project.

The ability to create a learning algorithm that can beat a human player at strategic games is a measure of AI development. AlphaGo is designed as a self-teaching AI and plays against itself to master the complex strategic game of Go. There have been versions of AlphaGo that beat human players but new versions are still being created.

Go is a Chinese board game similar to chess with two players, one using black pieces and one white, placing a piece each turn. Pieces are placed on a grid that varies in size according to the level of play up to 19x19 placement points. The goal is to capture more territory (empty spaces) or enemy pieces by surrounding them with your pieces. Only positions that are horizontal and vertical relative to the players need to be covered to capture; it's not required that they all be diagonals. Either pieces or territory can be captured individually or in groups.

Chess may be a more famous board game with white and black pieces but Go has a googol more possible moves. The number of possible positions makes a traditional brute force approach, as was used with IBMs' Big Blue in chess, impossible with current computers. That difference in complexity of the problem required a new approach.

AlphaGo is based off a Monte Carlo algorithm tree search based looking at a list of possible moves from its machine-learned repertoire. Algorithms and learning differ among the various versions of AlphaGo. AlphaGo Master, the version that beat the world champion Go player Ke Jie, uses supervised learning. AlphaGo Zero, the unsupervised learning version of AlphaGo, learns by playing against itself. First, the AI plays randomly, then with increasing sophistication. Its increased sophistication is such that it consistently beats the Master version that dominates human players.

Watch SciShow cover AlphaGo in the video below:

This was last updated in April 2018

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