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Google's AlphaGo AI can teach itself to master games like chess

No humans required. Google's DeepMind team has already advanced its AlphaGo AI to dominate Go without human input, but now the system is clever enough to master other board games without intervention. Researchers have developed a more generalized system for AlphaGo Zero that can train itself to achieve "superhuman" skill in chess, Shogi (a Japanese classic) and other game types knowing only the rules, all within less than a day. It doesn't need example games or other references. This doesn't mean that DeepMind has developed a truly general purpose, independent AI... yet. Chess and Shogi were relatively easy tests, as they're simpler than Go. It'll be another thing entirely to tackle complex video games like StarCraft II, let alone fuzzier concepts like walking or abstract thought. There's also the question of speed: less than 24 hours works for board games, but that's too slow for situations where AI needs to adapt on the spot. Even so, this is a major step toward AI that can accomplish any task with only minimal instructions. Robots and self-driving cars in particular may need to learn how to navigate unfamiliar environments without the luxury of pre-supplied training material. If nothing else, chess champions have one more reason to be nervous.

Google DeepMind repurposed its AlphaGo AI to beat the best chess and shogi bots

Google-owned DeepMind put out a new paper that outlines how the team took the machine learning system that created AlphaGo and built a new system that tackled chess and shogi, beating the top programs at each game. The program, called AlphaZero, also beat its predecessor, AlphaGo Zero. It was a logical next move for DeepMind. Chess and shogi (a chess-like board game that originated in Japan) are both games with computer programs that have already beaten top human players. AlphaZero beat both Stockfish, which is at the top of the game in chess, and Elmo, which is the best program at playing shogi. The program was trained to do that solely by playing itself, through a process known as reinforcement learning, without any foreknowledge except certain key information about the rules of each game, like how each piece is allowed to move. While AlphaGo (including AlphaGo Zero, which relied on self-play reinforcement learning for training) was built especially for Go, AlphaZero was designed to be far more flexible. That general-purpose architecture could provide a blueprint for how to develop future AI systems both for playing games and for solving other problems with clear rules and objectives like designing medicines. DeepMind trained three separate instances of AlphaZero, one each for Go, shogi, and chess. The chess system played 44 million games against itself, while the shogi system played 24 million games and the Go system played through 21 million games. AlphaZeros dominance wasnt assured. There are a number of key differences between Go and the two other games DeepMind selected. Both chess and shogi have restrictions on how different pieces can move, and the board in either game is not rotation independent like it is in Go. Whats more, captured pieces in shogi then become available for an opponent to place on the board. AlphaZeros main algorithm also had to change. Because the modern game of Go doesnt allow for draws, AlphaZeros algorithm had to adapt from optimizing for a win to optimizing for the best outcome, taking draws into account for chess. Some interesting trends emerged through all of the systems testing, though: It never lost a game of chess out of a 100-game match against Stockfish. When playing white, it won 25 times and drew 25 times. It won three times and drew 47 times when playing black. (Thats not unusual — there is a significant first-move advantage in chess.) AlphaZero also learned some of the most popular opening moves in chess through its self-play, which isnt necessarily surprising given the limited number of potential opening moves compared to later in the game, but it shows how quickly a computer can pick up knowledge about chess that was accumulated by humans over the course of years. AlphaZeros games against Elmo were more lopsided, but showed some weakness. The DeepMind system lost five times as white and three times as black. Shogi is a harder game than chess, since its played on a larger board, leading to to higher computational complexity. Go was the closest contest. While AlphaZero won more games than it lost playing either first or second, its predecessor AlphaGo Zero picked up 19 wins playing first and 21 wins playing second. Its unclear if well get to see how AlphaZero will measure up with human competitors. Elmo and Stockfish have beaten top human players, so DeepMind felt comfortable calling the systems performance superhuman. The company said that AlphaGo would retire from playing against people earlier this year, after handily defeating a set of flesh-and-blood competitors.