The history of artificial intelligence is a procession of one-trick ponies. Over decades researchers have crafted a series of super-specialized programs to beat humans at tougher and tougher games. They conquered tic-tac-toe, checkers, and chess. Most recently, Alphabet’s DeepMind research group shocked the world with a program called AlphaGo that mastered the Chinese board game Go. But each of these artificial champions could play only the game it was painstakingly designed to play.
DeepMind has now revealed the first multi-skilled AI board-game champ. A paper posted late Tuesday describes software called AlphaZero that can teach itself to be super-human in any of three challenging games: chess, Go, or Shogi—a game sometimes dubbed Japanese chess.
AlphaZero couldn’t learn to play all three games at once. But the ability of one program to learn three different, complex games to such a high level is striking because AI systems—including those that can “learn”—typically are extremely specialized, honed to tackle a particular problem. Even the best AI systems can’t generalize between problems—one reason why many experts say we still have a long way to go before machines rival human abilities.
AlphaZero could be a small step towards making AI systems less specialized. In a tweet Tuesday, NYU professor Julian Togelius noted that truly generalized AI remains a way off, but called DeepMind’s paper “excellent work.”
AlphaZero can learn to play each of the three games in its repertoire from scratch, although it needs to be programmed with the rules of each game. The program becomes expert by playing against itself to improve its skills, experimenting with different moves to discover what leads to a win.