Google’s DeepMind—the group that brought you the champion game-playing AIs AlphaGo and AlphaGoZero—is back with a new, improved, and more-generalized version. Dubbed AlphaZero, this program taught itself to play three different board games (chess, Go, and shogi, a Japanese form of chess) in just three days, with no human intervention.
A paper describing the achievement was just published in Science. “Starting from totally random play, AlphaZero gradually learns what good play looks like and forms its own evaluations about the game,” said Demis Hassabis, CEO and co-founder of DeepMind. “In that sense, it is free from the constraints of the way humans think about the game.”
Chess has long been an ideal testing ground for game-playing computers and the development of AI. The very first chess computer program was written in the 1950s at Los Alamos National Laboratory, and in the late 1960s, Richard D. Greenblatt’s Mac Hack IV program was the first to play in a human chess tournament—and to win against a human in tournament play. Many other computer chess programs followed, each a little better than the last, until IBM’s Deep Blue computer defeated chess grand master Garry Kasparov in May 1997.
As Kasparov points out in an accompanying editorial in Science, these days your average smartphone chess playing app is far more powerful than Deep Blue. So AI researchers turned their attention in recent years to creating programs that can master the game of Go, a hugely popular board game in East Asia that dates back more than 2,500 years. It’s a surprisingly complicated game, much more difficult than chess, despite only involving two players with a fairly simple set of ground rules. That makes it an ideal testing ground for AI.
