The first major conquest of artificial intelligence was chess. The game has a dizzying number of possible combinations, but it was relatively tractable because it was structured by a set of clear rules. An algorithm could always have perfect knowledge of the state of the game and know every possible move that both it and its opponent could make. The state of the game could be evaluated just by looking at the board.

But many other games aren't that simple. If you take something like Pac-Man , then figuring out the ideal move would involve considering the shape of the maze, the location of the ghosts, the location of any additional areas to clear, the availability of power-ups, etc., and the best plan can end up in disaster if Blinky or Clyde makes an unexpected move. We've developed AIs that can tackle these games, too, but they have had to take a very different approach to the ones that conquered chess and Go.

At least until now. Today, however, Google's DeepMind division published a paper describing the structure of an AI that can tackle both chess and Atari classics.

Enlarge (credit: Dimitri Otis | Getty Images )

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty.

“It’s a kind of creation,” he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion.

Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote. “For quite a number of games on the highest level, half of the game—sometimes a full game—is played out of memory,” Kramnik says. “You don’t even play your own preparation; you play your computer’s preparation.”