Google’s New AI Is a Master of Games, but How Does It Compare to the Human Mind?

For humans, chess may take a lifetime to master. But Google DeepMind’s new artificial intelligence program, AlphaZero, can teach itself to conquer the board in a matter of hours.

Building on its past success with the AlphaGo suite—a series of computer programs designed to play the Chinese board game Go—Google boasts that its new AlphaZero achieves a level of “superhuman performance” at not just one board game, but three: Go, chess, and shogi (essentially, Japanese chess). The team of computer scientists and engineers, led by Google’s David Silver, reported its findings recently in the journal Science.

“Before this, with machine learning, you could get a machine to do exactly what you want—but only that thing,” says Ayanna Howard, an expert in interactive computing and artificial intelligence at the Georgia Institute of Technology who did not participate in the research. “But AlphaZero shows that you can have an algorithm that isn’t so [specific], and it can learn within certain parameters.”

AlphaZero’s clever programming certainly ups the ante on gameplay for human and machine alike, but Google has long had its sights set on something bigger: engineering intelligence.

The researchers are careful not to claim that AlphaZero is on the verge of world domination (others have been a little quicker to jump the gun). Still, Silver and the rest of the DeepMind squad are already hopeful that they’ll someday see a similar system applied to drug design or materials science.

So what makes AlphaZero so impressive?

Gameplay has long been revered as a gold standard in artificial intelligence research. Structured, interactive games are simplifications of real-world scenarios: Difficult decisions must be made; wins and losses drive up the stakes; and prediction, critical thinking, and strategy are key.

Encoding this kind of skill is tricky. Older game-playing AIs—including the first prototypes of the original AlphaGo—have traditionally been pumped full of codes and data to mimic the experience typically earned through years of natural, human gameplay (essentially, a passive, programmer-derived knowledge dump). With AlphaGo Zero (the most recent version of AlphaGo), and now AlphaZero, the researchers gave the program just one input: the rules of the game in question. Then, the system hunkered down and actively learned the tricks of the trade itself.

This strategy, called self-play reinforcement learning, is pretty much exactly what it sounds like: To train for the big leagues, AlphaZero played itself in iteration after iteration, honing its skills by trial and error. And the brute-force approach paid off. Unlike AlphaGo Zero, AlphaZero doesn’t just play Go: It can beat the best AIs in the business at chess and shogi, too. The learning process is also impressively efficient, requiring only two, four, or 30 hours of self-tutelage to outperform programs specifically tailored to master shogi, chess, and Go, respectively. Notably, the study authors didn’t report any instances of AlphaZero going head-to-head with an actual human, Howard says. (The researchers may have assumed that, given that these programs consistently clobber their human counterparts, such a matchup would have been pointless.)

AlphaZero was also able to trounce Stockfish (the now unseated AI chess master) and Elmo (the former AI shogi expert) despite evaluating fewer possible next moves on each turn during game play. But because the algorithms in question are inherently different, and may consume different amounts of power, it’s difficult to directly compare AlphaZero to other, older programs, points out Joanna Bryson, who studies artificial intelligence at the University of Bath in the United Kingdom and did not contribute to AlphaZero.

Google keeps mum about a lot of the fine print on its software, and AlphaZero is no exception.