Noam Brown was never very good at poker. But an artificially intelligent program he created became the first to beat the world’s top players in no-limit Texas Hold’em, the game’s most popular variant.

In recent years, machines have defeated humans in checkers, chess, and Go—known as “perfect information” games, where both players know the exact state of play at any given point. Imperfect information games like poker, where hidden cards introduce strategies like bluffing, add another level of complexity.

“When you introduce hidden information, all these past techniques just fall apart,” Brown says.

Most strategic interactions in the real world, after all, involve some form of hidden information. In the long run, Brown envisions his research leading to automated solutions to situations that are similar to hidden-information games—from managing traffic, to predicting the performance of markets, to conducting national security negotiations.

Brown’s creation, known as Libratus, is essentially three AI systems in one. The first developed a strategy for poker by playing against itself over trillions of hands during several months of training. Another refined that strategy in real time during games with humans, and a third reviewed the hands played at the end of each day of competition to identify weaknesses, like predictable betting patterns, that opponents might exploit.

In January 2017, Libratus defeated four of the world’s top players head-to-head over 120,000 hands in 20 days at a Pittsburgh casino. Because the bot didn’t learn to play by mimicking humans, it used tactics that human players typically don’t employ. Some of those strategies, like dramatically upping the ante of small pots, have begun to change how the pros play poker.