Darren Elias knows poker. The 32-year-old is the only person to have won four World Poker Tour titles and has earned more than $7 million at tournaments. Despite his expertise, he learned something new this spring from an artificial intelligence bot.

Elias was helping test new soft­ware from researchers at Carnegie Mellon University and Facebook. He and another pro, Chris “Jesus” Ferguson, each played 5,000 hands over the internet in six-way games against five copies of a bot called Pluribus.

At the end, the bot was ahead by a good margin. Along the way, Elias noticed something: Although machines are often thought of as uninspired, this bot was ballsier than your typical poker pro. “It will bet two or three times the pot, which humans don’t do very much,” Elias says. “These huge bets are interesting to me and something I will incorporate into my own play.”

Pluribus is significant not just because a new bot taught an old pro new tricks. The software is the first to beat top professionals at multiplayer no-limit Texas Hold’em, seen as the elite form of poker. A paper in the journal Science on Thursday describes how Pluribus took on Elias and Ferguson, and also won handily in scenarios where a single copy of the bot played five human professionals for 10,000 hands.

“If you sit this bot down with five elite professional humans, it is going to beat them and make money off them,” says Noam Brown, a researcher in Facebook’s AI lab and cocreator of Pluribus. “This is really the gold standard as far as poker goes.”

Michael Littman, a professor at Brown University who has worked on computer poker but wasn’t involved in the project, agrees. Poker has long been seen as a grand challenge for AI researchers, with properties similar to many real world situations. Unlike in chess, poker players must choose actions without knowing what cards their opponents hold—as is the case in politics, business, and war. The complexity that creates in a six-way game has previously put multiplayer Hold’em out of reach for AI researchers. Most work has been on two-player games. Now the last major milestone for poker AI has fallen, Littman says. “This is really the end of a multi-decade effort involving many researchers,” he says.

Brown built Pluribus with Tuomas Sandholm, a Carnegie Mellon professor. Brown was previously a grad student in Sandholm’s lab, where the pair built a 2017 bot called Libratus that became the first software to beat professionals at the much simpler two-player form of no-limit Hold’em.

“If you sit this bot down with five elite professional humans, it is going to beat them and make money off them.” Noam Brown, cocreator of the bot

Brown started the Pluribus project after joining Facebook, but he says the social media giant doesn’t have specific applications of the technology in mind. “The goal is fundamental research on imperfect information and large-scale multi-agent systems,” he says—a phrase that also aptly describes Facebook’s main service. Longer term, ideas tested in Pluribus could help self-driving cars predict the actions of other drivers, or improve fraud-detection algorithms, he says.

Sandholm at CMU says he has already proven the commer­cial—and even national security—value of software that can strategize. He has established two companies to commer­cialize work on AI strategizing techniques from his lab.

One of those companies, Strategic Machine, works on uses such as improving bots in videogames and helping companies set optimum prices that take into account how competitors will respond. The other, Strategy Robot, signed a two-year contract worth up to $10 million with the Pentagon in 2018; Sandholm and the Pentagon decline to discuss the contract. But Sandholm has said one of Strategy Robot’s selling points is using ideas proven in poker and his other AI projects to make simulated—or even real—battlefield strategies more robust against enemy actions. Nothing from the project with Facebook will be licensed to either of Sandholm's companies, although some techniques central to Pluribus predate the project.

Pluribus is similar to Libratus in that it built up its skills by playing trillions of hands against versions of itself. After each hand, the system reviews what happened and what might have worked better—any improvements are added to its core strategy.