Artificial intelligence goes deep to beat humans at poker

Two artificial intelligence (AI) programs have finally proven they “know when to hold ’em, and when to fold ’em,” recently beating human professional card players for the first time at the popular poker game of Texas Hold 'em. And this week the team behind one of those AIs, known as DeepStack, has divulged some of the secrets to its success—a triumph that could one day lead to AIs that perform tasks ranging from from beefing up airline security to simplifying business negotiations.

AIs have long dominated games such as chess, and last year one conquered Go, but they have made relatively lousy poker players. In DeepStack researchers have broken their poker losing streak by combining new algorithms and deep machine learning, a form of computer science that in some ways mimics the human brain, allowing machines to teach themselves.

The team’s findings coincide with the very public success several weeks ago of Libratus, a poker AI designed by researchers at Carnegie Mellon University in Pittsburgh, Pennsylvania. In a 20-day poker competition held in Pittsburgh, Libratus bested four of the top-ranked human Texas Hold ’em players in the world over the course of 120,000 hands. Both teams say their system’s superiority over humans is backed by statistically significant findings. The main difference is that, because of its lack of deep learning, Libratus requires more computing power for its algorithms and initially needs to solve to the end of the every time to create a strategy, Bowling says. DeepStack can run on a laptop.
Though there's no clear consensus on which AI is the true poker champ—and no match between the two has been arranged so far—both systems have are already being adapted to solve more complex real-world problems in areas like security and negotiations. Bowling’s team has studied how AI could more successfully randomize ticket checks for honor-system public transit.

Machines are finally getting the best of humans at poker.

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