YORKTOWN HEIGHTS, NY - JANUARY 13:  A general view of IBM's 'Watson' computing system at a press conference to discuss the upcoming Man V. Machine "Jeopardy!" competition at the IBM T.J. Watson Research Center on January 13, 2011 in Yorktown Heights, New York.  (Photo by Ben Hider/Getty Images)
IBM Watson and the future of artificial intelligence
05:19 - Source: CNN

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An artificial intelligence program called DeepStack beat 10 out of 11 world poker experts

This type of artificial intelligence can deal with situations that better mirror real-world decision-making

CNN  — 

Poker expert Martin Sturc may have finally met his match: a computer program called DeepStack.

“You always have to be one step ahead,” said Sturc, president of the Austrian Pokersport Association. “When I realized he was adapting his gameplan, I had to adapt.”

Using a type of artificial intelligence its creators describe as “intuition,” DeepStack widely defeated 10 out of 11 professional players from the International Federation of Poker in a game of heads-up no-limit Texas Hold ‘em. Each player played 3,000 hands against DeepStack.

Sturc was the 11th player. DeepStack won by a margin that was too small to decide the overall winner; statistically, Sturc may be the one player who is still better than DeepStack.

“It’s a huge compliment,” he said. “It feels good that there’s a small chance that I’m still at least on the same level as artificial intelligence.”

The team of computer scientists that invented DeepStack say the program mirrors real-life decision-making more than traditional AI, and it may have uses beyond the poker table.

A high-tech digital world

To understand why DeepStack is so special, you have to understand the nature of the game, said Michael Bowling, one of DeepStack’s creators and the corresponding author on a paper published today in the journal Science.

Bowling said that in most AI games, such as the chess-playing Deep Blue and Google’s AlphaGo, the board has all the information you need to make a decision.

“There isn’t some knowledge in one of the other players’ heads that you wish you knew,” said Bowling, a computer science professor at the University of Alberta.

But in Texas Hold ‘em, the players can’t see each other’s cards. They all have a different view of the game. According to Bowling, this more closely mirrors the decisions we make in everyday life, which are often based on incomplete information.

“It’s actually a rare moment when we say, ‘Oh, yeah, I have all the information I need to make this decision,’ ” Bowling said.

Online poker sites have been around for years, and poker-playing AI is not necessarily new. But heads-up no-limit Texas Hold ‘em is a different game altogether. Because there are no limits on the sizes of individual bets, the number of possible decisions a player can make is astronomically high: 1 followed by 160 zeroes.

“(That’s) more than there are atoms in the universe,” Bowling said.

A common estimation for the number of atoms in the observable universe is roughly 1 followed by 80 zeroes. When poker bets are fixed – a variation called heads-up limit – the number of zeroes drops to just 14.

With so many possible choices, this version of Texas Hold ‘em would be too complex for most computer programs.

DeepStack “is different from ‘good old-fashioned AI,’ ” said Vlad Firoiu, a doctoral candidate at MIT’s Computer Science and Artificial Intelligence Laboratory, who recently developed an AI for “Super Smash Bros. Melee” that beat top players of the popular Nintendo fighting game. Firoiu was not involved in DeepStack.

Whereas traditional AI calculated all the possible outcomes of a game beforehand, that was not possible or practical for a game with so many possible choices. So Bowling and other modern gamemakers have turned to a newer type of algorithm called deep learning. The more examples they feed a computer,