Full details of the research are published in the 2 March 2017 online issue of the journal Science. DeepStack’s poker-playing success while running on fairly standard computer hardware could make it much more practical for AI to tackle many other “imperfect-information” situations involving business negotiations, medical diagnoses and treatments, or even guiding military robots on patrol. AI that can handle complex poker games such as heads-up, no-limit Texas Hold’em could also tackle similarly complex real-world situations by making the best decisions in the midst of uncertainty. This is a huge deal beyond just bragging rights for an AI’s ability to beat the best human poker pros. “Instead of playing from there, we use intuition to decide how to play.” “To make this practical, we only look ahead a few moves deep,” says Michael Bowling, a computer scientist and head of the Computer Poker Research Group at the University of Alberta in Edmonton, Canada.

That enabled DeepStack’s fairly humble computer chip to figure out its best move for each play within five seconds and handily beat poker professionals from all over the world. But the Canadian and Czech reseachers who developed the new DeepStack algorithm leveraged deep learning technology to create the computer equivalent of intuition and reduce the possible future plays that needed to be calculated at any point in the game to just 107.

This version of two-player poker with unrestricted bet sizes has 10 160 possible plays at different stages of the game-more than the number of atoms in the entire universe.

The success of any poker-playing computer algorithm in heads-up, no-limit Texas Hold’em is no small feat. But instead of relying on a supercomputer’s hardware, the DeepStack AI has shown how it too can decisively defeat human poker pros while running on a GPU chip equivalent to those found in gaming laptops. A fresh Texas Hold’em-playing AI terror has emerged barely a month after a supercomputer-powered bot claimed victory over four professional poker players.

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