Texas Hold'em AI Bot Taps Deep Learning to Demolish Humans

IEEE Spectrum Robotics

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. 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. The success of any poker-playing computer algorithm in heads-up, no-limit Texas Hold'em is no small feat. This version of two-player poker with unrestricted bet sizes has 10160 possible plays at different stages of the game--more than the number of atoms in the entire universe. 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.

Poker may be the latest game to fold against artificial intelligence


In a landmark achievement for artificial intelligence, a poker bot developed by researchers in Canada and the Czech Republic has defeated several professional players in one-on-one games of no-limit Texas hold'em poker. Perhaps most interestingly, the academics behind the work say their program overcame its human opponents by using an approximation approach that they compare to "gut feeling." "If correct, this is indeed a significant advance in game-playing AI," says Michael Wellman, a professor at the University of Michigan who specializes in game theory and AI. "First, it achieves a major milestone (beating poker professionals) in a game of prominent interest. Second, it brings together several novel ideas, which together support an exciting approach for imperfect-information games."

Could AI bot beat Bond in a Casino Royale style showdown

Daily Mail - Science & tech

In the high stakes world of professional poker, calculating the odds can go a long way to help win the hand. And a new bot developed by a team of computer scientists could give even James Bond a run for his money. The DeepStack system uses Artificial Intelligence to reduce an exponentially complex number of calculations to a more manageable size - then decides on its play in a matter of seconds. A combination of cool composure, a strong hand and more than a dash of luck allowed James Bond to walk off with a massive £93 million ($115 million) pot in Casino Royale. DeepStack played 3,000 hands each against eleven professional players.

Poker Game – the latest AI conquest


For the last day of this week, I've chosen to play poker with artificial intelligence (AI). Past this metaphorically first sentence, i would like to share with The Information Age a recent post from the MIT Technology Review, another irresistible one, and this time about a most recent AI conquest of a typically human capacity – the card game of Poker. This is a game that involves several human psychological traits that we might think an artificial device would never master. For example, the human ability to read other people's minds would have seemed intractable form an AI perspective; or the ability to induce contradictory beliefs in others, like when the poker players uses the strategy of bluffing. Strategic thinking under uncertainty and imperfect information appears to being conquered by the AI community of computer scientists and software geeks.

How rival bots battled their way to poker supremacy


Top professional poker players have been been beaten by AI bots at no-limits hold'em. A complex variant of poker is the latest game to be mastered by artificial intelligence (AI). And it has been conquered not once, but twice, by two rival bots developed by separate research teams. Each algorithm -- which plays a'no limits' two-player version of Texas hold'em -- has in recent months hit a crucial AI milestone: they have beaten human professional players. The game first fell in December to DeepStack, developed by computer scientists at the University of Alberta in Edmonton, Canada, with collaborators from Charles University and the Czech Technical University in Prague.