Analysis Machines have triumphed again. Libratus, a powerful computer program, has crushed its human opponents at a heads-up no-limit Texas hold'em poker tournament held at Rivers Casino in Pittsburgh, Pennsylvania, winning $1,776,250 over 120,000 hands. It's a landmark achievement in AI game playing, said Tuomas Sandholm, co-creator of Libratus and a machine-learning professor at Carnegie Mellon University (CMU). "Heads up no-limit Texas hold'em is – in a way – the last frontier standing within the foreseeable future. Of course, new things can come later.
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.
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.
It is no mystery why poker is such a popular pastime: the dynamic card game produces drama in spades as players are locked in a complicated tango of acting and reacting that becomes increasingly tense with each escalating bet. The same elements that make poker so entertaining have also created a complex problem for artificial intelligence (AI). A study published today in Science describes an AI system called DeepStack that recently defeated professional human players in heads-up, no-limit Texas hold'em poker, an achievement that represents a leap forward in the types of problems AI systems can solve. DeepStack, developed by researchers at the University of Alberta, relies on the use of artificial neural networks that researchers trained ahead of time to develop poker intuition. During play, DeepStack uses its poker smarts to break down a complicated game into smaller, more manageable pieces that it can then work through on the fly.
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.