Scientists at the University of Alberta are cracking away at the complexities of artificial intelligence with their new "DeepStack" system, which can not only play a round of poker with you, but walk away with all of your money. This new technology builds upon the legacy of systems like IBM's Deep Blue, which was the first program to beat a world champion, Gary Kasparov, at chess in 1996. As Michael Bowling, co-author of the research and leader of the Computer Poker Research Group at Alberta, puts it: poker is the next big step for designing AI. In a game of Heads Up No Limit poker, DeepStack was able to win against professional poker players at a rate of 49 big blinds per 100. "We are winning by 49 per 100, that's like saying whatever the players were doing was not that much more effective than if they just folded every hand," Bowling tells Inverse.
In conjunction with the Association for the Advancement of Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research.
AlphaGo, a largely self-taught Go-playing AI, last night won the fifth and final game in a match held in Seoul, South Korea, against that country's Lee Sedol. Sedol is one of the greatest modern players of the ancient Chinese game. The final score was 4 games to 1. Thus falls the last and computationally hardest game that programmers have taken as a test of machine intelligence. Chess, AI's original touchstone, fell to the machines 19 years ago, but Go had been expected to last for many years to come. The sweeping victory means far more than the US 1 million prize, which Google's London-based acquisition, DeepMind, says it will give to charity.