Alpha-beta pruning can be explained simply as a technique for not exploring those branches of a search tree that analysis indicates not to be of further interest either to the player making the analysis (this is obvious) or to his opponent (and this is frequently overlooked).
– Arthur L. Samuel, from Some Studies in Machine Learning Using the Game of Checkers. —Recent Progress. IBM Journal, November 1967, pp. 601-617.
Attention everyone playing checkers at a park, in grade school, or on the massive rug at Cracker Barrel: You can take your pieces and go home. After five thousand years of game play, checkers has been solved. Researchers at the University of Alberta led by Jonathan Schaeffer have created an unbeatable checkers program called Chinook. "There isn't a human alive today that can ever win a game anymore against the full program," Schaeffer says--although he does leave open the possibility that a person could eke out a draw in the unlikely event that she played a perfect game. Not only is Chinook unbeatable, but it has run through every possible move and every possible board configuration, so it will never, ever be surprised.
In what The New York Times is calling The Great AI Awakening and Forbes has dubbed The Year of AI, 2017 is shaping up to be obsessively focused on artificial intelligence, a field that has been around for awhile (remember playing checkers against a computer?) Because the technology has finally reached its tipping point, AI, and its close relative machine learning, have taken a variety of industries by storm, bringing self-driving Ubers to the streets of San Francisco (and then carting them away); robotic vacuum cleaners to dirty household floors; and natural language processing to chat bots and IVR communications. With AI already embedded into these industries, it's easy to find examples of how the technology is shaping fintech. Below are eight areas of fintech into which AI has made inroads. Each area is ranked and rated (out of 5 stars) based on how it is currently influenced by AI and based on AI's potential to add value.
The use of competitive gameplay to study artificial intelligence dates to the early days of modern AI, when Arthur Samuel developed a Checkers program in 1956 that trained itself using reinforcement learning. As computer Checkers advanced, so did Backgammon: in 1979 Hans Berliner's BKG 9.8 program defeated reigning Backgammon world champion Luigi Villa, winning the matchup 7–1. As a result, if the world's top ranked player Magnus Carlsen (Elo rating: 2851) played the 100th ranked player Loek Van Wely (Elo rating: 2653) tomorrow in a game, a large-scale analysis of historical gameplay predicts that Carlsen has about a 75% chance of beating Van Wely. In a series of excellent blog posts and research papers, computer scientist and International Master-level Chess player Ken Regan has explored the concept of a ratings horizon in Elo ratings for Chess: more and more modern computer programs mostly draw ties against each other, and Regan notes that we are steadily approaching the point where Chess programs may not lose to each other -- or to any human.