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. II—Recent Progress. IBM Journal, November 1967, pp. 601-617.
Machine learning (ML) enables artificial intelligence (AI) but Artificial Intelligence can exist without Machine Learning; for example, Rule-based AI and Expert Systems. Prior to his death, Professor McCarthy worked for five decades defining the discipline of artificial intelligence. The Samuel Checkers Playing program was a very early demonstration of AI. Writing a traditional program to play checkers must consider all the combinations in its handcrafted model.
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.
Combining it with the blockchain, which can provide an un-editable ledger of events, paired with AI's ability to analyze large data sets in real time makes it even more powerful. Because AI can analyze these data sets in real time, there's great potential for both borrowers and lenders to benefit. This technique, combined with the implementation of machine learning and advanced natural language recognition, makes this space prime for growth in 2017. However, bolstering AI in these difficult areas using machine learning, the blockchain, and human intervention offers some potential for growth in 2017.
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.
The checkers program Chinook has won the right to play a 40-game match for the World Checkers Championship against Dr. Marion Tinsley. This is the first time a program has earned the right to contest for a human world championship. In an exhibition match played in December 1990, Tinsley narrowly defeated Chinook 7.5-6.5. Many of the techniques used for computer chess are directly applicable to computer checkers.
While still unable to outplay checker masters, the program's playing ability has been greatly improved. Limited progress has been made in the development of an improved book-learning technique and in the optimization of playing strategies as applied to the checker playing program described in an earlier paper with this same title.' While the investigation of the learning procedures forms the essential core of the experimental work, certain improvements have been made in playing techniques which must first be described. The way in which two limiting values (McCarthy's alpha and beta) are used in pruning can be seen by referring The move tree of Figure 1 redrawn to illustrate the detailed method used to keep track of the comparison values.