About The Oldest AI Program In America
Samuel's Checkers-playing Program is still used for research and educational purposes
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
The first and oldest artificial intelligence program is The Samuel Checkers-Playing Program, or Samuel's Checkers Player, created in 1952. The self-learning program was the first of its kind, created by Arthur Lee Samuel, during his time at IBM's Research Laboratory in Poughkeepsie, New York. It is still referred to today as a learning tool for Artificial Intelligence coding programs.
Oxford University describes it as the first artificially intelligent computer program. IEEE Computer Society defines it as the pioneer of artificial intelligence and the world's first learning program. IBM recognizes it as the first "self-learning" program concept of artificial intelligence.
The Samuel Checkers-playing Program is the first example of a method now commonly used in artificial intelligence (AI) research, that is, to work in a complex yet understandable domain. Samuel's checkers-playing program was one of the first successful artificial intelligence programs.
Samuel's program was a self-learning program. It improved its playing ability by playing against itself and learning from its mistakes. The first of its kind program used a variety of techniques, including minimax search and reinforcement learning.
Samuel's program became quite skilled at checkers. In 1961 he put his program up against the Connecticut state checker champion, the number four ranked player in the nation. His checkers program won. Samuel went on to use techniques such as mutable evaluation functions, hill climbing, and signature tables to explore rote and generalization learning.
His work greatly influenced the instruction set of early IBM computers. The program continued to improve its playing ability over time, and in 1967, it was able to beat the world champion checkers player.
Samuel's Checkers-playing Program was a major milestone in the development of artificial intelligence. It showed that it was possible to create a computer program that could learn and improve on its own. The program's success also helped to popularize the field of artificial intelligence.
February 24, 1956, the computer program demonstrated the very first practical example of artificial intelligence ever to the public, and it was done on television. Samuel was interviewed on a live morning news program, sitting remotely at the 701, with Will Rogers Jr. at the TV studio, together with a checkers expert who played with the computer for about an hour.
Samuel's work on checkers-playing programs had a significant impact on the development of machine learning and artificial intelligence. His program was one of the first to use reinforcement learning, a type of machine learning that allows agents to learn from their own experience. Samuel's work also helped to popularize the idea of using games as a test bed for artificial intelligence research.
Samuel's Checkers-playing Program is still considered to be a landmark achievement in the field of artificial intelligence. It is a testament to the power of machine learning and the potential of artificial intelligence to solve complex problems.
This oldest AI program still exists today. It is not widely used, but it is still available for research and educational purposes. In 2009, a team of researchers at the University of California, Berkeley, recreated Samuel's checkers-playing program using modern computer hardware and software.
The researchers were able to show that the program could still beat a variety of checkers-playing programs, including some of the most advanced programs available today. In July 1959 Arthur Lee Samuel published “Some Studies in Machine Learning Using the Game of Checkers,” in the IBM Journal of Research and Development, coining the term “machine learning.”
In this paper, Samuel describes his work on developing a computer program that could play checkers (draughts) and improve its performance through self-play and learning. The paper is still used today for teaching AI coding in many learning institutions.