![]() ![]() The key insight proved to be an application of machine learning and deep learning. The tantalizing unmet goal of defeating the best human players without a handicap, long thought unreachable, brought a burst of renewed interest. High-dan amateurs and professionals could still exploit these programs' weaknesses and win consistently, but computer performance had advanced past the intermediate (single-digit kyu) level. The application of Monte Carlo tree search to Go algorithms provided a notable improvement in the late 2000s decade, with programs finally able to achieve a low-dan level: that of an advanced amateur. Some AI researchers speculated that the problem was unsolvable without creation of human-like AI. Creation of a human professional quality program with the techniques and hardware of the time was out of reach. Many of the algorithms such as alpha-beta minimax that performed well as AIs for checkers and chess fell apart on Go's 19x19 board, as there were too many branching possibilities to consider. Professionals could defeat these programs even given handicaps of 10+ stones in favor of the AI. The best efforts of the 1980s and 1990s produced only AIs that could be defeated by beginners, and AIs of the early 2000s were intermediate level at best. Before 2015, the programs of the era were weak. The field is sharply divided into two eras. ![]() ![]() Computer Go is the field of artificial intelligence (AI) dedicated to creating a computer program that plays the traditional board game Go. ![]()
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