Many in the field considered Go to require more elements that mimic human thought than chess. It has long been considered a difficult challenge in the field of artificial intelligence (AI) and is considerably more difficult to solve than chess. Professional Go players see the game as requiring intuition, creative and strategic thinking. DeepMind did not release AlphaGo for public use, but various programs have been built since based on the journal articles DeepMind released describing AlphaGo and its variants. Just as checkers had fallen to machines in 1995 and chess in 1997, computer programs finally conquered humanity's greatest Go champions in 2016–2017. AlphaGo defeated Lee Sedol, a 9 dan professional, in a no-handicap match in 2016, then defeated Ke Jie in 2017, who at the time continuously held the world No. DeepMind, a Google acquisition dedicated to AI research, produced AlphaGo in 2015 and announced it to the world in 2016. 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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