Go (game) significant event AlphaGo versus Lee Sedol
The reasons why computer programs had not played Go at the professional dan level prior to 2016 include:
The number of spaces on the board is much larger (over five times the number of spaces on a chess board—361 vs. 64). On most turns there are many more possible moves in Go than in chess. Throughout most of the game, the number of legal moves stays at around 150–250 per turn, and rarely falls below 100 (in chess, the average number of moves is 37). Because an exhaustive computer program for Go must calculate and compare every possible legal move in each ply (player turn), its ability to calculate the best plays is sharply reduced when there are a large number of possible moves. Most computer game algorithms, such as those for chess, compute several moves in advance. Given an average of 200 available moves through most of the game, for a computer to calculate its next move by exhaustively anticipating the next four moves of each possible play (two of its own and two of its opponent's), it would have to consider more than 320 billion (3.2×1011) possible combinations. To exhaustively calculate the next eight moves, would require computing 512 quintillion (5.12×1020) possible combinations. As of March 2014, the most powerful supercomputer in the world, NUDT's "Tianhe-2", can sustain 33.86 petaflops. At this rate, even given an exceedingly low estimate of 10 operations required to assess the value of one play of a stone, Tianhe-2 would require four hours to assess all possible combinations of the next eight moves in order to make a single play.
The placement of a single stone in the initial phase can affect the play of the game a hundred or more moves later. A computer would have to predict this influence, and it would be unworkable to attempt to exhaustively analyze the next hundred moves.
In capture-based games (such as chess), a position can often be evaluated relatively easily, such as by calculating who has a material advantage or more active pieces. In Go, there is often no easy way to evaluate a position. However a 6-kyu human can evaluate a position at a glance, to see which player has more territory, and even beginners can estimate the score within 10 points, given time to count it. The number of stones on the board (material advantage) is only a weak indicator of the strength of a position, and a territorial advantage (more empty points surrounded) for one player might be compensated by the opponent's strong positions and influence all over the board. Normally a 3-dan can easily judge most of these positions.As an illustration, the greatest handicap normally given to a weaker opponent is 9 stones. It was not until August 2008 that a computer won a game against a professional level player at this handicap. It was the Mogo program, which scored this first victory in an exhibition game played during the US Go Congress.  By 2013, a win at the professional level of play was accomplished with a four-stone advantage.  In October 2015, Google DeepMind's program AlphaGo beat Fan Hui, the European Go champion and a 2 dan (out of 9 dan possible) professional, five times out of five with no handicap on a full size 19×19 board.   AlphaGo used a fundamentally different paradigm than earlier Go programs; it included very little direct instruction, and mostly used deep learning where AlphaGo played itself in hundreds of millions of games such that it could measure positions more intuitively.  In March 2016, Google next challenged Lee Sedol, a 9 dan considered the top player in the world in the early 21st century, to a five-game match.  Leading up to the game, Lee Sedol and other top professionals were confident that he would win; however, AlphaGo defeated Lee in four of the five games. After having already lost the series by the third game, Lee won the fourth game, describing his win as "invaluable". In May 2017,  AlphaGo beat Ke Jie, who at the time continuously held the world No. 1 ranking for two years, winning each game in a three-game match during the Future of Go Summit.In October 2017, DeepMind announced a significantly stronger version called AlphaGo Zero which beat the previous version by 100 games to 0.In February 2023, Kellin Pelrine, an amateur American Go player, won 14 out of 15 games against a top-ranked AI system in a significant victory over artificial intelligence. Pelrine took advantage of a previously unknown flaw in the Go computer program, which had been identified by another computer. He exploited this weakness by slowly encircling the opponent's stones and distracting the AI with moves in other parts of the board. The tactics used by Pelrine have highlighted a fundamental flaw in the deep learning systems that underpin many of today's advanced AI. Although the AI systems can "understand" specific situations, they lack the ability to generalize in a way that humans find easy.
Landmark event Key happening Pivotal occurrence Momentous incident Notable episode