AlphaGo versus Lee Sedol
AlphaGo versus Lee Sedol
- AlphaGo versus Lee Sedol, also known as the DeepMind Challenge Match**, was a five-game [[go-(game)|Go]] match between top Go player Lee Sedol and AlphaGo, a [[computer-go]] program developed by DeepMind, played in Seoul, South Korea between the 9th and 15th of March 2016. AlphaGo won all but the fourth game; all games were won by resignation. The match has been compared with the historic chess match between [[deep-blue-versus-garry-kasparov|Deep Blue and Garry Kasparov]] in 1997.
The winner of the match was slated to win $1 million. Since AlphaGo won, Google DeepMind stated that the prize would be donated to charities, including UNICEF, and Go organisations. Lee received $170,000 ($150,000 for participating in the five games and an additional $20,000 for winning one game).
After the match, the korea-baduk-association awarded AlphaGo the highest Go grandmaster rank – an "honorary 9 dan". It was given in recognition of AlphaGo's "sincere efforts" to master Go. This match was chosen by Science as one of the runners-up for Breakthrough of the Year, on 22 December 2016.
Background ### Difficult challenge in artificial intelligence Go is a complex board game that requires intuition, creative and strategic thinking. It has long been considered a difficult challenge in the field of artificial intelligence (AI). It is considerably more difficult to design strong computer players for than chess. Many in artificial intelligence consider Go to require more elements that mimic human thought than chess. Mathematician I. J. Good wrote in 1965:
Prior to 2015, the best Go programs only managed to reach amateur dan level. On the small 9×9 board, the computer fared better, and some programs managed to win a fraction of their 9×9 games against professional players. Before AlphaGo, some researchers had claimed that computers would never defeat top humans at Go. Elon Musk, an early investor of DeepMind, said in 2016 that experts in the field thought AI was 10 years away from achieving a victory against a top professional Go player.
The match AlphaGo versus Lee Sedol is comparable to the 1997 chess match when Garry Kasparov lost to IBM computer Deep Blue. Kasparov's loss to Deep Blue is considered the moment a computer became better than humans at chess.
AlphaGo is significantly different from previous AI efforts. Instead of using probability algorithms hard-coded by human programmers, AlphaGo uses neural networks to estimate its probability of winning. AlphaGo accesses and analyses the entire online library of Go, including all matches, players, analytics, literature, and games played by AlphaGo against itself and other players. Once set up, AlphaGo is independent of the developer team and evaluates the best pathway to solving Go (i.e., winning the game). By using neural networks and monte-carlo-tree-search, AlphaGo calculates colossal numbers of likely and unlikely probabilities many moves into the future .
Related research results are being applied to fields such as cognitive science, pattern recognition and machine learning.
Match against Fan Hui
AlphaGo defeated European champion fan-hui, a 2 dan professional, 5–0 in October 2015, the first time an AI had beaten a human professional player at the game on a full-sized board without a handicap. Some commentators stressed the gulf between Fan and Lee, who is ranked 9 dan professional. Canadian AI specialist jonathan-schaeffer, commenting after the win against Fan, compared AlphaGo with a "child prodigy" that lacked experience, and considered, "the real achievement will be when the program plays a player in the true top echelon." He then believed that Lee would win the match in March 2016.
Preparation Go experts found errors in AlphaGo's play against Fan, particularly relating to a lack of awareness of the entire board. Before the game against Lee, it was unknown how much the program had improved its game since its October match. AlphaGo's original training dataset started with games of strong amateur players from internet Go servers, after which AlphaGo trained by playing against itself for tens of millions of games.
Players ### AlphaGo
AlphaGo is a computer program developed by google-deepmind to play the board game Go. AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. The system's neural networks were initially bootstrapped from human game-play expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a KGS Go Server database of around 30 million moves from 160,000 games by KGS 6 to 9 dan human players. Once it had reached a certain degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using reinforcement learning to improve its play. The system does not use a "database" of moves to play. As one of the creators of AlphaGo explained:
In the match against Lee, AlphaGo used about the same computing power as it had in the match against Fan Hui, where it used 1,202 CPUs and 176 GPUs. Google has also stated that its proprietary tensor processing units were used in the match against Lee Sedol.
Lee Sedol
Lee Sedol is a professional Go player of 9 dan rank and is one of the strongest players in the history-of-go. He started his career in 1996 (promoted to professional dan rank at the age of 12), winning 18 international titles since then. He is a "national hero" in his native South Korea, known for his unconventional and creative play. Lee Sedol initially predicted he would defeat AlphaGo in a "landslide".
Games The match was a five-game match with one million US dollars as the grand prize,
The match was played at the Four Seasons Hotel in Seoul, South Korea in March 2016 and was video-streamed live with commentary; the English language commentary was done by Michael Redmond (9-dan professional) and Chris Garlock. Aja Huang, a DeepMind team member and amateur 6-dan Go player, placed stones on the Go board for AlphaGo, which ran through the Google Cloud Platform with its server located in the United States.
Summary ### Game 1 AlphaGo (white) won the first game. Lee appeared to be in control throughout the match, but AlphaGo gained the advantage in the final 20 minutes, and Lee resigned. David Ormerod, commenting on the game at Go Game Guru, described Lee's seventh stone as "a strange move to test AlphaGo's strength in the opening", characterising the move as a mistake and AlphaGo's response as "accurate and efficient". He described AlphaGo's position as favourable in the first part of the game, considering that Lee started to come back with move 81 before making "questionable" moves at 119 and 123, followed by a "losing" move at 129. Professional Go player Cho Hanseung commented that AlphaGo's game had greatly improved from when it beat fan-hui in October 2015.
According to 9-dan Go grandmaster Kim Seong-ryong, Lee seemed stunned by AlphaGo's strong play on the 102nd stone. After watching AlphaGo make the game's 102nd move, Lee mulled over his options for more than 10 minutes. "from very beginning of the game I did not feel like there was a point that I was leading". One of the creators of AlphaGo, Demis Hassabis, said that the system was confident of victory from the midway point of the game, even though the professional commentators could not tell which player was ahead.
AlphaGo showed anomalies and moves from a broader perspective, which professional Go players described as looking like mistakes at first sight but an intentional strategy in hindsight. As one of the creators of the system explained, AlphaGo does not attempt to maximize its points or its margin of victory, but tries to maximize its probability of winning.
After the second game, players still had doubts about whether AlphaGo was truly a strong player in the sense that a human might be. The third game was described as removing that doubt, with analysts commenting that: {{blockquote|AlphaGo won so convincingly as to remove all doubt about its strength from the minds of experienced players. In fact, it played so well that it was almost scary ... In forcing AlphaGo to withstand a very severe, one-sided attack, Lee revealed its hitherto undetected power ... Lee wasn't gaining enough profit from his attack ... One of the greatest virtuosos of the middle game had just been upstaged in black and white clarity. AlphaGo was seen to capably navigate tricky situations known as [[ko-fight|ko]] that did not come up in the previous two matches. An and Ormerod consider move 148 to be particularly notable: in the middle of a complex ko fight, AlphaGo displayed sufficient "confidence" that it was winning the game to play a significant move elsewhere. AlphaGo had gained control of the game by move 48, and forced Lee onto the defensive. Lee counterattacked at moves 77/79, but AlphaGo's response was effective, and its move 90 succeeded in simplifying the position. It then gained a large area of control at the bottom of the board, strengthening its position with moves from 102 to 112 described by An as "sophisticated". By doing so, his apparent aim was to force an "all or nothing" style of situation – a possible weakness for an opponent strong at negotiation types of play, and one which might make AlphaGo's capability of deciding slim advantages largely irrelevant. Gu Li (9p) described it as a "divine move" and stated that the move had been completely unforeseen by him.
AlphaGo responded poorly on move 79, at which time it estimated it had a 70% chance to win the game. Lee followed up with a strong move at white 82. provoking it to make a series of very bad moves from black 87 to 101. David Ormerod characterised moves 87 to 101 as typical of Monte Carlo-based program mistakes. For this reason, he requested that he play black in the fifth game, which is considered more risky.
David Ormerod of Go Game Guru stated that although an analysis of AlphaGo's play around 79–87 was not yet available, he believed it resulted from a known weakness in play algorithms that use monte-carlo-tree-search. In essence, the search attempts to prune less relevant sequences. In some cases, a play can lead to a particular line of play which is significant but which is overlooked when the tree is pruned, and this outcome is therefore "off the search radar".
Game 5 AlphaGo (white) won the fifth game.
Lee, playing black, opened similarly to the first game and began to stake out territory in the right and top left corners – a similar strategy to the one he employed successfully in game 4 – while AlphaGo gained influence in the centre of the board. The game remained even until white moves 48 to 58, which AlphaGo played in the bottom right. These moves unnecessarily lost ko threats and aji, allowing Lee to take the lead.
Coverage Live video of the games and associated commentary was broadcast in Korean, Chinese, Japanese, and English. Korean-language coverage was made available through Baduk TV. Chinese-language coverage of game 1 with commentary by 9-dan players Gu Li and Ke Jie was provided by Tencent and LeTV respectively, reaching about 60 million viewers. Online English-language coverage presented by US 9-dan Michael Redmond and Chris Garlock, a vice-president of the american-go-association, reached an average 80 thousand viewers with a peak of 100 thousand viewers near the end of game 1.
Responses ### AI community AlphaGo's victory was a major milestone in artificial intelligence research. Go had previously been regarded as a hard problem in machine learning that was expected to be out of reach for the technology of the time. Most experts thought a Go program as powerful as AlphaGo was at least five years away; some experts thought that it would take at least another decade before computers would beat Go champions. Most observers at the beginning of the 2016 matches expected Lee to beat AlphaGo. Some commentators believe AlphaGo's victory makes for a good opportunity for society to start discussing preparations for the possible future impact of machines with general purpose intelligence. In March 2016, AI researcher Stuart Russell stated that "AI methods are progressing much faster than expected, (which) makes the question of the long-term outcome more urgent," adding that "to ensure that increasingly powerful AI systems remain completely under human control... there is a lot of work to do." Some scholars, such as physicist Stephen Hawking, warn that some future self-improving AI could gain actual general intelligence, leading to an unexpected AI takeover; other scholars disagree: AI expert Jean-Gabriel Ganascia believes that "Things like 'common sense'... may never be reproducible", and says "I don't see why we would speak about fears. On the contrary, this raises hopes in many domains such as health and space exploration."
The DeepMind AlphaGo Team received the Inaugural IJCAI Marvin Minsky Medal for Outstanding Achievements in AI. "AlphaGo is a wonderful achievement, and a perfect example of what the Minsky Medal was initiated to recognise", said Professor Michael Wooldridge, Chair of the IJCAI Awards Committee. "What particularly impressed IJCAI was that AlphaGo achieves what it does through a brilliant combination of classic AI techniques as well as the state-of-the-art machine learning techniques that DeepMind is so closely associated with. It's a breathtaking demonstration of contemporary AI, and we are delighted to be able to recognise it with this award".
Go community Go is a popular game in South Korea, China, and Japan. This match was watched and analyzed by millions of people worldwide. where a computer had beaten a Go professional for the first time without the advantage of a handicap. As the matches progressed, Ke Jie went back and forth, stating that "it is highly likely that I (could) lose" after analyzing the first three matches, but regaining confidence after the fourth match. In the end, Ke Jie played Alpha Go the next year and was defeated in three games.
Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary general of the international-go-federation, both reason that in the future, Go players will get help from computers to learn what they have done wrong in games and improve their skills.
After game three, Lee apologized for his losses and stated, "I misjudged the capabilities of AlphaGo and felt powerless." Lee said his eventual loss to a machine was "inevitable" but stated that "robots will never understand the beauty of the game the same way that we humans do."
Government In response to the match the South Korean government announced on 17 March 2016 that it would invest 1 trillion won (US$863 million) in artificial intelligence (AI) research over the next five years.
Other human vs AI competitors Ken Jennings, who was famously defeated in 2011 by IBM Watson in a two-game Jeopardy! The IBM Challenge between the AI supercomputer and two of the game show's legendary champions, wrote in Slate magazine after the event. He stated, "The nightmarish robot dystopias of science-fiction movies just got one benchmark closer."
Jennings compared AlphaGo to Kurt Vonnegut's 1952 novel Player Piano, where artificial intelligence eliminates almost all careers, and a those whose jobs were replaced by AI, in Vonnegut's novel, are placed into a government Works Progress Administration-style organisation that revolts because of people lost self-respect to AI. He stated it was "charmingly retrofuturistic as Walt Disney's Tomorrowland."
Jennings, who was eventually named interim host on October 29, 2020 and permanent full-time host of Jeopardy! on December 15, 2023, concluded his article with the following:
In media An award-winning documentary film about the matches, AlphaGo, was made in 2017. On 13 March 2020, the film was made free online on the DeepMind YouTube channel.
The matches were featured in Benjamin Labatut's 2023 novel, The MANIAC.