| AlphaGo | |
|---|---|
 | |
| Developer | Google DeepMind |
| Type | Computer Gosoftware |
| Website | deepmind.com/research/highlighted-research/alphago |
| Part ofa series on |
| Artificial intelligence (AI) |
|---|
_artificial_intelligence_icon.png){kind=icon} |
Glossary |
AlphaGo is acomputer program that plays theboard gameGo.[1] It was developed by the London-basedDeepMind Technologies,[2] an acquired subsidiary ofGoogle. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the nameMaster.[3] After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known asAlphaGo Zero, which was completelyself-taught without learning from human games. AlphaGo Zero was then generalized into a program known asAlphaZero, which played additional games, includingchess andshogi. AlphaZero has in turn been succeeded by a program known asMuZero which learns without being taught the rules.
AlphaGo and its successors use aMonte Carlo tree search algorithm to find its moves based on knowledge previously acquired bymachine learning, specifically by anartificial neural network (adeep learning method) by extensive training, both from human and computer play.[4] A neural network is trained to identify the best moves and the winning percentages of these moves. This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration.
In October 2015, in a match againstFan Hui, the original AlphaGo became the firstcomputer Go program to beat a humanprofessional Go player withouthandicap on a full-sized 19×19 board.[5][6] In March 2016, it beatLee Sedol ina five-game match, the first time a computer Go program has beaten a9-dan professional without handicap.[7] Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by theKorea Baduk Association.[8] The lead up and the challenge match with Lee Sedol were documented in a documentary film also titledAlphaGo,[9] directed by Greg Kohs. The win by AlphaGo was chosen byScience as one of theBreakthrough of the Year runners-up on 22 December 2016.[10]
At the 2017Future of Go Summit, theMaster version of AlphaGo beatKe Jie, the number one ranked player in the world at the time, in athree-game match, after which AlphaGo was awarded professional 9-dan by theChinese Weiqi Association.[11]
After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas.[12] The self-taught AlphaGo Zero achieved a 100–0 victory against the early competitive version of AlphaGo, and its successorAlphaZero was perceived as the world's top player in Go by the end of the 2010s.[13][14]
Go is considered much more difficult for computers to win than other games such aschess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much largerbranching factor makes it prohibitively difficult to use traditional AI methods such asalpha–beta pruning,tree traversal andheuristic search.[5][15]
Almost two decades afterIBM's computerDeep Blue beat world chess championGarry Kasparov in the1997 match, the strongest Go programs usingartificial intelligence techniques only reached aboutamateur 5-dan level,[4] and still could not beat a professional Go player without ahandicap.[5][6][16] In 2012, the software programZen, running on a four PC cluster, beatMasaki Takemiya (9p) twice at five- and four-stone handicaps.[17] In 2013,Crazy Stone beatYoshio Ishida (9p) at a four-stone handicap.[18]
According to DeepMind'sDavid Silver, the AlphaGo research project was formed around 2014 to test how well a neural network usingdeep learning can compete at Go.[19] AlphaGo represents a significant improvement over previous Go programs. In 500 games against other available Go programs, including Crazy Stone and Zen, AlphaGo running on a single computer won all but one.[20] In a similar matchup, AlphaGo running on multiple computers won all 500 games played against other Go programs, and 77% of games played against AlphaGo running on a single computer. The distributed version in October 2015 was using 1,202CPUs and 176GPUs.[4]
In October 2015, the distributed version of AlphaGo defeated theEuropean Go championFan Hui,[21] a2-dan (out of 9 dan possible) professional, five to zero.[6][22] This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap.[23] The announcement of the news was delayed until 27 January 2016 to coincide with the publication of a paper in the journalNature[4] describing the algorithms used.[6]
AlphaGo played South Korean professional Go playerLee Sedol, ranked 9-dan, one of the best players at Go,[16][needs update] with five games taking place at theFour Seasons Hotel inSeoul, South Korea on 9, 10, 12, 13, and 15 March 2016,[24][25] which were video-streamed live.[26] Out of five games, AlphaGo won four games and Lee won the fourth game which made him recorded as the only human player who beat AlphaGo in all of its 74 official games.[27] AlphaGo ran on Google's cloud computing with its servers located in the United States.[28] The match usedChinese rules with a 7.5-pointkomi, and each side had two hours of thinking time plus three 60-secondbyoyomi periods.[29] The version of AlphaGo playing against Lee used a similar amount of computing power as was used in the Fan Hui match.[30]The Economist reported that it used 1,920CPUs and 280GPUs.[31] At the time of play, Lee Sedol had the second-highest number of Go international championship victories in the world after South Korean playerLee Chang-ho who kept the world championship title for 16 years.[32] Since there is no single official method ofranking in international Go, the rankings may vary among the sources. While he was ranked top sometimes, some sources ranked Lee Sedol as the fourth-best player in the world at the time.[33][34] AlphaGo was not specifically trained to face Lee nor was designed to compete with any specific human players.
The first three games were won by AlphaGo following resignations by Lee.[35][36] However, Lee beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then continued to achieve a fourth win, winning the fifth game by resignation.[37]
The prize was US$1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, includingUNICEF.[38] Lee Sedol received $150,000 for participating in all five games and an additional $20,000 for his win in Game 4.[29]
In June 2016, at a presentation held at a university in the Netherlands, Aja Huang, one of the Deep Mind team, revealed that they had patched the logical weakness that occurred during the 4th game of the match between AlphaGo and Lee, and that after move 78 (which was dubbed the "divine move" by many professionals), it would play as intended and maintain Black's advantage. Before move 78, AlphaGo was leading throughout the game, but Lee's move caused the program's computing powers to be diverted and confused.[39] Huang explained that AlphaGo's policy network of finding the most accurate move order and continuation did not precisely guide AlphaGo to make the correct continuation after move 78, since its value network did not determine Lee's 78th move as being the most likely, and therefore when the move was made AlphaGo could not make the right adjustment to the logical continuation.[40]
On 29 December 2016, a new account on theTygem server named "Magister" (shown as 'Magist' at the server's Chinese version) from South Korea began to play games with professional players. It changed its account name to "Master" on 30 December, then moved to the FoxGo server on 1 January 2017. On 4 January, DeepMind confirmed that the "Magister" and the "Master" were both played by an updated version of AlphaGo, calledAlphaGo Master.[41][42] As of 5 January 2017, AlphaGo Master's online record was 60 wins and 0 losses,[43] including three victories over Go's top-ranked player,Ke Jie,[44] who had been quietly briefed in advance that Master was a version of AlphaGo.[43] After losing to Master,Gu Li offered a bounty of 100,000yuan (US$14,400) to the first human player who could defeat Master.[42] Master played at the pace of 10 games per day. Many quickly suspected it to be an AI player due to little or no resting between games. Its adversaries included many world champions such asKe Jie,Park Jeong-hwan,Yuta Iyama,Tuo Jiaxi,Mi Yuting,Shi Yue,Chen Yaoye, Li Qincheng,Gu Li,Chang Hao, Tang Weixing,Fan Tingyu,Zhou Ruiyang,Jiang Weijie,Chou Chun-hsun,Kim Ji-seok,Kang Dong-yun,Park Yeong-hun, andWon Seong-jin; national champions or world championship runners-up such asLian Xiao,Tan Xiao, Meng Tailing, Dang Yifei, Huang Yunsong,Yang Dingxin, Gu Zihao, Shin Jinseo,Cho Han-seung, and An Sungjoon. All 60 games except one were fast-paced games with three 20 or 30 secondsbyo-yomi. Master offered to extend the byo-yomi to one minute when playing withNie Weiping in consideration of his age. After winning its 59th game Master revealed itself in the chatroom to be controlled by Dr.Aja Huang of the DeepMind team,[45] then changed its nationality to the United Kingdom. After these games were completed, the co-founder ofDeepMind,Demis Hassabis, said in a tweet, "we're looking forward to playing some official, full-length games later [2017] in collaboration with Go organizations and experts".[41][42]
Go experts were impressed by the program's performance and its nonhuman play style; Ke Jie stated that "After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong... I would go as far as to say not a single human has touched the edge of the truth of Go."[43]
In the Future of Go Summit held inWuzhen in May 2017,AlphaGo Master played three games with Ke Jie, the world No.1 ranked player, as well as two games with several top Chinese professionals, one pair Go game and one against a collaborating team of five human players.[46]
Google DeepMind offered 1.5 million dollar winner prizes for the three-game match between Ke Jie and Master while the losing side took 300,000 dollars.[47][48] Master won all three games against Ke Jie,[49][50] after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[11]
After winning its three-game match against Ke Jie, the top-rated world Go player, AlphaGo retired. DeepMind also disbanded the team that worked on the game to focus on AI research in other areas.[12] After the Summit, Deepmind published 50 full length AlphaGo vs AlphaGo matches, as a gift to the Go community.[51]
AlphaGo's team published an article in the journalNature on 19 October 2017, introducing AlphaGo Zero, a version without human data and stronger than any previous human-champion-defeating version.[52] By playing games against itself, AlphaGo Zero surpassed the strength ofAlphaGo Lee in three days by winning 100 games to 0, reached the level ofAlphaGo Master in 21 days, and exceeded all the old versions in 40 days.[53]
In a paper released onarXiv on 5 December 2017, DeepMind claimed that it generalized AlphaGo Zero's approach into a single AlphaZero algorithm, which achieved within 24 hours a superhuman level of play in the games ofchess,shogi, andGo by defeating world-champion programs,Stockfish,Elmo, and 3-day version of AlphaGo Zero in each case.[54]
On 11 December 2017, DeepMind released an AlphaGo teaching tool on its website[55] to analyze winning rates of differentGo openings as calculated byAlphaGo Master.[56] The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.[56]
An early version of AlphaGo was tested on hardware with various numbers ofCPUs andGPUs, running in asynchronous or distributed mode. Two seconds of thinking time was given to each move. The resultingElo ratings are listed below.[4] In the matches with more time per move higher ratings are achieved.
| Configuration | Search threads | No. of CPU | No. of GPU | Elo rating |
|---|---|---|---|---|
| Single[4]p. 10–11 | 40 | 48 | 1 | 2,181 |
| Single | 40 | 48 | 2 | 2,738 |
| Single | 40 | 48 | 4 | 2,850 |
| Single | 40 | 48 | 8 | 2,890 |
| Distributed | 12 | 428 | 64 | 2,937 |
| Distributed | 24 | 764 | 112 | 3,079 |
| Distributed | 40 | 1,202 | 176 | 3,140 |
| Distributed | 64 | 1,920 | 280 | 3,168 |
In May 2016, Google unveiled its own proprietary hardware "tensor processing units", which it stated had already been deployed in multiple internal projects at Google, including the AlphaGo match against Lee Sedol.[57][58]
In theFuture of Go Summit in May 2017, DeepMind disclosed that the version of AlphaGo used in this Summit wasAlphaGo Master,[59][60] and revealed that it had measured the strength of different versions of the software. AlphaGo Lee, the version used against Lee, could give AlphaGo Fan, the version used in AlphaGo vs. Fan Hui, three stones, and AlphaGo Master was even three stones stronger.[61]
| Versions | Hardware | Elo rating | Date | Results |
|---|---|---|---|---|
| AlphaGo Fan | 176GPUs,[53] distributed | 3,144[52] | Oct 2015 | 5:0 againstFan Hui |
| AlphaGo Lee | 48TPUs,[53] distributed | 3,739[52] | Mar 2016 | 4:1 againstLee Sedol |
| AlphaGo Master | 4 TPUs,[53] single machine | 4,858[52] | May 2017 | 60:0 against professional players; Future of Go Summit |
| AlphaGo Zero (40 block) | 4 TPUs,[53] single machine | 5,185[52] | Oct 2017 | 100:0 against AlphaGo Lee 89:11 against AlphaGo Master |
| AlphaZero (20 block) | 4 TPUs, single machine | 5,018[63] | Dec 2017 | 60:40 against AlphaGo Zero (20 block) |
As of 2016, AlphaGo's algorithm uses a combination ofmachine learning andtree search techniques, combined with extensive training, both from human and computer play. It usesMonte Carlo tree search, guided by a "value network" and a "policy network", both implemented usingdeep neural network technology.[5][4] A limited amount of game-specific feature detection pre-processing (for example, to highlight whether a move matches anakade pattern) is applied to the input before it is sent to the neural networks.[4] The networks areconvolutional neural networks with 12 layers, trained byreinforcement learning.[4]
The system's neural networks were initially bootstrapped from human gameplay expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves.[21] 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, usingreinforcement learning to improve its play.[5] To avoid "disrespectfully" wasting its opponent's time, the program is specifically programmed to resign if its assessment of win probability falls beneath a certain threshold; for the match against Lee, the resignation threshold was set to 20%.[64]
Toby Manning, the match referee for AlphaGo vs. Fan Hui, has described the program's style as "conservative".[65] AlphaGo's playing style strongly favours greater probability of winning by fewer points over lesser probability of winning by more points.[19] Its strategy of maximising its probability of winning is distinct from what human players tend to do which is to maximise territorial gains, and explains some of its odd-looking moves.[66] It makes a lot of opening moves that have never or seldom been made by humans. It likes to useshoulder hits, especially if the opponent is over concentrated.[67]
AlphaGo's March 2016 victory was a major milestone in artificial intelligence research.[68] 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.[68][69][70] Most experts thought a Go program as powerful as AlphaGo was at least five years away;[71] some experts thought that it would take at least another decade before computers would beat Go champions.[4][72][73] Most observers at the beginning of the 2016 matches expected Lee to beat AlphaGo.[68]
With games such as checkers (that has beensolved by theChinook computer engine), chess, and now Go won by computers, victories at popular board games can no longer serve as major milestones for artificial intelligence in the way that they used to.Deep Blue'sMurray Campbell called AlphaGo's victory "the end of an era... board games are more or less done and it's time to move on."[68]
When compared with Deep Blue orWatson, AlphaGo's underlying algorithms are potentially more general-purpose and may be evidence that the scientific community is making progress towardsartificial general intelligence.[19][74] Some commentators believe AlphaGo's victory makes for a good opportunity for society to start preparing for the possible future impact ofmachines with general purpose intelligence. As noted by entrepreneur Guy Suter, AlphaGo only knows how to play Go and doesn't possess general-purpose intelligence; "[It] couldn't just wake up one morning and decide it wants to learn how to use firearms."[68] AI researcherStuart Russell said that AI systems such as AlphaGo have progressed quicker and become more powerful than expected, and we must therefore develop methods to ensure they "remain under human control".[75] Some scholars, such asStephen Hawking, warned (in May 2015 before the matches) that some future self-improving AI could gain actual general intelligence, leading to an unexpectedAI takeover; other scholars disagree: AI expert Jean-Gabriel Ganascia believes that "Things like 'common sense'... may never be reproducible",[76] 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."[75] Computer scientistRichard Sutton said "I don't think people should be scared... but I do think people should be paying attention."[77]
In China, AlphaGo was a "Sputnik moment" which helped convince the Chinese government to prioritize and dramatically increase funding for artificial intelligence.[78]
In 2017, the DeepMind AlphaGo team received the inauguralIJCAIMarvin 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 ProfessorMichael 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."[79]
Go is a popular game in China, Japan and Korea, and the 2016 matches were watched by perhaps a hundred million people worldwide.[68][80] Many top Go players characterized AlphaGo's unorthodox plays as seemingly-questionable moves that initially befuddled onlookers, but made sense in hindsight:[72] "All but the very best Go players craft their style by imitating top players. AlphaGo seems to have totally original moves it creates itself."[68] AlphaGo appeared to have unexpectedly become much stronger, even when compared with its October 2015 match[81] where a computer had beaten a Go professional for the first time ever without the advantage of a handicap.[82] The day after Lee's first defeat, Jeong Ahram, the lead Go correspondent for one of South Korea's biggest daily newspapers, said "Last night was very gloomy... Many people drank alcohol."[83] TheKorea Baduk Association, the organization that oversees Go professionals in South Korea, awarded AlphaGo an honorary 9-dan title for exhibiting creative skills and pushing forward the game's progress.[84]
China'sKe Jie, an 18-year-old generally recognized as the world's best Go player at the time,[33][85] initially claimed that he would be able to beat AlphaGo, but declined to play against it for fear that it would "copy my style".[85] As the matches progressed, Ke Jie went back and forth, stating that "it is highly likely that I (could) lose" after analysing the first three matches,[86] but regaining confidence after AlphaGo displayed flaws in the fourth match.[87]
Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary general of theInternational 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.[82]
After game two, Lee said he felt "speechless": "From the very beginning of the match, I could never manage an upper hand for one single move. It was AlphaGo's total victory."[88] Lee apologized for his losses, stating after game three that "I misjudged the capabilities of AlphaGo and felt powerless."[68] He emphasized that the defeat was "Lee Se-dol's defeat" and "not a defeat of mankind".[27][76] 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."[76] Lee called his game four victory a "priceless win that I (would) not exchange for anything."[27]
OnRotten Tomatoes the documentary has an average rating of 100% from 10 reviews.[89]
Michael Rechtshaffen of theLos Angeles Times gave the documentary a positive review and said: "It helps matters when you have a group of engaging human subjects like soft-spoken Sedol, who's as intensively contemplative as the game itself, contrasted by the spirited, personable Fan Hui, the Paris-based European champ who accepts an offer to serve as an advisor for the DeepMind team after suffering a demoralizing AI trouncing". He also mentioned that with the passion of Hauschka's Volker Bertelmann, the film's producer, this documentary shows many unexpected sequences, including strategic and philosophical components.[90] (Rechtshaffen, 2017John Defore ofThe Hollywood Reporter, wrote this documentary is "an involving sports-rivalry doc with an AI twist." "In the end, observers wonder if AlphaGo's odd variety of intuition might not kill Go as an intellectual pursuit but shift its course, forcing the game's scholars to consider it from new angles. So maybe it isn't time to welcome our computer overlords, and won't be for a while - maybe they'll teach us to be better thinkers before turning us into their slaves."[91]
Greg Kohs, the director of the film, said "The complexity of the game of Go, combined with the technical depth of an emerging technology like artificial intelligence seemed like it might create an insurmountable barrier for a film like this. The fact that I was so innocently unaware of Go and AlphaGo actually proved to be beneficial. It allowed me to approach the action and interviews with pure curiosity, the kind that helps make any subject matter emotionally accessible." Kohs also said that "Unlike the film's human characters – who turn their curious quest for knowledge into an epic spectacle with great existential implications, who dare to risk their reputation and pride to contest that curiosity – AI might not yet possess the ability to empathize. But it can teach us profound things about our humanness – the way we play board games, the way we think and feel and grow. It's a deep, vast premise, but my hope is, by sharing it, we can discover something within ourselves we never saw before".[92]
Hajin Lee, a former professional Go player, described this documentary as being "beautifully filmed". In addition to the story itself, the feelings and atmosphere were also conveyed through different scene arrangements. For example, the close-up shots of Lee Sedol when he realizes that the AlphaGo AI is intelligent, the atmospheric scene of the Korean commentator's distress and affliction following the first defeat, and the tension being held inside the room. The documentary also tells a story by describing the background of AlphaGo technology and the customs of the Korean Go community. She suggests some areas to be covered additionally. For instance, the details of the AI prior to AlphaGo, the confidence and pride of the professional Go players, and the shifting of perspective to the Go AI between and after the match as "If anything could be added, I would include information about the primitive level of top Go A.I.s before AlphaGo, and more about professional Go players' lives and pride, to provide more context for Lee Sedol's pre-match confidence, and Go players' changing perception of AlphaGo as the match advanced".[93](Lee, 2017).
Fan Hui, a professional Go player, and former player with AlphaGo said that "DeepMind had trained AlphaGo by showing it many strong amateur games of Go to develop its understanding of how a human plays before challenging it to play versions of itself thousands of times, a novel form of reinforcement learning which had given it the ability to rival an expert human. History had been made, and centuries of received learning overturned in the process. The program was free to learn the game for itself.[94]
James Vincent, a reporter from The Verge, comments that "It prods and pokes viewers with unsubtle emotional cues, like a reality TV show would. "Now, you should be nervous; now you should feel relieved". The AlphaGo footage slowly captures the moment when Lee Sedol acknowledges the true power of AlphaGo AI. In the first game, he had more experience than his human-programmed AI, so he thought it would be easy to beat the AI. However, the early game dynamics were not what he expected. After losing the first match, he became more nervous and lost confidence. Afterward, he reacted to attacks by saying that he just wanted to win the match, unintentionally displaying his anger, and acting in an unusual way. Also, he spends 12 minutes on one move, while AlphaGo only takes a minute and a half to respond. AlphaGo weighs each alternative equally and consistently. No reaction to Lee's fight. Instead, the game continues as if he was not there.
James also said that "suffice to say that humanity does land at least one blow on the machines, through Lee's so-called "divine move". "More likely, the forces of automation we'll face will be impersonal and incomprehensible. They'll come in the form of star ratings we can't object to, and algorithms we can't fully understand. Dealing with the problems of AI will take a perspective that looks beyond individual battles. AlphaGo is worth seeing because it raises these questions"[95](Vincent, 2017)
Murray Shanahan, a professor of cognitive robotics at Imperial College London, critics that "Go is an extraordinary game but it represents what we can do with AI in all kinds of other spheres," says Murray Shanahan, professor of cognitive robotics at Imperial College London and senior research scientist at DeepMind, says. "In just the same way there are all kinds of realms of possibility within Go that have not been discovered, we could never have imagined the potential for discovering drugs and other materials."[94]
Facebook has also been working on its own Go-playing systemdarkforest, also based on combining machine learning andMonte Carlo tree search.[65][96] Although a strong player against other computer Go programs, as of early 2016, it had not yet defeated a professional human player.[97] Darkforest has lost to CrazyStone and Zen and is estimated to be of similar strength to CrazyStone and Zen.[98]
DeepZenGo, a system developed with support from video-sharing websiteDwango and theUniversity of Tokyo, lost 2–1 in November 2016 to Go masterCho Chikun, who holds the record for the largest number of Go title wins in Japan.[99][100]
A 2018 paper inNature cited AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules.[101][102] Systems consisting ofMonte Carlo tree search guided by neural networks have since been explored for a wide array of applications.[103]
AlphaGo Master (white) v.Tang Weixing (31 December 2016), AlphaGo won by resignation. White 36 was widely praised.
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| First 99 moves |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Moves 100–186 (149 at 131, 150 at 130) |
The documentary filmAlphaGo[9][89] raised hopes thatLee Sedol andFan Hui would have benefitted from their experience of playing AlphaGo, but as of May 2018[update], their ratings were little changed;Lee Sedol was ranked 11th in the world, andFan Hui 545th.[104] On 19 November 2019, Lee announced his retirement from professional play, arguing that he could never be the top overall player of Go due to the increasing dominance of AI. Lee referred to them as being "an entity that cannot be defeated".[105]
More than 100 million people watched the AlphaGo-Lee matches, Mr. Hassabis said.
{{cite news}}: CS1 maint: numeric names: authors list (link)
Media related toAlphaGo at Wikimedia Commons
Quotations related toAlphaGo at Wikiquote| Overview | |
|---|---|
| Equipment | |
| Terms | |
| Strategy and tactics | |
| History | |
| Competition | |
| Games and matches | |
| Art and media | |
| Computers | |
| Organizations | |
| Other | |
| Computer programs |
| ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Machine learning |
| ||||||||||
| Generative AI |
| ||||||||||
| See also | |||||||||||
