1639:
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".(Lee, 2017).
1630:
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".
98:
1652:
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.
819:" 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. 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.
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very best Go players craft their style by imitating top players. AlphaGo seems to have totally original moves it creates itself." AlphaGo appeared to have unexpectedly become much stronger, even when compared with its
October 2015 match where a computer had beaten a Go professional for the first time ever without the advantage of a handicap. 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." The
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1626:, 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."
1586:, an 18-year-old generally recognized as the world's best Go player at the time, initially claimed that he would be able to beat AlphaGo, but declined to play against it for fear that it would "copy my style". 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, but regaining confidence after AlphaGo displayed flaws in the fourth match.
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1566:, 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."
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770:, South Korea on 9, 10, 12, 13, and 15 March 2016, which were video-streamed live. 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. AlphaGo ran on Google's cloud computing with its servers located in the United States. The match used
1597:
emphasized that the defeat was "Lee Se-dol's defeat" and "not a defeat of mankind". 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." Lee called his game four victory a "priceless win that I (would) not exchange for anything."
1622:
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. (Rechtshaffen, 2017 John Defore of
1655:
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
1638:
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
1629:
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
1489:
AlphaGo's March 2016 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
961:
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
1668:
Director Greg Kohs brings out all of the drama and rush of these games to show the selection process for the master of Go. On this small stage, the human Go champion and the AI challenger engage in an intense duel. In this documentary, Kohs looks into how the human mind functions under pressure, the
1642:
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
1596:
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." Lee apologized for his losses, stating after game three that "I misjudged the capabilities of AlphaGo and felt powerless." He
1574:
Go is a popular game in China, Japan and Korea, and the 2016 matches were watched by perhaps a hundred million people worldwide. Many top Go players characterized AlphaGo's unorthodox plays as seemingly-questionable moves that initially befuddled onlookers, but made sense in hindsight: "All but the
837:
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"
738:(out of 9 dan possible) professional, five to zero. This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap. The announcement of the news was delayed until 27 January 2016 to coincide with the publication of a paper in the journal
1621:
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
704:
can compete at Go. 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. In a similar matchup, AlphaGo running on multiple computers won all 500
1651:
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
1457:
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. Once it had reached a certain
997:
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. After the Summit, Deepmind published 50 full length AlphaGo vs AlphaGo matches, as a gift to the Go
1659:
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
1471:
Toby
Manning, the match referee for AlphaGo vs. Fan Hui, has described the program's style as "conservative". AlphaGo's playing style strongly favours greater probability of winning by fewer points over lesser probability of winning by more points. Its strategy of maximising its probability of
530:
method) by extensive training, both from human and computer play. 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.
993:
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. Master won all three games against Ke Jie, after which AlphaGo was awarded professional 9-dan by the
Chinese Weiqi Association.
814:
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
800:, 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. AlphaGo was not specifically trained to face Lee nor was designed to compete with any specific human players.
1462:
to improve its play. 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%.
1276:, 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.
854:(US$ 14,400) to the first human player who could defeat Master. 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 as
796:. At the time of play, Lee Sedol had the second-highest number of Go international championship victories in the world after South Korean player Lee Changho who kept the world championship title for 16 years. Since there is no single official method of
562:
professional without handicap. 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 the
6928:
545th. 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".
1690:. Although a strong player against other computer Go programs, as of early 2016, it had not yet defeated a professional human player. Darkforest has lost to CrazyStone and Zen and is estimated to be of similar strength to CrazyStone and Zen.
803:
The first three games were won by AlphaGo following resignations by Lee. 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.
9151:
Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen; Hassabis, Demis (7 December 2018).
7513:
Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen; Hassabis, Demis (7 December 2018).
609:
After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas. The self-taught AlphaGo Zero achieved a 100–0 victory against the early competitive version of AlphaGo, and its successor
1472:
winning is distinct from what human players tend to do which is to maximise territorial gains, and explains some of its odd-looking moves. It makes a lot of opening moves that have never or seldom been made by humans. It likes to use
9539:
7831:
7147:; Maddison, Chris J.; Guez, Arthur; Sifre, Laurent; Driessche, George van den; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal;
1660:
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."
1524:. As noted by entrepreneur Guy Suter, AlphaGo only knows how to play Go and doesn't possess general-purpose intelligence; " couldn't just wake up one morning and decide it wants to learn how to use firearms." AI researcher
1022:
on 19 October 2017, introducing AlphaGo Zero, a version without human data and stronger than any previous human-champion-defeating version. By playing games against itself, AlphaGo Zero surpassed the strength of
8526:
1528:
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". Some scholars, such as
9947:
7895:
1643:
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.
1490:
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.
990:
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.
10099:
8126:
1656:
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" (Vincent, 2017)
1541:'... 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." Computer scientist
10568:
10182:
8095:
10147:
8876:; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen;
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1038:
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 of
9039:
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1074:. 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.
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1579:, 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.
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team), 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.
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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
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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 1,202
9873:
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7240:
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357:
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10307:
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448:
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1516:, AlphaGo's underlying algorithms are potentially more general-purpose and may be evidence that the scientific community is making progress towards
11975:
958:, said in a tweet, "we're looking forward to playing some official, full-length games later in collaboration with Go organizations and experts".
10516:
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1593:, 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.
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1533:, warned (in May 2015 before the matches) that some future self-improving AI could gain actual general intelligence, leading to an unexpected
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7853:
1520:. Some commentators believe AlphaGo's victory makes for a good opportunity for society to start preparing for the possible future impact of
9698:
6982:
492:
8604:
8315:
8058:"We are using roughly same amount of compute power as in Fan Hui match: distributing search over further machines has diminishing returns"
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8403:
244:
209:
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10395:
7764:
842:. As of 5 January 2017, AlphaGo Master's online record was 60 wins and 0 losses, including three victories over Go's top-ranked player,
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8362:
807:
The prize was US$ 1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, including
8843:
8023:
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1265:", which it stated had already been deployed in multiple internal projects at Google, including the AlphaGo match against Lee Sedol.
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9782:
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8195:
11136:
10478:
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1458:
degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using
1442:
technology. A limited amount of game-specific feature detection pre-processing (for example, to highlight whether a move matches a
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cited AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules. Systems consisting of
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222:
10803:
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periods. The version of AlphaGo playing against Lee used a similar amount of computing power as was used in the Fan Hui match.
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950:
of the DeepMind team, then changed its nationality to the United Kingdom. After these games were completed, the co-founder of
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8486:
7988:
626:, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger
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367:
321:
276:
271:
1552:" which helped convince the Chinese government to prioritize and dramatically increase funding for artificial intelligence.
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in consideration of his age. After winning its 59th game Master revealed itself in the chatroom to be controlled by Dr.
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would have benefitted from their experience of playing AlphaGo, but as of May 2018, their ratings were little changed;
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On 11 December 2017, DeepMind released an AlphaGo teaching tool on its website to analyze winning rates of different
811:. Lee Sedol received $ 150,000 for participating in all five games and an additional $ 20,000 for his win in Game 4.
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significance of errors, the actions of the computer, and its inventiveness. (Frederic and Mary Ann Brussat, n.d.)
1090:, running in asynchronous or distributed mode. Two seconds of thinking time was given to each move. The resulting
13350:
12968:
9838:
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7642:
1521:
1517:
658:
116:
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8909:
483:. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name
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12219:
12159:
11757:
487:. After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as
8745:
8337:
11752:
11441:
9494:
8880:(5 December 2017). "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm".
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1447:
10854:
9666:
8694:
1509:
called AlphaGo's victory "the end of an era... board games are more or less done and it's time to move on."
13021:
12194:
11591:
11548:
11501:
11496:
10780:
10456:
Tian, Yuandong; Zhu, Yan (2015). "Better Computer Go Player with Neural Network and Long-term Prediction".
10426:
10053:
7378:
12471:
10266:
797:
13392:
13337:
12245:
11541:
11467:
11237:
9973:
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10885:
7886:"Google DeepMind is going to take on the world's best Go player in a luxury 5-star hotel in South Korea"
846:, who had been quietly briefed in advance that Master was a version of AlphaGo. After losing to Master,
13413:
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11804:
11405:
10896:
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1502:
650:
567:. The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled
126:
109:
10236:"Review: Ancient Chinese board game treated with NFL-like drama and intrigue in documentary 'AlphaGo'"
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1589:
Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary general of the
13315:
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9448:
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7209:
97:
13382:
13320:
13284:
13001:
12909:
12441:
12169:
11426:
11338:
11030:
10842:
9699:"AlphaGo: Google's artificial intelligence to take on world champion of ancient Chinese board game"
9229:
1328:
1308:
1087:
793:
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728:
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said "I don't think people should be scared... but I do think people should be paying attention."
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1083:
907:
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662:
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515:
89:
73:
12668:
9154:"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play"
7516:"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play"
1473:
13507:
13387:
13327:
13302:
13200:
13061:
12991:
12587:
12556:
12458:
12453:
12174:
11934:
11653:
11648:
11169:
11024:
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10271:
10148:"...if today's performance was its true capability, then it doesn't deserve to play against me"
8600:
7789:
6939:
1623:
1459:
1451:
1334:
1302:
1262:
722:
686:
631:
543:
199:
9806:"An AI expert says Google's Go-playing program is missing 1 key feature of human intelligence"
8395:
700:, the AlphaGo research project was formed around 2014 to test how well a neural network using
13075:
12996:
12680:
12204:
12189:
12154:
11842:
11742:
11610:
11225:
11036:
10530:
7765:"In Major AI Breakthrough, Google System Secretly Beats Top Player at the Ancient Game of Go"
1190:
977:
938:, and An Sungjoon. All 60 games except one were fast-paced games with three 20 or 30 seconds
887:
599:
495:
without learning from human games. AlphaGo Zero was then generalized into a program known as
12072:
10427:"A snappy and mind-boggling documentary about a 3000-year-old board game and a A.I. program"
9734:
9527:
8517:"The world's best Go player says he still has "one last move" to defeat Google's AlphaGo AI"
8366:
1446:
pattern) is applied to the input before it is sent to the neural networks. The networks are
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12575:
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7002:
735:
559:
141:
12673:
12642:
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12570:
12563:
12514:
12507:
12493:
9938:"Google's AlphaGo AI program strong but not perfect, says defeated South Korean Go player"
7301:
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8:
13512:
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13170:
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13011:
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12663:
12656:
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11799:
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11300:
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8578:
7155:(28 January 2016). "Mastering the game of Go with deep neural networks and tree search".
1701:
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1434:
techniques, combined with extensive training, both from human and computer play. It uses
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9191:
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7198:
6966:
1537:; other scholars disagree: AI expert Jean-Gabriel Ganascia believes that "Things like '
771:
763:
343:
12542:
1094:
are listed below. In the matches with more time per move higher ratings are achieved.
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12238:
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guided by neural networks have since been explored for a wide array of applications.
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in May 2017, DeepMind disclosed that the version of AlphaGo used in this Summit was
13502:
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7890:
7535:
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762:, ranked 9-dan, one of the best players at Go, with five games taking place at the
740:
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622:
Go is considered much more difficult for computers to win than other games such as
519:
465:
259:
194:
179:
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12748:
12395:
10729:"Beyond games: a systematic review of neural Monte Carlo tree search applications"
10121:
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13136:
13054:
12932:
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9924:
More than 100 million people watched the AlphaGo-Lee matches, Mr. Hassabis said.
7822:"Première défaite d'un professionnel du go contre une intelligence artificielle"
7233:"Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning"
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13185:
13124:
13047:
12862:
12802:
12777:
12734:
12717:
12705:
12082:
12047:
12037:
11862:
11620:
11446:
11054:
10754:
10620:
10175:"In Seoul, Go Games Spark Interest (and Concern) About Artificial Intelligence"
10122:"Chinese Go master Ke Jie says he could lose to AlphaGo : The DONG-A ILBO"
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674:
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591:
569:
484:
20:
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10890:
10651:
10054:"Google artificial intelligence program beats S. Korean Go pro with 4–1 score"
9985:
9414:
8458:
8431:
8151:
13496:
13262:
13068:
12835:
12027:
12007:
11924:
11603:
11202:
11102:
10804:"Former Go champion beaten by DeepMind retires after declaring AI invincible"
10762:
10490:
10025:
9529:"A computer has beaten a professional at the world's most complex board game"
9266:
8997:
8782:
7993:
7919:"YouTube will livestream Google's AI playing Go superstar Lee Sedol in March"
7186:
1734:(31 December 2016), AlphaGo won by resignation. White 36 was widely praised.
1559:
1438:, guided by a "value network" and a "policy network", both implemented using
935:
919:
911:
784:
701:
690:
527:
189:
12867:
9216:; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian;
9178:
9153:
8732:; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian;
7540:
7515:
7151:; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore;
13355:
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13190:
13089:
12354:
12332:
12113:
11944:
11359:
11315:
10995:
10948:
10693:
10207:
9868:
9595:"GOOGLE'S ALPHAGO BEATS WORLD CHAMPION IN THIRD MATCH TO WIN ENTIRE SERIES"
9385:
9274:
9187:
8790:
8467:
8119:"Google's AI machine v world champion of 'Go': everything you need to know"
7858:
7549:
7194:
1731:
1538:
1377:
1082:
An early version of AlphaGo was tested on hardware with various numbers of
1007:
931:
899:
895:
838:
and the "Master" were both played by an updated version of AlphaGo, called
670:
547:
488:
333:
10727:
Kemmerling, Marco; Lütticke, Daniel; Schmitt, Robert H. (1 January 2024).
13437:
13360:
13175:
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12857:
12792:
12772:
12344:
12209:
11980:
11889:
11884:
11506:
11484:
11310:
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9217:
9121:
8938:
8733:
8218:"Google's AI beats world Go champion in first of five matches – BBC News"
7923:
7463:
7461:
7093:
1708:, who holds the record for the largest number of Go title wins in Japan.
1534:
1494:
1431:
1024:
943:
816:
539:
362:
347:
63:
60:
12400:
10685:
9258:
8774:
7178:
942:. Master offered to extend the byo-yomi to one minute when playing with
35:
13449:
13230:
13205:
13111:
12942:
12797:
12787:
12727:
12103:
12062:
12057:
11970:
11879:
11787:
11699:
11679:
11164:
11062:
10652:"Planning chemical syntheses with deep neural networks and symbolic AI"
9495:"AlphaGo beats human Go champ in milestone for artificial intelligence"
9320:
8254:
8222:
7854:"Google's AI AlphaGo to take on world No 1 Lee Sedol in live broadcast"
6996:
6976:
1705:
1682:
1067:
891:
879:
863:
630:
makes it prohibitively difficult to use traditional AI methods such as
614:
was perceived as the world's top player in Go by the end of the 2010s.
469:
43:
8626:
8396:"Demis Hassabis on Twitter: "Excited to share an update on #AlphaGo!""
7458:
7034:"Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol"
518:
algorithm to find its moves based on knowledge previously acquired by
13476:
13195:
12877:
12782:
12390:
12337:
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12315:
12098:
12067:
11965:
11809:
11772:
11709:
11663:
11658:
11643:
11255:
11084:
11001:
10932:
10650:
Segler, Martwin H.S.; Preuss, Mike; Waller, Mark P. (29 March 2018).
10462:
9671:
9213:
9065:
9035:
8901:
8729:
8724:
7574:
Temporal Difference Learning of Position Evaluation in the Game of Go
7144:
7012:
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6992:
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1400:
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1011:
947:
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639:
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551:
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472:
397:
161:
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9376:
9351:
9316:"Go Grandmaster Lee Sedol Grabs Consolation Win Against Google's AI"
8575:"Exploring the mysteries of Go with AlphaGo and China's top players"
778:, and each side had two hours of thinking time plus three 60-second
13267:
13082:
12887:
12764:
12000:
11832:
11096:
10745:
10668:
9942:
9777:
9739:
9634:
9630:"Google DeepMind computer AlphaGo sweeps human champ in Go matches"
9566:
9452:
9150:
8905:
8886:
8872:
8847:
7826:
7701:
7635:"Zen computer Go program beats Takemiya Masaki with just 4 stones!"
7512:
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7274:
6949:
1677:
951:
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669:
level, and still could not beat a professional Go player without a
476:
234:
156:
10869:
10591:"Humans strike back: Korean Go master bests AI in board game bout"
12961:
12872:
12823:
12807:
12123:
11914:
11837:
11737:
11732:
11684:
11130:
10849:
10058:
9221:
9002:"Google supercharges machine learning tasks with TPU custom chip"
8737:
8654:"Revamped AlphaGo Wins First Game Against Chinese Go Grandmaster"
8487:"Humans Mourn Loss After Google Is Unmasked as China's Go Master"
8399:
8250:"Google AI wins second Go game against world champion – BBC News"
8073:
7790:"Special Computer Go insert covering the AlphaGo v Fan Hui match"
7306:
7302:"Match 1 – Google DeepMind Challenge Match: Lee Sedol vs AlphaGo"
6925:
6917:
1321:
779:
731:
727:
In October 2015, the distributed version of AlphaGo defeated the
535:
402:
10776:
9401:"The Go Files: AI computer clinches victory against Go champion"
7370:
1614:
the documentary has an average rating of 100% from 10 reviews.
13220:
13215:
12816:
12710:
12138:
12118:
11990:
11782:
11007:
10943:
10837:
10561:"Go master Cho wins best-of-three series against Japan-made AI"
10021:"How victory for Google's Go AI is stoking fear in South Korea"
9770:
9212:; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis;
8728:; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis;
8673:"Google's AlphaGo Continues Dominance With Second Win in China"
1583:
983:
855:
843:
808:
595:
508:
480:
12947:
8601:"World No.1 Go player Ke Jie takes on upgraded AlphaGo in May"
8312:"Human champion certain he'll beat AI at ancient Chinese game"
8182:"Korean Go master proves human intuition still powerful in Go"
7609:"Computer scores big win against humans in ancient game of Go"
1027:
in three days by winning 100 games to 0, reached the level of
922:; national champions or world championship runners-up such as
598:, the number one ranked player in the world at the time, in a
507:. AlphaZero has in turn been succeeded by a program known as
13442:
13272:
12405:
11939:
11919:
11909:
11904:
11899:
11894:
11857:
11689:
11231:
9902:"Google's Computer Program Beats Lee Se-dol in Go Tournament"
9667:"A Google computer victorious over the world's 'Go' champion"
9224:; Sifre, Laurent; Driessche, George van den; Graepel, Thore;
9092:"New version of AlphaGo self-trained and much more efficient"
8740:; Sifre, Laurent; Driessche, George van den; Graepel, Thore;
8432:"Google reveals secret test of AI bot to beat top Go players"
7733:
7571:
Schraudolph, Nicol N.; Terrence, Peter Dayan; Sejnowski, J.,
1696:, a system developed with support from video-sharing website
1556:
1043:
1039:
1035:
834:
767:
623:
504:
500:
10901:
602:, after which AlphaGo was awarded professional 9-dan by the
19:
This article is about a computer program. For the film, see
13454:
11929:
11243:
10365:"Fan Hui: What I learned from losing to DeepMind's AlphaGo"
9125:
8282:"Google DeepMind AI wins final Go match for 4–1 series win"
7697:"AlphaGo's unusual moves prove its AI prowess, experts say"
6958:, subfield of machine learning that is the basis of AlphaGo
1261:
In May 2016, Google unveiled its own proprietary hardware "
13471:
10090:"Google AlphaGo 'can't beat me' says China Go grandmaster"
10051:
9492:
8338:"In Two Moves, AlphaGo and Lee Sedol Redefined the Future"
8018:
8016:
7270:"Google achieves AI 'breakthrough' by beating Go champion"
1555:
In 2017, the DeepMind AlphaGo team received the inaugural
1031:
in 21 days, and exceeded all the old versions in 40 days.
573:, directed by Greg Kohs. The win by AlphaGo was chosen by
10726:
9874:
International Joint Conference on Artificial Intelligence
7401:"From AI to protein folding: Our Breakthrough runners-up"
710:
706:
646:
10331:"AlphaGo" Film Review: The Art of Capturing the Essence"
9869:"Marvin Minsky Medal for Outstanding Achievements in AI"
7570:
7470:"After Win in China, AlphaGo's Designers Explore New AI"
9735:"Rise of the Machines: Keep an eye on AI, experts warn"
8013:
7139:
9773:"Game over? New AI challenge to human smarts (Update)"
7950:
7948:
7946:
1426:
As of 2016, AlphaGo's algorithm uses a combination of
12283:
9766:
9764:
9762:
8146:
8144:
7729:"Google AlphaGo AI clean sweeps European Go champion"
7488:"AlphaZero Crushes Stockfish In New 1,000-Game Match"
10479:"No Go: Facebook fails to spoil Google's big AI day"
10304:
Science Media Awards & Summit in the Hub (SMASH)
9488:
9486:
9484:
9482:
9480:
9478:
9476:
9474:
8355:
8314:. Associated Press. 22 February 2016. Archived from
7135:
7133:
7131:
7129:
7127:
7125:
7123:
7121:
7119:
685:) twice at five- and four-stone handicaps. In 2013,
558:, the first time a computer Go program has beaten a
550:
on a full-sized 19×19 board. In March 2016, it beat
8838:
8836:
8834:
8832:
8830:
7943:
1680:has also been working on its own Go-playing system
1646:
1476:, especially if the opponent is over concentrated.
1016:AlphaGo's team published an article in the journal
758:AlphaGo played South Korean professional Go player
10649:
10083:
10081:
9771:Mariëtte Le Roux; Pascale Mollard (8 March 2016).
9759:
9230:"Mastering the game of Go without human knowledge"
8746:"Mastering the game of Go without human knowledge"
8141:
7690:
7688:
7686:
1600:
1058:, and 3-day version of AlphaGo Zero in each case.
74:deepmind.com/research/highlighted-research/alphago
9471:
8850:official website. 18 October 2017. Archived from
7116:
13494:
9839:"Beijing Wants A.I. to Be Made in China by 2030"
9732:
9352:"Google AI algorithm masters ancient game of Go"
8827:
8429:
8425:
8423:
8421:
7758:
7756:
7430:
7428:
1663:
10150:. M.hankooki.com. 14 March 2016. Archived from
10078:
9967:
9965:
9728:
9726:
8929:
8927:
8116:
7683:
1686:, also based on combining machine learning and
10449:
10396:"How will we face being defeated by machines?"
10052:JEE HEUN KAHNG; SE YOUNG LEE (15 March 2016).
9592:
9493:Steven Borowiec; Tracey Lien (12 March 2016).
8390:
8052:
7365:
7363:
6988:Samuel's learning computer checkers (draughts)
12299:
11375:
10917:
10172:
9899:
9624:
9622:
8720:
8718:
8716:
8714:
8712:
8418:
7981:
7954:
7753:
7425:
1001:
511:which learns without being taught the rules.
442:
11389:
10720:
10233:
9962:
9935:
9723:
8924:
8627:"Ke Jie vs. AlphaGo: 8 things you must know"
8481:
8479:
8477:
8363:"黄士杰：AlphaGo李世石人机大战第四局问题已解决date=8 July 2016"
7694:
6983:Matchbox Educable Noughts and Crosses Engine
1493:With games such as checkers (that has been "
1479:
10515:: CS1 maint: numeric names: authors list (
10202:
10200:
10087:
9803:
8691:"Full length games for Go players to enjoy"
7360:
7334:"Google's AlphaGo gets 'divine' Go ranking"
930:, Meng Tailing, Dang Yifei, Huang Yunsong,
499:, which played additional games, including
12306:
12292:
11382:
11368:
10924:
10910:
10424:
9619:
8966:"Google Isn't Playing Games With New Chip"
8709:
7603:
7601:
7294:
7227:
7225:
7223:
7221:
7219:
1522:machines with general purpose intelligence
833:On 29 December 2016, a new account on the
747:
449:
435:
34:
10744:
10667:
10476:
10461:
10234:Rechtshaffen, Michael (26 October 2017).
9664:
9375:
9313:
9177:
9122:"【柯洁战败解密】AlphaGo Master最新架构和算法，谷歌云与TPU拆解"
8885:
8474:
8457:
8386:
8384:
7814:
7539:
1704:, lost 2–1 in November 2016 to Go master
1633:
10362:
10300:"Five Questions for Filmmakers: AlphaGo"
10197:
9562:"Google's AI beats human champion at Go"
9114:
9024:
8963:
8683:
7068:DeepMind Artificial Intelligence AlphaGo
716:
538:, the original AlphaGo became the first
479:Technologies, an acquired subsidiary of
10801:
10455:
10393:
10018:
9398:
8150:
7598:
7264:
7262:
7260:
7258:
7216:
6895:Moves 100–186 (149 at 131, 150 at 130)
475:. It was developed by the London-based
13495:
10531:"Strachey Lecture – Dr Demis Hassabis"
10425:Brussat, Frederic; Brussat, Mary Ann.
10264:
9971:
9525:
9349:
9302:AlphaGo Lee... 12 convolutional layers
9132:from the original on 17 September 2017
8996:
8555:from the original on 30 September 2020
8381:
8024:"이세돌 vs 알파고, '구글 딥마인드 챌린지 매치' 기자회견 열려"
7074:from the original on 14 September 2019
1415:60:40 against AlphaGo Zero (20 block)
1050:by defeating world-champion programs,
965:
12287:
11363:
10905:
10708:from the original on 12 December 2021
10621:"Go and make some drugs The Engineer"
10601:from the original on 25 November 2016
10363:Williams, Rhiannon (8 October 2020).
10358:
10356:
10279:from the original on 13 February 2023
9974:"Go players react to computer defeat"
9836:
9607:from the original on 16 December 2016
9350:Gibney, Elizabeth (27 January 2016).
9345:
9343:
9328:from the original on 17 November 2017
8945:from the original on 12 December 2017
8912:from the original on 12 December 2017
8844:"AlphaGo Zero: Learning from scratch"
8808:from the original on 24 November 2020
8633:from the original on 14 December 2017
8529:from the original on 19 November 2020
7916:
7665:"「アマ六段の力。天才かも」囲碁棋士、コンピューターに敗れる 初の公式戦"
7494:from the original on 12 November 2020
822:
677:, running on a four PC cluster, beat
16:Artificial intelligence that plays Go
12220:Generative adversarial network (GAN)
11344:
10297:
10000:from the original on 30 January 2016
9881:from the original on 21 October 2017
9542:from the original on 28 January 2016
8670:
8651:
8551:(in Chinese). 澎湃新闻. 4 January 2017.
7931:from the original on 9 February 2016
7834:from the original on 29 January 2016
7802:from the original on 2 February 2016
7762:
7741:from the original on 29 January 2016
7615:from the original on 31 January 2016
7467:
7282:from the original on 2 December 2021
7255:
7243:from the original on 30 January 2016
534:In October 2015, in a match against
11160:Quantum Artificial Intelligence Lab
10802:Vincent, James (27 November 2019).
10783:from the original on 15 August 2021
10571:from the original on 14 August 2017
10328:
9704:Australian Broadcasting Corporation
8430:Elizabeth Gibney (4 January 2017).
8098:from the original on 14 August 2017
7957:"Lee Se-dol shows AlphaGo beatable"
7866:from the original on 14 August 2017
7381:from the original on 3 January 2018
7348:from the original on 7 October 2016
7044:from the original on 26 August 2016
6962:Glossary of artificial intelligence
982:In the Future of Go Summit held in
13:
13398:Taiwan Chi Yuan Culture Foundation
11306:Generative pre-trained transformer
10876:, including links to AlphaGo games
10541:from the original on 16 March 2016
10497:from the original on 15 March 2016
10437:from the original on 15 April 2023
10406:from the original on 15 April 2023
10394:Vincent, James (12 October 2017).
10375:from the original on 28 March 2023
10353:
10341:from the original on 15 April 2023
10310:from the original on 28 March 2023
10265:Defore, John (29 September 2017).
10246:from the original on 15 April 2023
10185:from the original on 18 March 2016
10128:from the original on 15 March 2016
10102:from the original on 13 March 2016
10033:from the original on 21 March 2016
9950:from the original on 13 March 2016
9914:from the original on 18 March 2016
9849:from the original on 11 April 2018
9818:from the original on 12 March 2016
9785:from the original on 14 March 2016
9747:from the original on 13 March 2016
9733:Mariëtte Le Roux (12 March 2016).
9679:from the original on 8 August 2020
9646:from the original on 13 March 2016
9574:from the original on 10 March 2016
9459:from the original on 24 April 2017
9340:
9042:from the original on 16 April 2021
8607:from the original on 15 April 2017
8292:from the original on 15 March 2016
8262:from the original on 10 March 2016
8230:from the original on 10 March 2018
8129:from the original on 15 March 2016
8030:. 22 February 2016. Archived from
7969:from the original on 14 March 2016
7587:from the original on 28 March 2017
7314:from the original on 29 March 2017
6924:was ranked 11th in the world, and
1672:
1368:60:0 against professional players;
661:, the strongest Go programs using
96:
14:
13529:
10886:Estimated 2017 rating of Alpha Go
10830:
10814:from the original on 7 April 2020
10631:from the original on 3 April 2018
10066:from the original on 28 July 2017
9711:from the original on 15 June 2016
9526:Connor, Steve (27 January 2016).
9429:from the original on 18 June 2016
9399:Chouard, Tanguy (12 March 2016).
9292:from the original on 18 July 2018
8978:from the original on 29 June 2016
8001:from the original on 4 March 2016
7917:Novet, Jordan (4 February 2016).
7898:from the original on 2 March 2016
7709:from the original on 17 July 2016
7667:. MSN Sankei News. Archived from
7413:from the original on 17 June 2022
6979:, open-source learning Go program
514:AlphaGo and its successors use a
13470:
12258:
12257:
12237:
11343:
11334:
11333:
10848:
10836:
10795:
10769:
10643:
10613:
10583:
10553:
10523:
9593:Dave Gershgorn (12 March 2016).
9507:from the original on 13 May 2018
9102:from the original on 3 June 2017
9072:from the original on 3 June 2017
9012:from the original on 18 May 2016
8964:McMillan, Robert (18 May 2016).
8819:
8497:from the original on 26 May 2019
8117:Steven Borowiec (9 March 2016).
7997:(in Chinese). 23 February 2016.
7446:from the original on 3 June 2017
7208:
7104:from the original on 28 May 2017
6942:, wrote first Go program in 1968
6901:
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2014:
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1751:
1744:
1647:Technology and AI-related fields
1512:When compared with Deep Blue or
1466:
1061:
744:describing the algorithms used.
673:. In 2012, the software program
583:runners-up on 22 December 2016.
13351:All Japan Student Go Federation
10470:
10431:www.spiritualityandpractice.com
10418:
10387:
10322:
10291:
10258:
10227:
10173:CHOE SANG-HUN (15 March 2016).
10166:
10140:
10114:
10045:
10019:Zastrow, Mark (15 March 2016).
10012:
9929:
9900:CHOE SANG-HUN (16 March 2016).
9893:
9861:
9830:
9797:
9691:
9658:
9586:
9554:
9519:
9441:
9392:
9307:
9202:
9144:
9084:
9054:
8990:
8957:
8894:
8866:
8664:
8645:
8619:
8593:
8577:. 10 April 2017. Archived from
8567:
8541:
8509:
8406:from the original on 4 May 2019
8330:
8304:
8274:
8242:
8210:
8194:. 14 March 2016. Archived from
8174:
8110:
8080:
8046:
7955:Yoon Sung-won (14 March 2016).
7910:
7878:
7846:
7782:
7721:
7657:
7627:
7564:
7506:
7480:
7393:
1722:
1601:AlphaGo documentary film (2016)
1569:
1518:artificial general intelligence
1484:
693:(9p) at a four-stone handicap.
117:Artificial general intelligence
13428:Games played with Go equipment
12170:Recurrent neural network (RNN)
12160:Differentiable neural computer
10298:Kohs, Greg (23 October 2018).
9936:John Ribeiro (12 March 2016).
9062:"各版alphago实力对比 master能让李世石版3子"
8549:"横扫中日韩棋手斩获59胜的Master发话：我是阿尔法狗"
7830:(in French). 27 January 2016.
7763:Metz, Cade (27 January 2016).
7695:John Riberio (14 March 2016).
7326:
7086:
7056:
7026:
1098:Configuration and performance
665:techniques only reached about
1:
12215:Variational autoencoder (VAE)
12175:Long short-term memory (LSTM)
11442:Computational learning theory
10931:
10477:HAL 90210 (28 January 2016).
10088:Neil Connor (11 March 2016).
9804:Tanya Lewis (11 March 2016).
8935:"AlphaGo教学工具上线 樊麾：使用Master版本"
7019:
1664:Spiritual and cultural expert
1448:convolutional neural networks
1394:89:11 against AlphaGo Master
12195:Convolutional neural network
10329:Lee, Hajin (28 April 2017).
9837:Mozur, Paul (20 July 2017).
8365:(in Chinese). Archived from
7795:. British Go Journal. 2017.
1617:Michael Rechtshaffen of the
1605:
1421:
850:offered a bounty of 100,000
788:reported that it used 1,920
7:
13393:Singapore Weiqi Association
13338:International Go Federation
12190:Multilayer perceptron (MLP)
9665:Sofia Yan (12 March 2016).
9314:Cade Metz (13 March 2016).
9032:"AlphaGo官方解读让三子 对人类高手没这种优势"
8156:"Rating List of 2016-01-01"
7436:"中国围棋协会授予AlphaGo职业九段 并颁发证书"
6956:Deep reinforcement learning
6932:
1591:International Go Federation
1450:with 12 layers, trained by
1280:Configuration and strength
1077:
798:ranking in international Go
152:Natural language processing
10:
13534:
13105:Long Ode to Watching Weiqi
12266:Artificial neural networks
12180:Gated recurrent unit (GRU)
11406:Differentiable programming
10755:10.1007/s10489-023-05240-w
9972:Gibney, Elizabeth (2016).
9449:"韩国研究新版AlphaGo：穿越而来展示未来围棋"
9006:Google Cloud Platform Blog
8693:. Deepmind. Archived from
8671:Metz, Cade (25 May 2017).
8652:Metz, Cade (23 May 2017).
7468:Metz, Cade (27 May 2017).
1399:
1392:100:0 against AlphaGo Lee
1376:
1353:
1327:
1301:
1242:
1225:
1208:
1189:
1172:
1155:
1138:
1121:
1005:
1002:AlphaGo Zero and AlphaZero
975:
969:
826:
751:
720:
653:beat world chess champion
617:
205:Hybrid intelligent systems
127:Recursive self-improvement
18:
13465:
13406:
13316:Chinese Weiqi Association
13298:Australian Go Association
13283:
13161:
13040:
12982:
12925:
12834:
12696:
12418:
12353:
12323:
12233:
12147:
12091:
12020:
11953:
11825:
11725:
11718:
11672:
11636:
11599:Artificial neural network
11579:
11455:
11422:Automatic differentiation
11395:
11329:
11295:Attention Is All You Need
11286:
11265:
11218:
11189:
11182:
11152:
11123:
11116:
11077:
11046:
11017:
10976:
10969:
10962:
10939:
9986:10.1038/nature.2016.19255
9415:10.1038/nature.2016.19553
8459:10.1038/nature.2017.21253
7989:"李世乭：即使Alpha Go得到升级也一样能赢"
6946:Chinook (draughts player)
1548:In China, AlphaGo was a "
1480:Responses to 2016 victory
934:, Gu Zihao, Shin Jinseo,
645:Almost two decades after
604:Chinese Weiqi Association
524:artificial neural network
69:
54:
42:
33:
13518:Applied machine learning
13383:Mind Sports Organisation
13321:Hong Kong Go Association
13032:Lee's broken ladder game
12910:Nihon Ki-in Hall of Fame
11427:Neuromorphic engineering
11390:Differentiable computing
10882:, with archive and games
10267:"'AlphaGo': Film Review"
7611:. CNN. 28 January 2016.
754:AlphaGo versus Lee Sedol
696:According to DeepMind's
594:version of AlphaGo beat
581:Breakthrough of the Year
542:program to beat a human
329:Artificial consciousness
13371:Korea Baduk Association
13333:French Federation of Go
13293:American Go Association
13248:Monte Carlo tree search
13022:The Game of the Century
12200:Residual neural network
11616:Artificial Intelligence
9179:10.1126/science.aar6404
9096:American Go Association
8971:The Wall Street Journal
8902:"AlphaGo teaching tool"
8028:Korea Baduk Association
7541:10.1126/science.aar6404
1717:Monte Carlo tree search
1688:Monte Carlo tree search
1577:Korea Baduk Association
1499:Chinook draughts player
1436:Monte Carlo tree search
1263:tensor processing units
1034:In a paper released on
748:Match against Lee Sedol
663:artificial intelligence
565:Korea Baduk Association
516:Monte Carlo tree search
491:, which was completely
200:Evolutionary algorithms
90:Artificial intelligence
13388:New Zealand Go Society
13328:European Go Federation
13303:British Go Association
13063:The Girl Who Played Go
12917:Professional handicaps
12313:
11170:Tensor Processing Unit
10853:Quotations related to
10565:The Japan Times Online
10272:The Hollywood Reporter
9220:; Lillicrap, Timothy;
8736:; Lillicrap, Timothy;
6940:Albert Lindsey Zobrist
1634:Professional Go player
1624:The Hollywood Reporter
1460:reinforcement learning
1452:reinforcement learning
1406:4 TPUs, single machine
1383:4 TPUs, single machine
1359:4 TPUs, single machine
723:AlphaGo versus Fan Hui
544:professional Go player
101:
13002:AlphaGo vs. Lee Sedol
12155:Neural Turing machine
11743:Human image synthesis
10625:www.theengineer.co.uk
6906:The documentary film
978:AlphaGo versus Ke Jie
976:Further information:
717:Match against Fan Hui
522:, specifically by an
100:
13343:Irish Go Association
13145:The Surrounding Game
12698:Strategy and tactics
12246:Computer programming
12225:Graph neural network
11800:Text-to-video models
11778:Text-to-image models
11626:Large language model
11611:Scientific computing
11417:Statistical manifold
11412:Information geometry
10845:at Wikimedia Commons
10733:Applied Intelligence
10567:. 24 November 2016.
8941:. 11 December 2017.
7894:. 22 February 2016.
7409:. 22 December 2016.
7237:Google Research Blog
7094:"AlphaGo | DeepMind"
7003:Pluribus (poker bot)
729:European Go champion
142:General game playing
13243:Future of Go Summit
13171:Computer Go UEC Cup
13012:Blood-vomiting game
12992:AlphaGo vs. Fan Hui
11592:In-context learning
11432:Pattern recognition
11301:Future of Go Summit
10892:AlphaGo - The Movie
10686:10.1038/nature25978
10678:2018Natur.555..604S
10335:hajinlee.medium.com
9877:. 19 October 2017.
9570:. 27 January 2016.
9455:. 11 January 2017.
9368:2016Natur.529..445G
9259:10.1038/nature24270
9251:2017Natur.550..354S
9228:(19 October 2017).
9170:2018Sci...362.1140S
9164:(6419): 1140–1144.
8775:10.1038/nature24270
8767:2017Natur.550..354S
8744:(19 October 2017).
8491:Wall Street Journal
8450:2017Natur.541..142G
8398:. Demis Hassabis's
7862:. 5 February 2016.
7737:. 28 January 2016.
7532:2018Sci...362.1140S
7526:(6419): 1140–1144.
7278:. 27 January 2016.
7239:. 27 January 2016.
7179:10.1038/nature16961
7171:2016Natur.529..484S
1702:University of Tokyo
1440:deep neural network
1371:Future of Go Summit
1281:
1270:Future of Go Summit
1099:
972:Future of Go Summit
966:Future of Go Summit
588:Future of Go Summit
294:Machine translation
210:Systems integration
147:Knowledge reasoning
84:Part of a series on
30:
13433:Go and mathematics
13414:Benson's algorithm
13376:Myongji University
13017:Ear-reddening game
12997:AlphaGo vs. Ke Jie
12852:Dunhuang Go Manual
12185:Echo state network
12073:Jürgen Schmidhuber
11768:Facial recognition
11763:Speech recognition
11673:Software libraries
11047:In popular culture
10535:The New Livestream
10179:The New York Times
10095:The Telegraph (UK)
9907:The New York Times
9843:The New York Times
8854:on 19 October 2017
8525:. 4 January 2017.
8493:. 5 January 2017.
8394:(4 January 2017).
8369:on 10 October 2018
8318:on 24 January 2019
7645:on 1 February 2016
7064:"DeepMind AlphaGO"
6967:Go and mathematics
6912:raised hopes that
1564:Michael Wooldridge
1279:
1097:
823:Sixty online games
764:Four Seasons Hotel
632:alpha–beta pruning
102:
28:
13490:
13489:
13253:Smart Game Format
12984:Games and matches
12938:Ranks and ratings
12281:
12280:
12043:Stephen Grossberg
12016:
12015:
11357:
11356:
11282:
11281:
11178:
11177:
11112:
11111:
11073:
11072:
10963:Computer programs
10841:Media related to
10662:(7698): 604–610.
9642:. 12 March 2016.
9500:Los Angeles Times
9245:(7676): 354–359.
8761:(7676): 354–359.
8603:. 10 April 2017.
8288:. 15 March 2016.
8187:The Korean Herald
8056:(11 March 2016).
7490:. 17 April 2019.
7344:. 15 March 2016.
7338:The Straits Times
7165:(7587): 484–489.
7040:. 12 March 2016.
6899:
6898:
6890:
6889:
4317:
4316:
4308:
4307:
1619:Los Angeles Times
1419:
1418:
1259:
1258:
1070:as calculated by
829:Master (software)
774:with a 7.5-point
556:a five-game match
459:
458:
195:Bayesian networks
122:Intelligent agent
79:
78:
13525:
13475:
13474:
13132:Sensei's Library
13119:The Master of Go
13027:Kamakura jubango
13007:Atomic bomb game
12900:European players
12308:
12301:
12294:
12285:
12284:
12271:Machine learning
12261:
12260:
12241:
11996:Action selection
11986:Self-driving car
11793:Stable Diffusion
11758:Speech synthesis
11723:
11722:
11587:Machine learning
11463:Gradient descent
11384:
11377:
11370:
11361:
11360:
11347:
11346:
11337:
11336:
11321:Google Workspace
11187:
11186:
11121:
11120:
11117:Machine learning
10974:
10973:
10967:
10966:
10926:
10919:
10912:
10903:
10902:
10893:
10874:Sensei's Library
10866:
10865:
10863:Official website
10852:
10840:
10824:
10823:
10821:
10819:
10799:
10793:
10792:
10790:
10788:
10773:
10767:
10766:
10748:
10739:(1): 1020–1046.
10724:
10718:
10717:
10715:
10713:
10671:
10647:
10641:
10640:
10638:
10636:
10627:. 3 April 2018.
10617:
10611:
10610:
10608:
10606:
10587:
10581:
10580:
10578:
10576:
10557:
10551:
10550:
10548:
10546:
10527:
10521:
10520:
10514:
10506:
10504:
10502:
10474:
10468:
10467:
10465:
10453:
10447:
10446:
10444:
10442:
10422:
10416:
10415:
10413:
10411:
10400:www.theverge.com
10391:
10385:
10384:
10382:
10380:
10360:
10351:
10350:
10348:
10346:
10326:
10320:
10319:
10317:
10315:
10295:
10289:
10288:
10286:
10284:
10262:
10256:
10255:
10253:
10251:
10231:
10225:
10224:
10222:
10220:
10204:
10195:
10194:
10192:
10190:
10170:
10164:
10163:
10161:
10159:
10154:on 15 March 2016
10144:
10138:
10137:
10135:
10133:
10118:
10112:
10111:
10109:
10107:
10085:
10076:
10075:
10073:
10071:
10049:
10043:
10042:
10040:
10038:
10016:
10010:
10009:
10007:
10005:
9969:
9960:
9959:
9957:
9955:
9933:
9927:
9926:
9921:
9919:
9897:
9891:
9890:
9888:
9886:
9865:
9859:
9858:
9856:
9854:
9834:
9828:
9827:
9825:
9823:
9811:Business Insider
9801:
9795:
9794:
9792:
9790:
9768:
9757:
9756:
9754:
9752:
9730:
9721:
9720:
9718:
9716:
9707:. 8 March 2016.
9695:
9689:
9688:
9686:
9684:
9662:
9656:
9655:
9653:
9651:
9640:Associated Press
9626:
9617:
9616:
9614:
9612:
9590:
9584:
9583:
9581:
9579:
9558:
9552:
9551:
9549:
9547:
9531:
9523:
9517:
9516:
9514:
9512:
9490:
9469:
9468:
9466:
9464:
9445:
9439:
9438:
9436:
9434:
9396:
9390:
9389:
9379:
9347:
9338:
9337:
9335:
9333:
9311:
9305:
9304:
9299:
9297:
9291:
9234:
9206:
9200:
9199:
9181:
9148:
9142:
9141:
9139:
9137:
9118:
9112:
9111:
9109:
9107:
9088:
9082:
9081:
9079:
9077:
9058:
9052:
9051:
9049:
9047:
9028:
9022:
9021:
9019:
9017:
8994:
8988:
8987:
8985:
8983:
8961:
8955:
8954:
8952:
8950:
8931:
8922:
8921:
8919:
8917:
8898:
8892:
8891:
8889:
8870:
8864:
8863:
8861:
8859:
8840:
8825:
8824:
8823:
8817:
8815:
8813:
8807:
8750:
8722:
8707:
8706:
8704:
8702:
8697:on 5 August 2019
8687:
8681:
8680:
8668:
8662:
8661:
8649:
8643:
8642:
8640:
8638:
8623:
8617:
8616:
8614:
8612:
8597:
8591:
8590:
8588:
8586:
8581:on 11 April 2017
8571:
8565:
8564:
8562:
8560:
8545:
8539:
8538:
8536:
8534:
8513:
8507:
8506:
8504:
8502:
8483:
8472:
8471:
8461:
8427:
8416:
8415:
8413:
8411:
8388:
8379:
8378:
8376:
8374:
8359:
8353:
8352:
8350:
8348:
8334:
8328:
8327:
8325:
8323:
8308:
8302:
8301:
8299:
8297:
8278:
8272:
8271:
8269:
8267:
8246:
8240:
8239:
8237:
8235:
8214:
8208:
8207:
8205:
8203:
8198:on 12 April 2016
8178:
8172:
8171:
8169:
8167:
8162:on 18 March 2016
8158:. Archived from
8148:
8139:
8138:
8136:
8134:
8114:
8108:
8107:
8105:
8103:
8084:
8078:
8077:
8071:
8069:
8050:
8044:
8043:
8041:
8039:
8020:
8011:
8010:
8008:
8006:
7985:
7979:
7978:
7976:
7974:
7952:
7941:
7940:
7938:
7936:
7914:
7908:
7907:
7905:
7903:
7891:Business Insider
7882:
7876:
7875:
7873:
7871:
7850:
7844:
7843:
7841:
7839:
7818:
7812:
7811:
7809:
7807:
7801:
7794:
7786:
7780:
7779:
7777:
7775:
7760:
7751:
7750:
7748:
7746:
7725:
7719:
7718:
7716:
7714:
7692:
7681:
7680:
7678:
7676:
7671:on 24 March 2013
7661:
7655:
7654:
7652:
7650:
7641:. Archived from
7631:
7625:
7624:
7622:
7620:
7605:
7596:
7595:
7594:
7592:
7586:
7579:
7568:
7562:
7561:
7543:
7510:
7504:
7503:
7501:
7499:
7484:
7478:
7477:
7465:
7456:
7455:
7453:
7451:
7432:
7423:
7422:
7420:
7418:
7397:
7391:
7390:
7388:
7386:
7367:
7358:
7357:
7355:
7353:
7342:straitstimes.com
7330:
7324:
7323:
7321:
7319:
7310:. 8 March 2016.
7298:
7292:
7291:
7289:
7287:
7266:
7253:
7252:
7250:
7248:
7229:
7214:
7213:
7212:
7206:
7137:
7114:
7113:
7111:
7109:
7090:
7084:
7083:
7081:
7079:
7060:
7054:
7053:
7051:
7049:
7030:
6972:Leela (software)
6886:
6885:
6879:
6878:
6872:
6871:
6865:
6864:
6858:
6857:
6851:
6850:
6844:
6843:
6837:
6836:
6830:
6829:
6823:
6822:
6816:
6815:
6809:
6808:
6802:
6801:
6795:
6794:
6788:
6787:
6781:
6780:
6774:
6773:
6767:
6766:
6760:
6759:
6751:
6750:
6744:
6743:
6737:
6736:
6730:
6729:
6723:
6722:
6716:
6715:
6709:
6708:
6702:
6701:
6695:
6694:
6688:
6687:
6681:
6680:
6674:
6673:
6667:
6666:
6660:
6659:
6653:
6652:
6646:
6645:
6639:
6638:
6632:
6631:
6625:
6624:
6616:
6615:
6609:
6608:
6602:
6601:
6595:
6594:
6588:
6587:
6581:
6580:
6574:
6573:
6567:
6566:
6560:
6559:
6553:
6552:
6546:
6545:
6539:
6538:
6532:
6531:
6525:
6524:
6518:
6517:
6511:
6510:
6504:
6503:
6497:
6496:
6490:
6489:
6481:
6480:
6474:
6473:
6467:
6466:
6460:
6459:
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6452:
6446:
6445:
6439:
6438:
6432:
6431:
6425:
6424:
6418:
6417:
6411:
6410:
6404:
6403:
6397:
6396:
6390:
6389:
6383:
6382:
6376:
6375:
6369:
6368:
6362:
6361:
6355:
6354:
6346:
6345:
6339:
6338:
6332:
6331:
6325:
6324:
6318:
6317:
6311:
6310:
6304:
6303:
6297:
6296:
6290:
6289:
6283:
6282:
6276:
6275:
6269:
6268:
6262:
6261:
6255:
6254:
6248:
6247:
6241:
6240:
6234:
6233:
6227:
6226:
6220:
6219:
6211:
6210:
6204:
6203:
6197:
6196:
6190:
6189:
6183:
6182:
6176:
6175:
6169:
6168:
6162:
6161:
6155:
6154:
6148:
6147:
6141:
6140:
6134:
6133:
6127:
6126:
6120:
6119:
6113:
6112:
6106:
6105:
6099:
6098:
6092:
6091:
6085:
6084:
6076:
6075:
6069:
6068:
6062:
6061:
6055:
6054:
6048:
6047:
6041:
6040:
6034:
6033:
6027:
6026:
6020:
6019:
6013:
6012:
6006:
6005:
5999:
5998:
5992:
5991:
5985:
5984:
5978:
5977:
5971:
5970:
5964:
5963:
5957:
5956:
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5941:
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5934:
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5927:
5926:
5920:
5919:
5913:
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5877:
5871:
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5863:
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5843:
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5815:
5814:
5806:
5805:
5799:
5798:
5792:
5791:
5785:
5784:
5778:
5777:
5771:
5770:
5764:
5763:
5757:
5756:
5750:
5749:
5743:
5742:
5736:
5735:
5729:
5728:
5722:
5721:
5715:
5714:
5708:
5707:
5701:
5700:
5694:
5693:
5687:
5686:
5680:
5679:
5671:
5670:
5664:
5663:
5657:
5656:
5650:
5649:
5643:
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2018:
2017:
2009:
2008:
2002:
2001:
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1939:
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1897:
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1775:
1769:
1768:
1762:
1761:
1755:
1754:
1748:
1747:
1741:
1737:
1736:
1711:A 2018 paper in
1428:machine learning
1282:
1278:
1100:
1096:
890:, Tang Weixing,
628:branching factor
600:three-game match
520:machine learning
466:computer program
451:
444:
437:
358:Existential risk
180:Machine learning
81:
80:
38:
31:
27:
13533:
13532:
13528:
13527:
13526:
13524:
13523:
13522:
13493:
13492:
13491:
13486:
13469:
13461:
13402:
13279:
13157:
13152:The Weiqi Devil
13056:The Divine Move
13036:
12978:
12957:Honorary titles
12933:Go professional
12921:
12883:Oskar Korschelt
12845:Classic of Arts
12830:
12692:
12442:Board positions
12414:
12349:
12319:
12312:
12282:
12277:
12229:
12143:
12109:Google DeepMind
12087:
12053:Geoffrey Hinton
12012:
11949:
11875:Project Debater
11821:
11719:Implementations
11714:
11668:
11632:
11575:
11517:Backpropagation
11451:
11437:Tensor calculus
11391:
11388:
11358:
11353:
11325:
11278:
11261:
11219:Language models
11214:
11174:
11148:
11124:Neural networks
11108:
11069:
11042:
11013:
10958:
10954:Google DeepMind
10935:
10930:
10891:
10861:
10860:
10833:
10828:
10827:
10817:
10815:
10800:
10796:
10786:
10784:
10775:
10774:
10770:
10725:
10721:
10711:
10709:
10648:
10644:
10634:
10632:
10619:
10618:
10614:
10604:
10602:
10589:
10588:
10584:
10574:
10572:
10559:
10558:
10554:
10544:
10542:
10529:
10528:
10524:
10508:
10507:
10500:
10498:
10475:
10471:
10454:
10450:
10440:
10438:
10423:
10419:
10409:
10407:
10392:
10388:
10378:
10376:
10361:
10354:
10344:
10342:
10327:
10323:
10313:
10311:
10296:
10292:
10282:
10280:
10263:
10259:
10249:
10247:
10240:www.latimes.com
10232:
10228:
10218:
10216:
10213:Rotten Tomatoes
10206:
10205:
10198:
10188:
10186:
10171:
10167:
10157:
10155:
10146:
10145:
10141:
10131:
10129:
10120:
10119:
10115:
10105:
10103:
10086:
10079:
10069:
10067:
10050:
10046:
10036:
10034:
10017:
10013:
10003:
10001:
9970:
9963:
9953:
9951:
9934:
9930:
9917:
9915:
9898:
9894:
9884:
9882:
9867:
9866:
9862:
9852:
9850:
9835:
9831:
9821:
9819:
9802:
9798:
9788:
9786:
9769:
9760:
9750:
9748:
9731:
9724:
9714:
9712:
9697:
9696:
9692:
9682:
9680:
9663:
9659:
9649:
9647:
9628:
9627:
9620:
9610:
9608:
9600:Popular Science
9591:
9587:
9577:
9575:
9560:
9559:
9555:
9545:
9543:
9535:The Independent
9524:
9520:
9510:
9508:
9491:
9472:
9462:
9460:
9447:
9446:
9442:
9432:
9430:
9397:
9393:
9377:10.1038/529445a
9362:(7587): 445–6.
9348:
9341:
9331:
9329:
9312:
9308:
9295:
9293:
9289:
9232:
9226:Hassabis, Demis
9208:
9207:
9203:
9149:
9145:
9135:
9133:
9128:. 24 May 2017.
9120:
9119:
9115:
9105:
9103:
9098:. 24 May 2017.
9090:
9089:
9085:
9075:
9073:
9068:. 24 May 2017.
9060:
9059:
9055:
9045:
9043:
9038:. 25 May 2017.
9030:
9029:
9025:
9015:
9013:
9000:(18 May 2016).
8995:
8991:
8981:
8979:
8962:
8958:
8948:
8946:
8933:
8932:
8925:
8915:
8913:
8900:
8899:
8895:
8878:Hassabis, Demis
8871:
8867:
8857:
8855:
8842:
8841:
8828:
8818:
8811:
8809:
8805:
8748:
8742:Hassabis, Demis
8723:
8710:
8700:
8698:
8689:
8688:
8684:
8669:
8665:
8650:
8646:
8636:
8634:
8629:. 27 May 2017.
8625:
8624:
8620:
8610:
8608:
8599:
8598:
8594:
8584:
8582:
8573:
8572:
8568:
8558:
8556:
8547:
8546:
8542:
8532:
8530:
8515:
8514:
8510:
8500:
8498:
8485:
8484:
8475:
8428:
8419:
8409:
8407:
8389:
8382:
8372:
8370:
8361:
8360:
8356:
8346:
8344:
8336:
8335:
8331:
8321:
8319:
8310:
8309:
8305:
8295:
8293:
8280:
8279:
8275:
8265:
8263:
8248:
8247:
8243:
8233:
8231:
8216:
8215:
8211:
8201:
8199:
8180:
8179:
8175:
8165:
8163:
8149:
8142:
8132:
8130:
8115:
8111:
8101:
8099:
8086:
8085:
8081:
8067:
8065:
8054:Demis Hassabis
8051:
8047:
8037:
8035:
8034:on 3 March 2016
8022:
8021:
8014:
8004:
8002:
7987:
7986:
7982:
7972:
7970:
7962:The Korea Times
7953:
7944:
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7883:
7879:
7869:
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7693:
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7662:
7658:
7648:
7646:
7633:
7632:
7628:
7618:
7616:
7607:
7606:
7599:
7590:
7588:
7584:
7577:
7569:
7565:
7511:
7507:
7497:
7495:
7486:
7485:
7481:
7466:
7459:
7449:
7447:
7442:. 27 May 2017.
7434:
7433:
7426:
7416:
7414:
7399:
7398:
7394:
7384:
7382:
7371:"AlphaGo Movie"
7369:
7368:
7361:
7351:
7349:
7332:
7331:
7327:
7317:
7315:
7300:
7299:
7295:
7285:
7283:
7268:
7267:
7256:
7246:
7244:
7231:
7230:
7217:
7207:
7153:Hassabis, Demis
7149:Sutskever, Ilya
7138:
7117:
7107:
7105:
7092:
7091:
7087:
7077:
7075:
7062:
7061:
7057:
7047:
7045:
7032:
7031:
7027:
7022:
7017:
6952:playing program
6935:
6904:
6883:
6876:
6869:
6862:
6855:
6848:
6841:
6834:
6827:
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6813:
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5605:
5598:
5591:
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5577:
5570:
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5549:
5542:
5533:
5526:
5519:
5512:
5505:
5498:
5491:
5484:
5477:
5470:
5463:
5456:
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5442:
5435:
5428:
5421:
5414:
5407:
5398:
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5377:
5370:
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5356:
5349:
5342:
5335:
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5300:
5293:
5286:
5279:
5272:
5263:
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5249:
5242:
5235:
5228:
5221:
5214:
5207:
5200:
5193:
5186:
5179:
5172:
5165:
5158:
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5137:
5128:
5121:
5114:
5107:
5100:
5093:
5086:
5079:
5072:
5065:
5058:
5051:
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5030:
5023:
5016:
5009:
5002:
4993:
4986:
4979:
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4958:
4951:
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4937:
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4923:
4916:
4909:
4902:
4895:
4888:
4881:
4874:
4867:
4858:
4851:
4844:
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4830:
4823:
4816:
4809:
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4774:
4767:
4760:
4753:
4746:
4739:
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4716:
4709:
4702:
4695:
4688:
4681:
4674:
4667:
4660:
4653:
4646:
4639:
4632:
4625:
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4604:
4597:
4588:
4581:
4574:
4567:
4560:
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4546:
4539:
4532:
4525:
4518:
4511:
4504:
4497:
4490:
4483:
4476:
4469:
4462:
4453:
4446:
4439:
4432:
4425:
4418:
4411:
4404:
4397:
4390:
4383:
4376:
4369:
4362:
4355:
4348:
4341:
4334:
4327:
4313:First 99 moves
4301:
4294:
4287:
4280:
4273:
4266:
4259:
4252:
4245:
4238:
4231:
4224:
4217:
4210:
4203:
4196:
4189:
4182:
4175:
4166:
4159:
4152:
4145:
4138:
4131:
4124:
4117:
4110:
4103:
4096:
4089:
4082:
4075:
4068:
4061:
4054:
4047:
4040:
4031:
4024:
4017:
4010:
4003:
3996:
3989:
3982:
3975:
3968:
3961:
3954:
3947:
3940:
3933:
3926:
3919:
3912:
3905:
3896:
3889:
3882:
3875:
3868:
3861:
3854:
3847:
3840:
3833:
3826:
3819:
3812:
3805:
3798:
3791:
3784:
3777:
3770:
3761:
3754:
3747:
3740:
3733:
3726:
3719:
3712:
3705:
3698:
3691:
3684:
3677:
3670:
3663:
3656:
3649:
3642:
3635:
3626:
3619:
3612:
3605:
3598:
3591:
3584:
3577:
3570:
3563:
3556:
3549:
3542:
3535:
3528:
3521:
3514:
3507:
3500:
3491:
3484:
3477:
3470:
3463:
3456:
3449:
3442:
3435:
3428:
3421:
3414:
3407:
3400:
3393:
3386:
3379:
3372:
3365:
3356:
3349:
3342:
3335:
3328:
3321:
3314:
3307:
3300:
3293:
3286:
3279:
3272:
3265:
3258:
3251:
3244:
3237:
3230:
3221:
3214:
3207:
3200:
3193:
3186:
3179:
3172:
3165:
3158:
3151:
3144:
3137:
3130:
3123:
3116:
3109:
3102:
3095:
3086:
3079:
3072:
3065:
3058:
3051:
3044:
3037:
3030:
3023:
3016:
3009:
3002:
2995:
2988:
2981:
2974:
2967:
2960:
2951:
2944:
2937:
2930:
2923:
2916:
2909:
2902:
2895:
2888:
2881:
2874:
2867:
2860:
2853:
2846:
2839:
2832:
2825:
2816:
2809:
2802:
2795:
2788:
2781:
2774:
2767:
2760:
2753:
2746:
2739:
2732:
2725:
2718:
2711:
2704:
2697:
2690:
2681:
2674:
2667:
2660:
2653:
2646:
2639:
2632:
2625:
2618:
2611:
2604:
2597:
2590:
2583:
2576:
2569:
2562:
2555:
2546:
2539:
2532:
2525:
2518:
2511:
2504:
2497:
2490:
2483:
2476:
2469:
2462:
2455:
2448:
2441:
2434:
2427:
2420:
2411:
2404:
2397:
2390:
2383:
2376:
2369:
2362:
2355:
2348:
2341:
2334:
2327:
2320:
2313:
2306:
2299:
2292:
2285:
2276:
2269:
2262:
2255:
2248:
2241:
2234:
2227:
2220:
2213:
2206:
2199:
2192:
2185:
2178:
2171:
2164:
2157:
2150:
2141:
2134:
2127:
2120:
2113:
2106:
2099:
2092:
2085:
2078:
2071:
2064:
2057:
2050:
2043:
2036:
2029:
2022:
2015:
2006:
1999:
1992:
1985:
1978:
1971:
1964:
1957:
1950:
1943:
1936:
1929:
1922:
1915:
1908:
1901:
1894:
1887:
1880:
1871:
1864:
1857:
1850:
1843:
1836:
1829:
1822:
1815:
1808:
1801:
1794:
1787:
1780:
1773:
1766:
1759:
1752:
1745:
1725:
1675:
1673:Similar systems
1666:
1649:
1636:
1612:Rotten Tomatoes
1608:
1603:
1572:
1531:Stephen Hawking
1507:Murray Campbell
1487:
1482:
1469:
1424:
1369:
1107:
1080:
1064:
1014:
1006:Main articles:
1004:
980:
974:
968:
882:, Li Qincheng,
860:Park Jeong-hwan
831:
825:
756:
750:
725:
719:
679:Masaki Takemiya
620:
468:that plays the
455:
426:
425:
416:
408:
407:
383:
373:
372:
344:Control problem
324:
314:
313:
225:
215:
214:
175:
167:
166:
137:Computer vision
112:
49:Google DeepMind
24:
17:
12:
11:
5:
13531:
13521:
13520:
13515:
13510:
13505:
13488:
13487:
13485:
13484:
13479:
13466:
13463:
13462:
13460:
13459:
13458:
13457:
13452:
13447:
13435:
13430:
13425:
13416:
13410:
13408:
13404:
13403:
13401:
13400:
13395:
13390:
13385:
13380:
13379:
13378:
13373:
13365:
13364:
13363:
13358:
13353:
13345:
13340:
13335:
13330:
13325:
13324:
13323:
13318:
13313:
13305:
13300:
13295:
13289:
13287:
13281:
13280:
13278:
13277:
13276:
13275:
13270:
13265:
13255:
13250:
13245:
13240:
13239:
13238:
13233:
13228:
13223:
13218:
13213:
13208:
13203:
13198:
13193:
13188:
13186:AlphaGo Master
13183:
13173:
13167:
13165:
13159:
13158:
13156:
13155:
13148:
13141:
13134:
13129:
13122:
13115:
13108:
13101:
13098:Igo Hatsuyōron
13094:
13087:
13080:
13073:
13066:
13059:
13052:
13044:
13042:
13038:
13037:
13035:
13034:
13029:
13024:
13019:
13014:
13009:
13004:
12999:
12994:
12988:
12986:
12980:
12979:
12977:
12976:
12971:
12966:
12959:
12954:
12953:
12952:
12945:
12935:
12929:
12927:
12923:
12922:
12920:
12919:
12914:
12913:
12912:
12907:
12905:Female players
12902:
12892:
12885:
12880:
12875:
12870:
12865:
12863:Four Go houses
12860:
12855:
12848:
12840:
12838:
12832:
12831:
12829:
12828:
12821:
12814:
12813:
12812:
12805:
12803:Empty triangle
12795:
12790:
12788:Opening theory
12785:
12780:
12778:Life and death
12775:
12770:
12762:
12761:
12760:
12753:
12739:
12738:
12737:
12732:
12725:
12720:
12708:
12706:Capturing race
12702:
12700:
12694:
12693:
12691:
12690:
12683:
12678:
12671:
12666:
12661:
12654:
12647:
12640:
12635:
12630:
12625:
12618:
12611:
12604:
12597:
12590:
12585:
12578:
12573:
12568:
12561:
12554:
12547:
12540:
12533:
12526:
12519:
12512:
12505:
12498:
12491:
12484:
12469:
12464:
12456:
12451:
12444:
12439:
12432:
12424:
12422:
12416:
12415:
12413:
12412:
12411:
12410:
12403:
12398:
12388:
12387:
12386:
12379:
12365:
12359:
12357:
12351:
12350:
12348:
12347:
12342:
12335:
12329:
12327:
12321:
12320:
12311:
12310:
12303:
12296:
12288:
12279:
12278:
12276:
12275:
12274:
12273:
12268:
12255:
12254:
12253:
12248:
12234:
12231:
12230:
12228:
12227:
12222:
12217:
12212:
12207:
12202:
12197:
12192:
12187:
12182:
12177:
12172:
12167:
12162:
12157:
12151:
12149:
12145:
12144:
12142:
12141:
12136:
12131:
12126:
12121:
12116:
12111:
12106:
12101:
12095:
12093:
12089:
12088:
12086:
12085:
12083:Ilya Sutskever
12080:
12075:
12070:
12065:
12060:
12055:
12050:
12048:Demis Hassabis
12045:
12040:
12038:Ian Goodfellow
12035:
12030:
12024:
12022:
12018:
12017:
12014:
12013:
12011:
12010:
12005:
12004:
12003:
11993:
11988:
11983:
11978:
11973:
11968:
11963:
11957:
11955:
11951:
11950:
11948:
11947:
11942:
11937:
11932:
11927:
11922:
11917:
11912:
11907:
11902:
11897:
11892:
11887:
11882:
11877:
11872:
11867:
11866:
11865:
11855:
11850:
11845:
11840:
11835:
11829:
11827:
11823:
11822:
11820:
11819:
11814:
11813:
11812:
11807:
11797:
11796:
11795:
11790:
11785:
11775:
11770:
11765:
11760:
11755:
11750:
11745:
11740:
11735:
11729:
11727:
11720:
11716:
11715:
11713:
11712:
11707:
11702:
11697:
11692:
11687:
11682:
11676:
11674:
11670:
11669:
11667:
11666:
11661:
11656:
11651:
11646:
11640:
11638:
11634:
11633:
11631:
11630:
11629:
11628:
11621:Language model
11618:
11613:
11608:
11607:
11606:
11596:
11595:
11594:
11583:
11581:
11577:
11576:
11574:
11573:
11571:Autoregression
11568:
11563:
11562:
11561:
11551:
11549:Regularization
11546:
11545:
11544:
11539:
11534:
11524:
11519:
11514:
11512:Loss functions
11509:
11504:
11499:
11494:
11489:
11488:
11487:
11477:
11472:
11471:
11470:
11459:
11457:
11453:
11452:
11450:
11449:
11447:Inductive bias
11444:
11439:
11434:
11429:
11424:
11419:
11414:
11409:
11401:
11399:
11393:
11392:
11387:
11386:
11379:
11372:
11364:
11355:
11354:
11352:
11351:
11341:
11330:
11327:
11326:
11324:
11323:
11318:
11313:
11308:
11303:
11298:
11290:
11288:
11284:
11283:
11280:
11279:
11277:
11276:
11269:
11267:
11263:
11262:
11260:
11259:
11253:
11247:
11241:
11235:
11229:
11222:
11220:
11216:
11215:
11213:
11212:
11206:
11200:
11193:
11191:
11184:
11180:
11179:
11176:
11175:
11173:
11172:
11167:
11162:
11156:
11154:
11150:
11149:
11147:
11146:
11140:
11134:
11127:
11125:
11118:
11114:
11113:
11110:
11109:
11107:
11106:
11100:
11094:
11088:
11081:
11079:
11075:
11074:
11071:
11070:
11068:
11067:
11059:
11050:
11048:
11044:
11043:
11041:
11040:
11034:
11028:
11021:
11019:
11015:
11014:
11012:
11011:
11005:
10999:
10993:
10987:
10980:
10978:
10971:
10964:
10960:
10959:
10957:
10956:
10951:
10946:
10940:
10937:
10936:
10929:
10928:
10921:
10914:
10906:
10900:
10899:
10888:
10883:
10877:
10867:
10858:
10846:
10832:
10831:External links
10829:
10826:
10825:
10794:
10779:. Go Ratings.
10768:
10719:
10642:
10612:
10582:
10552:
10522:
10469:
10448:
10417:
10386:
10352:
10321:
10290:
10257:
10226:
10196:
10165:
10139:
10113:
10077:
10044:
10011:
9961:
9928:
9892:
9860:
9829:
9796:
9758:
9722:
9690:
9657:
9618:
9585:
9553:
9518:
9470:
9451:(in Chinese).
9440:
9391:
9339:
9306:
9201:
9143:
9124:(in Chinese).
9113:
9083:
9064:(in Chinese).
9053:
9034:(in Chinese).
9023:
8989:
8956:
8937:(in Chinese).
8923:
8893:
8865:
8826:
8708:
8682:
8663:
8644:
8618:
8592:
8566:
8540:
8508:
8473:
8417:
8392:Demis Hassabis
8380:
8354:
8329:
8303:
8273:
8241:
8209:
8173:
8140:
8109:
8079:
8045:
8012:
7980:
7942:
7909:
7877:
7845:
7813:
7781:
7752:
7720:
7682:
7656:
7626:
7597:
7563:
7505:
7479:
7457:
7438:(in Chinese).
7424:
7392:
7359:
7325:
7293:
7254:
7215:
7115:
7085:
7055:
7024:
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7018:
7016:
7015:
7010:
7005:
7000:
6999:neural network
6990:
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2103:
2096:
2089:
2082:
2075:
2068:
2061:
2054:
2047:
2040:
2033:
2026:
2019:
2011:
2010:
2003:
1996:
1989:
1982:
1975:
1968:
1961:
1954:
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1933:
1926:
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1749:
1728:AlphaGo Master
1724:
1721:
1674:
1671:
1665:
1662:
1648:
1645:
1635:
1632:
1607:
1604:
1602:
1599:
1571:
1568:
1550:Sputnik moment
1543:Richard Sutton
1526:Stuart Russell
1486:
1483:
1481:
1478:
1468:
1465:
1423:
1420:
1417:
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1413:
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1397:
1396:
1390:
1387:
1384:
1381:
1374:
1373:
1366:
1363:
1360:
1357:
1355:AlphaGo Master
1351:
1350:
1344:
1341:
1338:
1331:
1325:
1324:
1318:
1315:
1312:
1305:
1299:
1298:
1295:
1292:
1289:
1286:
1274:AlphaGo Master
1257:
1256:
1253:
1250:
1247:
1244:
1240:
1239:
1236:
1233:
1230:
1227:
1223:
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1219:
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1202:
1199:
1196:
1193:
1187:
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1174:
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1157:
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1132:
1129:
1126:
1123:
1119:
1118:
1115:
1112:
1109:
1104:
1079:
1076:
1072:AlphaGo Master
1063:
1060:
1029:AlphaGo Master
1003:
1000:
988:AlphaGo Master
970:Main article:
967:
964:
956:Demis Hassabis
916:Park Yeong-hun
904:Chou Chun-hsun
840:AlphaGo Master
827:Main article:
824:
821:
752:Main article:
749:
746:
721:Main article:
718:
715:
655:Garry Kasparov
636:tree traversal
619:
616:
579:as one of the
457:
456:
454:
453:
446:
439:
431:
428:
427:
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423:
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240:Earth sciences
237:
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230:Bioinformatics
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93:
92:
86:
85:
77:
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71:
67:
66:
58:
52:
51:
46:
40:
39:
21:AlphaGo (film)
15:
9:
6:
4:
3:
2:
13530:
13519:
13516:
13514:
13511:
13509:
13508:2015 software
13506:
13504:
13501:
13500:
13498:
13483:
13480:
13478:
13477:Go portal
13473:
13468:
13467:
13464:
13456:
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13446:
13445:
13441:
13440:
13439:
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13426:
13424:
13422:
13419:Game record (
13417:
13415:
13412:
13411:
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13405:
13399:
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13301:
13299:
13296:
13294:
13291:
13290:
13288:
13286:
13285:Organizations
13282:
13274:
13271:
13269:
13266:
13264:
13263:KGS Go Server
13261:
13260:
13259:
13256:
13254:
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13234:
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13100:
13099:
13095:
13093:
13092:
13088:
13086:
13085:
13081:
13079:
13078:
13077:The Go Player
13074:
13072:
13071:
13070:The Go Master
13067:
13065:
13064:
13060:
13058:
13057:
13053:
13051:
13050:
13046:
13045:
13043:
13041:Art and media
13039:
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12969:Title holders
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12148:Architectures
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12092:Organizations
12090:
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12034:
12031:
12029:
12028:Yoshua Bengio
12026:
12025:
12023:
12019:
12009:
12008:Robot control
12006:
12002:
11999:
11998:
11997:
11994:
11992:
11989:
11987:
11984:
11982:
11979:
11977:
11974:
11972:
11969:
11967:
11964:
11962:
11959:
11958:
11956:
11952:
11946:
11943:
11941:
11938:
11936:
11933:
11931:
11928:
11926:
11925:Chinchilla AI
11923:
11921:
11918:
11916:
11913:
11911:
11908:
11906:
11903:
11901:
11898:
11896:
11893:
11891:
11888:
11886:
11883:
11881:
11878:
11876:
11873:
11871:
11868:
11864:
11861:
11860:
11859:
11856:
11854:
11851:
11849:
11846:
11844:
11841:
11839:
11836:
11834:
11831:
11830:
11828:
11824:
11818:
11815:
11811:
11808:
11806:
11803:
11802:
11801:
11798:
11794:
11791:
11789:
11786:
11784:
11781:
11780:
11779:
11776:
11774:
11771:
11769:
11766:
11764:
11761:
11759:
11756:
11754:
11751:
11749:
11746:
11744:
11741:
11739:
11736:
11734:
11731:
11730:
11728:
11724:
11721:
11717:
11711:
11708:
11706:
11703:
11701:
11698:
11696:
11693:
11691:
11688:
11686:
11683:
11681:
11678:
11677:
11675:
11671:
11665:
11662:
11660:
11657:
11655:
11652:
11650:
11647:
11645:
11642:
11641:
11639:
11635:
11627:
11624:
11623:
11622:
11619:
11617:
11614:
11612:
11609:
11605:
11604:Deep learning
11602:
11601:
11600:
11597:
11593:
11590:
11589:
11588:
11585:
11584:
11582:
11578:
11572:
11569:
11567:
11564:
11560:
11557:
11556:
11555:
11552:
11550:
11547:
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11540:
11538:
11535:
11533:
11530:
11529:
11528:
11525:
11523:
11520:
11518:
11515:
11513:
11510:
11508:
11505:
11503:
11500:
11498:
11495:
11493:
11492:Hallucination
11490:
11486:
11483:
11482:
11481:
11478:
11476:
11473:
11469:
11466:
11465:
11464:
11461:
11460:
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11443:
11440:
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11413:
11410:
11408:
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11380:
11378:
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11366:
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11350:
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11314:
11312:
11309:
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11302:
11299:
11296:
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11236:
11233:
11230:
11227:
11224:
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11221:
11217:
11210:
11207:
11204:
11201:
11198:
11195:
11194:
11192:
11188:
11185:
11183:Generative AI
11181:
11171:
11168:
11166:
11163:
11161:
11158:
11157:
11155:
11151:
11144:
11141:
11138:
11135:
11132:
11129:
11128:
11126:
11122:
11119:
11115:
11104:
11103:AlphaGeometry
11101:
11098:
11095:
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11089:
11086:
11083:
11082:
11080:
11076:
11065:
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11060:
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11020:
11016:
11009:
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10988:
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10968:
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10961:
10955:
10952:
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10938:
10934:
10927:
10922:
10920:
10915:
10913:
10908:
10907:
10904:
10898:
10894:
10889:
10887:
10884:
10881:
10878:
10875:
10871:
10868:
10864:
10859:
10856:
10851:
10847:
10844:
10839:
10835:
10834:
10813:
10809:
10805:
10798:
10782:
10778:
10772:
10764:
10760:
10756:
10752:
10747:
10742:
10738:
10734:
10730:
10723:
10707:
10703:
10699:
10695:
10691:
10687:
10683:
10679:
10675:
10670:
10665:
10661:
10657:
10653:
10646:
10630:
10626:
10622:
10616:
10600:
10596:
10592:
10586:
10570:
10566:
10562:
10556:
10540:
10536:
10532:
10526:
10518:
10512:
10496:
10492:
10488:
10484:
10480:
10473:
10464:
10459:
10452:
10436:
10432:
10428:
10421:
10405:
10401:
10397:
10390:
10374:
10370:
10366:
10359:
10357:
10340:
10336:
10332:
10325:
10309:
10305:
10301:
10294:
10278:
10274:
10273:
10268:
10261:
10245:
10241:
10237:
10230:
10215:
10214:
10209:
10203:
10201:
10184:
10180:
10176:
10169:
10153:
10149:
10143:
10127:
10123:
10117:
10101:
10097:
10096:
10091:
10084:
10082:
10065:
10061:
10060:
10055:
10048:
10032:
10028:
10027:
10026:New Scientist
10022:
10015:
9999:
9995:
9991:
9987:
9983:
9979:
9975:
9968:
9966:
9949:
9945:
9944:
9939:
9932:
9925:
9913:
9909:
9908:
9903:
9896:
9880:
9876:
9875:
9870:
9864:
9848:
9844:
9840:
9833:
9817:
9813:
9812:
9807:
9800:
9784:
9780:
9779:
9774:
9767:
9765:
9763:
9746:
9742:
9741:
9736:
9729:
9727:
9710:
9706:
9705:
9700:
9694:
9678:
9674:
9673:
9668:
9661:
9645:
9641:
9637:
9636:
9631:
9625:
9623:
9606:
9602:
9601:
9596:
9589:
9573:
9569:
9568:
9563:
9557:
9541:
9537:
9536:
9530:
9522:
9506:
9502:
9501:
9496:
9489:
9487:
9485:
9483:
9481:
9479:
9477:
9475:
9458:
9454:
9450:
9444:
9428:
9424:
9420:
9416:
9412:
9408:
9407:
9402:
9395:
9387:
9383:
9378:
9373:
9369:
9365:
9361:
9357:
9353:
9346:
9344:
9327:
9323:
9322:
9317:
9310:
9303:
9288:
9284:
9280:
9276:
9272:
9268:
9264:
9260:
9256:
9252:
9248:
9244:
9240:
9239:
9231:
9227:
9223:
9219:
9215:
9211:
9210:Silver, David
9205:
9197:
9193:
9189:
9185:
9180:
9175:
9171:
9167:
9163:
9159:
9155:
9147:
9131:
9127:
9123:
9117:
9101:
9097:
9093:
9087:
9071:
9067:
9063:
9057:
9041:
9037:
9033:
9027:
9011:
9007:
9003:
8999:
8993:
8977:
8973:
8972:
8967:
8960:
8944:
8940:
8936:
8930:
8928:
8911:
8907:
8903:
8897:
8888:
8883:
8879:
8875:
8874:Silver, David
8869:
8853:
8849:
8845:
8839:
8837:
8835:
8833:
8831:
8822:
8804:
8800:
8796:
8792:
8788:
8784:
8780:
8776:
8772:
8768:
8764:
8760:
8756:
8755:
8747:
8743:
8739:
8735:
8731:
8727:
8726:Silver, David
8721:
8719:
8717:
8715:
8713:
8696:
8692:
8686:
8678:
8674:
8667:
8659:
8655:
8648:
8632:
8628:
8622:
8606:
8602:
8596:
8580:
8576:
8570:
8554:
8550:
8544:
8528:
8524:
8523:
8518:
8512:
8496:
8492:
8488:
8482:
8480:
8478:
8469:
8465:
8460:
8455:
8451:
8447:
8444:(7636): 142.
8443:
8439:
8438:
8433:
8426:
8424:
8422:
8405:
8401:
8397:
8393:
8387:
8385:
8368:
8364:
8358:
8343:
8339:
8333:
8317:
8313:
8307:
8291:
8287:
8283:
8277:
8261:
8257:
8256:
8251:
8245:
8229:
8225:
8224:
8219:
8213:
8197:
8193:
8192:
8188:
8183:
8177:
8161:
8157:
8153:
8147:
8145:
8128:
8124:
8120:
8113:
8097:
8093:
8092:The Economist
8089:
8083:
8075:
8063:
8059:
8055:
8049:
8033:
8029:
8026:(in Korean).
8025:
8019:
8017:
8000:
7996:
7995:
7994:JoongAng Ilbo
7990:
7984:
7968:
7964:
7963:
7958:
7951:
7949:
7947:
7930:
7926:
7925:
7920:
7913:
7897:
7893:
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7887:
7881:
7865:
7861:
7860:
7855:
7849:
7833:
7829:
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7817:
7798:
7791:
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7766:
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7757:
7740:
7736:
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7730:
7724:
7708:
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7703:
7698:
7691:
7689:
7687:
7670:
7666:
7660:
7644:
7640:
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7614:
7610:
7604:
7602:
7583:
7576:
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7567:
7559:
7555:
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7537:
7533:
7529:
7525:
7521:
7517:
7509:
7493:
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7475:
7471:
7464:
7462:
7445:
7441:
7437:
7431:
7429:
7412:
7408:
7407:
7402:
7396:
7380:
7376:
7375:AlphaGo Movie
7372:
7366:
7364:
7347:
7343:
7339:
7335:
7329:
7313:
7309:
7308:
7303:
7297:
7281:
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7271:
7265:
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7242:
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7234:
7228:
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7224:
7222:
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7211:
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7184:
7180:
7176:
7172:
7168:
7164:
7160:
7159:
7154:
7150:
7146:
7142:
7141:Silver, David
7136:
7134:
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7130:
7128:
7126:
7124:
7122:
7120:
7103:
7099:
7095:
7089:
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7059:
7043:
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7025:
7014:
7011:
7009:
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6947:
6944:
6941:
6938:
6937:
6930:
6927:
6923:
6919:
6915:
6911:
6910:
6902:Impacts on Go
6894:
6893:
6881:
6874:
6867:
6860:
6853:
6846:
6839:
6832:
6825:
6818:
6811:
6804:
6797:
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6783:
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6762:
6755:
6754:
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6604:
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6506:
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5994:
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3405:
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3333:
3326:
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3305:
3298:
3291:
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3270:
3263:
3256:
3249:
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1474:shoulder hits
1467:Style of play
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1103:Configuration
1102:
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1062:Teaching tool
1059:
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1026:
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1020:
1013:
1009:
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989:
986:in May 2017,
985:
979:
973:
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957:
953:
949:
945:
941:
937:
936:Cho Han-seung
933:
929:
925:
921:
920:Won Seong-jin
917:
913:
912:Kang Dong-yun
909:
905:
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787:
786:
785:The Economist
781:
777:
773:
772:Chinese rules
769:
765:
761:
755:
745:
743:
742:
737:
733:
730:
724:
714:
712:
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702:deep learning
699:
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691:Yoshio Ishida
688:
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680:
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668:
667:amateur 5-dan
664:
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528:deep learning
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287:Mental health
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250:Generative AI
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41:
37:
32:
26:
22:
13443:
13420:
13356:Kansai Ki-in
13311:China Qiyuan
13191:AlphaGo Zero
13180:
13151:
13144:
13137:
13125:
13118:
13110:
13104:
13097:
13091:Hikaru no Go
13090:
13083:
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13069:
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13055:
13048:
12962:
12948:
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12674:
12669:Shoulder hit
12657:
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12406:
12382:
12375:
12368:
12338:
12314:
12114:Hugging Face
12078:David Silver
11960:
11726:Audio–visual
11580:Applications
11559:Augmentation
11404:
11316:Google Pixel
11061:
11053:
11018:Competitions
10996:AlphaGo Zero
10983:
10949:Google Brain
10880:AlphaGo page
10870:AlphaGo wiki
10857:at Wikiquote
10816:. Retrieved
10807:
10797:
10785:. Retrieved
10777:"Go Ratings"
10771:
10736:
10732:
10722:
10710:. Retrieved
10659:
10655:
10645:
10633:. Retrieved
10624:
10615:
10603:. Retrieved
10594:
10585:
10573:. Retrieved
10564:
10555:
10543:. Retrieved
10534:
10525:
10499:. Retrieved
10483:The Guardian
10482:
10472:
10463:1511.06410v1
10451:
10439:. Retrieved
10430:
10420:
10408:. Retrieved
10399:
10389:
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10368:
10343:. Retrieved
10334:
10324:
10312:. Retrieved
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10293:
10281:. Retrieved
10270:
10260:
10248:. Retrieved
10239:
10229:
10217:. Retrieved
10211:
10187:. Retrieved
10178:
10168:
10156:. Retrieved
10152:the original
10142:
10130:. Retrieved
10116:
10104:. Retrieved
10093:
10068:. Retrieved
10057:
10047:
10035:. Retrieved
10024:
10014:
10002:. Retrieved
9977:
9952:. Retrieved
9941:
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9916:. Retrieved
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9863:
9851:. Retrieved
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9832:
9820:. Retrieved
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9787:. Retrieved
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9749:. Retrieved
9738:
9713:. Retrieved
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9681:. Retrieved
9670:
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9609:. Retrieved
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9576:. Retrieved
9565:
9556:
9544:. Retrieved
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9431:. Retrieved
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9330:. Retrieved
9319:
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9294:. Retrieved
9242:
9236:
9218:Chen, Yutian
9204:
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9134:. Retrieved
9116:
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9086:
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9056:
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9026:
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9005:
8998:Jouppi, Norm
8992:
8980:. Retrieved
8969:
8959:
8947:. Retrieved
8914:. Retrieved
8896:
8868:
8856:. Retrieved
8852:the original
8810:. Retrieved
8758:
8752:
8734:Chen, Yutian
8699:. Retrieved
8695:the original
8685:
8676:
8666:
8657:
8647:
8635:. Retrieved
8621:
8609:. Retrieved
8595:
8583:. Retrieved
8579:the original
8569:
8557:. Retrieved
8543:
8531:. Retrieved
8520:
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8499:. Retrieved
8490:
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8435:
8408:. Retrieved
8371:. Retrieved
8367:the original
8357:
8345:. Retrieved
8341:
8332:
8320:. Retrieved
8316:the original
8306:
8294:. Retrieved
8285:
8276:
8264:. Retrieved
8253:
8244:
8232:. Retrieved
8221:
8212:
8200:. Retrieved
8196:the original
8185:
8176:
8164:. Retrieved
8160:the original
8131:. Retrieved
8123:The Guardian
8122:
8112:
8100:. Retrieved
8091:
8082:
8072:– via
8066:. Retrieved
8048:
8036:. Retrieved
8032:the original
8003:. Retrieved
7992:
7983:
7971:. Retrieved
7960:
7933:. Retrieved
7922:
7912:
7900:. Retrieved
7889:
7880:
7868:. Retrieved
7859:The Guardian
7857:
7848:
7836:. Retrieved
7825:
7816:
7804:. Retrieved
7784:
7772:. Retrieved
7768:
7743:. Retrieved
7732:
7723:
7711:. Retrieved
7700:
7673:. Retrieved
7669:the original
7659:
7647:. Retrieved
7643:the original
7639:Go Game Guru
7638:
7629:
7617:. Retrieved
7589:, retrieved
7573:
7566:
7523:
7519:
7508:
7496:. Retrieved
7482:
7473:
7448:. Retrieved
7415:. Retrieved
7404:
7395:
7383:. Retrieved
7374:
7350:. Retrieved
7337:
7328:
7316:. Retrieved
7305:
7296:
7284:. Retrieved
7273:
7245:. Retrieved
7236:
7162:
7156:
7106:. Retrieved
7097:
7088:
7078:16 September
7076:. Retrieved
7067:
7058:
7046:. Retrieved
7037:
7028:
6907:
6905:
1732:Tang Weixing
1726:
1723:Example game
1710:
1692:
1681:
1676:
1667:
1658:
1654:
1650:
1641:
1637:
1628:
1616:
1609:
1595:
1588:
1581:
1573:
1570:Go community
1554:
1547:
1539:common sense
1511:
1492:
1488:
1485:AI community
1470:
1456:
1425:
1393:
1378:AlphaGo Zero
1346:4:1 against
1320:5:0 against
1267:
1260:
1081:
1065:
1033:
1017:
1015:
1008:AlphaGo Zero
996:
992:
981:
960:
932:Yang Dingxin
900:Jiang Weijie
896:Zhou Ruiyang
832:
813:
806:
802:
783:
757:
739:
726:
698:David Silver
695:
649:'s computer
644:
621:
608:
586:At the 2017
585:
574:
568:
533:
513:
489:AlphaGo Zero
461:
460:
334:Chinese room
223:Applications
44:Developer(s)
25:
13361:Nihon Ki-in
13201:Crazy Stone
12974:Tournaments
12926:Competition
12858:Emperor Yao
12588:Monkey jump
12558:Korigatachi
12454:Divine move
12262:Categories
12210:Autoencoder
12165:Transformer
12033:Alex Graves
11981:OpenAI Five
11885:IBM Watsonx
11507:Convolution
11485:Overfitting
11311:Google Labs
11137:Transformer
10818:28 November
10712:12 December
10605:27 November
10575:27 November
10369:inews.co.uk
9433:18 December
8949:11 December
8939:Sina.com.cn
8916:11 December
8559:11 December
8347:12 November
8322:22 February
8152:Rémi Coulom
8102:19 November
8038:22 February
8005:24 February
7924:VentureBeat
7902:23 February
7870:15 February
7417:29 December
1730:(white) v.
1535:AI takeover
1432:tree search
1329:AlphaGo Lee
1303:AlphaGo Fan
1243:Distributed
1226:Distributed
1209:Distributed
1191:Distributed
1117:Elo rating
1092:Elo ratings
1068:Go openings
1025:AlphaGo Lee
998:community.
944:Nie Weiping
908:Kim Ji-seok
817:divine move
687:Crazy Stone
540:computer Go
493:self-taught
363:Turing test
339:Friendly AI
110:Major goals
61:Computer Go
13513:Go engines
13497:Categories
13450:Capture go
13231:Leela Zero
13206:Darkforest
13112:The MANIAC
12729:Shinfuseki
12251:Technology
12104:EleutherAI
12063:Fei-Fei Li
12058:Yann LeCun
11971:Q-learning
11954:Decisional
11880:IBM Watson
11788:Midjourney
11680:TensorFlow
11527:Activation
11480:Regression
11475:Clustering
11238:Chinchilla
11165:TensorFlow
11063:The MANIAC
10746:2303.08060
10669:1708.04202
10501:1 February
10004:29 January
9885:21 October
9578:28 January
9546:28 January
9321:Wired News
9296:13 October
9214:Huang, Aja
8887:1712.01815
8858:19 October
8730:Huang, Aja
8255:BBC Online
8223:BBC Online
8088:"Showdown"
7935:7 February
7838:28 January
7806:1 February
7774:1 February
7745:28 January
7649:28 January
7619:28 January
7591:31 January
7450:9 December
7385:14 October
7352:9 December
7247:28 January
7145:Huang, Aja
7020:References
6997:backgammon
6977:Leela Zero
1706:Cho Chikun
1683:darkforest
1403:(20 block)
1380:(40 block)
1291:Elo rating
1114:No. of GPU
1111:No. of CPU
892:Fan Tingyu
880:Chen Yaoye
864:Yuta Iyama
659:1997 match
470:board game
368:Regulation
322:Philosophy
277:Healthcare
272:Government
174:Approaches
13196:AlphaZero
13163:Computers
12878:9 Pin Zhi
12868:Four arts
12783:Mirror Go
12723:Kobayashi
12681:Thickness
12396:Clamshell
12355:Equipment
12333:Handicaps
12134:MIT CSAIL
12099:Anthropic
12068:Andrew Ng
11966:AlphaZero
11810:VideoPoet
11773:AlphaFold
11710:MindSpore
11664:SpiNNaker
11659:Memristor
11566:Diffusion
11542:Rectifier
11522:Batchnorm
11502:Attention
11497:Adversary
11256:VideoPoet
11197:Assistant
11091:AlphaStar
11085:AlphaFold
11031:Lee Sedol
11002:AlphaZero
10933:Google AI
10808:The Verge
10763:1573-7497
10702:205264340
10511:cite news
10491:0261-3077
10208:"ALPHAGO"
9994:146868978
9672:CNN Money
9423:155164502
9283:205261034
9267:0028-0836
8812:29 August
8799:205261034
8783:0028-0836
8533:6 January
8501:6 January
8410:4 January
8402:account.
7187:0028-0836
7013:AlphaFold
7008:AlphaZero
6993:TD-Gammon
6922:Lee Sedol
6914:Lee Sedol
1694:DeepZenGo
1606:Reception
1503:Deep Blue
1497:" by the
1422:Algorithm
1401:AlphaZero
1348:Lee Sedol
1052:Stockfish
1012:AlphaZero
948:Aja Huang
924:Lian Xiao
888:Chang Hao
872:Mi Yuting
868:Tuo Jiaxi
760:Lee Sedol
651:Deep Blue
640:heuristic
612:AlphaZero
552:Lee Sedol
497:AlphaZero
398:AI winter
299:Military
162:AI safety
13482:Category
13438:Variants
13268:Pandanet
13211:Fine Art
13084:Go World
12889:Oshirogo
12793:Proverbs
12325:Overview
12242:Portals
12001:Auto-GPT
11833:Word2vec
11637:Hardware
11554:Datasets
11456:Concepts
11339:Category
11287:See also
11190:Chatbots
11097:AlphaDev
10977:Versions
10812:Archived
10781:Archived
10706:Archived
10694:29595767
10629:Archived
10599:Archived
10569:Archived
10545:17 March
10539:Archived
10495:Archived
10441:15 April
10435:Archived
10410:15 April
10404:Archived
10379:15 April
10373:Archived
10345:15 April
10339:Archived
10314:15 April
10308:Archived
10283:15 April
10277:Archived
10250:15 April
10244:Archived
10219:15 April
10189:18 March
10183:Archived
10132:17 March
10126:Archived
10106:13 March
10100:Archived
10070:18 March
10064:Archived
10037:18 March
10031:Archived
9998:Archived
9954:13 March
9948:Archived
9943:PC World
9918:18 March
9912:Archived
9879:Archived
9853:11 April
9847:Archived
9822:13 March
9816:Archived
9789:13 March
9783:Archived
9778:phys.org
9751:13 March
9745:Archived
9740:Phys.org
9715:13 March
9709:Archived
9683:13 March
9677:Archived
9650:13 March
9644:Archived
9635:CBC News
9611:13 March
9605:Archived
9572:Archived
9567:CBC News
9540:Archived
9511:13 March
9505:Archived
9463:24 April
9457:Archived
9453:Sina.com
9427:Archived
9386:26819021
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