AlphaGo

In October 2015, the distributed version of AlphaGo defeated the European Go champion Fan Hui,[21] a 2-dan (out of 9 dan possible) professional, five to zero.[6][22] This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap.[23] The announcement of the news was delayed until 27 January 2016 to coincide with the publication of a paper in the journal Nature[4] describing the algorithms used.[6]

AlphaGo played South Korean professional Go player Lee Sedol, ranked 9-dan, one of the best players at Go,[16] with five games taking place at the Four Seasons Hotel in Seoul, South Korea on 9, 10, 12, 13, and 15 March 2016,[24][25] which were video-streamed live.[26] Out of five games, AlphaGo won four games and Lee won the fourth game which made him recorded as the only human player who beat AlphaGo in all of its 74 official games.[27] AlphaGo ran on Google's cloud computing with its servers located in the United States.[28] The match used Chinese rules with a 7.5-point komi, and each side had two hours of thinking time plus three 60-second byoyomi periods.[29] The version of AlphaGo playing against Lee used a similar amount of computing power as was used in the Fan Hui match.[30] The Economist reported that it used 1,920 CPUs and 280 GPUs.[31] 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.[32] Since there is no single official method of ranking in international Go, the rankings may vary among the sources. While he was ranked top sometimes, some sources ranked Lee Sedol as the fourth-best player in the world at the time.[33][34] AlphaGo was not specifically trained to face Lee nor was designed to compete with any specific human players.

The first three games were won by AlphaGo following resignations by Lee.[35][36] However, Lee beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then continued to achieve a fourth win, winning the fifth game by resignation.[37]

The prize was US$1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, including UNICEF.[38] Lee Sedol received $150,000 for participating in all five games and an additional $20,000 for his win in Game 4.[29]

In June 2016, at a presentation held at a university in the Netherlands, Aja Huang, one of the Deep Mind team, revealed that they had patched the logical weakness that occurred during the 4th game of the match between AlphaGo and Lee, and that after move 78 (which was dubbed the "divine move" by many professionals), it would play as intended and maintain Black's advantage. Before move 78, AlphaGo was leading throughout the game, but Lee's move caused the program's computing powers to be diverted and confused.[39] Huang explained that AlphaGo's policy network of finding the most accurate move order and continuation did not precisely guide AlphaGo to make the correct continuation after move 78, since its value network did not determine Lee's 78th move as being the most likely, and therefore when the move was made AlphaGo could not make the right adjustment to the logical continuation.[40]

On 29 December 2016, a new account on the Tygem server named "Magister" (shown as 'Magist' at the server's Chinese version) from South Korea began to play games with professional players. It changed its account name to "Master" on 30 December, then moved to the FoxGo server on 1 January 2017. On 4 January, DeepMind confirmed that the "Magister" and the "Master" were both played by an updated version of AlphaGo, called AlphaGo Master.[41][42] As of 5 January 2017, AlphaGo Master's online record was 60 wins and 0 losses,[43] including three victories over Go's top-ranked player, Ke Jie,[44] who had been quietly briefed in advance that Master was a version of AlphaGo.[43] After losing to Master, Gu Li offered a bounty of 100,000 yuan (US$14,400) to the first human player who could defeat Master.[42] Master played at the pace of 10 games per day. Many quickly suspected it to be an AI player due to little or no resting between games. Its adversaries included many world champions such as Ke Jie, Park Jeong-hwan, Yuta Iyama, Tuo Jiaxi, Mi Yuting, Shi Yue, Chen Yaoye, Li Qincheng, Gu Li, Chang Hao, Tang Weixing, Fan Tingyu, Zhou Ruiyang, Jiang Weijie, Chou Chun-hsun, Kim Ji-seok, Kang Dong-yun, Park Yeong-hun, and Won Seong-jin; national champions or world championship runners-up such as Lian Xiao, Tan Xiao, Meng Tailing, Dang Yifei, Huang Yunsong, Yang Dingxin, Gu Zihao, Shin Jinseo, Cho Han-seung, and An Sungjoon. All 60 games except one were fast-paced games with three 20 or 30 seconds byo-yomi. Master offered to extend the byo-yomi to one minute when playing with Nie Weiping in consideration of his age. After winning its 59th game Master revealed itself in the chatroom to be controlled by Dr. Aja Huang of the DeepMind team,[45] then changed its nationality to the United Kingdom. After these games were completed, the co-founder of DeepMind, Demis Hassabis, said in a tweet, "we're looking forward to playing some official, full-length games later [2017] in collaboration with Go organizations and experts".[41][42]

Go experts were impressed by the program's performance and its nonhuman play style; Ke Jie stated that "After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong... I would go as far as to say not a single human has touched the edge of the truth of Go."[43]

In the Future of Go Summit held in Wuzhen in May 2017, AlphaGo Master played three games with Ke Jie, the world No.1 ranked player, as well as two games with several top Chinese professionals, one pair Go game and one against a collaborating team of five human players.[46]

Google DeepMind offered 1.5 million dollar winner prizes for the three-game match between Ke Jie and Master while the losing side took 300,000 dollars.[47][48] Master won all three games against Ke Jie,[49][50] after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[11]

After winning its three-game match against Ke Jie, the top-rated world Go player, AlphaGo retired. DeepMind also disbanded the team that worked on the game to focus on AI research in other areas.[12] After the Summit, Deepmind published 50 full length AlphaGo vs AlphaGo matches, as a gift to the Go community.[51]

AlphaGo's team published an article in the journal Nature on 19 October 2017, introducing AlphaGo Zero, a version without human data and stronger than any previous human-champion-defeating version.[52] By playing games against itself, AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.[53]

In a paper released on arXiv 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 chess, shogi, and Go by defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case.[54]

On 11 December 2017, DeepMind released AlphaGo teaching tool on its website[55] to analyze winning rates of different Go openings as calculated by AlphaGo Master.[56] The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.[56]

AlphaGo's March 2016 victory was a major milestone in artificial intelligence research.[69] 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.[69][70][71] Most experts thought a Go program as powerful as AlphaGo was at least five years away;[72] some experts thought that it would take at least another decade before computers would beat Go champions.[4][73][74] Most observers at the beginning of the 2016 matches expected Lee to beat AlphaGo.[69]

With games such as checkers (that has been "solved" by the Chinook draughts player 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. Deep Blue's Murray Campbell called AlphaGo's victory "the end of an era... board games are more or less done and it's time to move on."[69]

When compared with Deep Blue or Watson, AlphaGo's underlying algorithms are potentially more general-purpose and may be evidence that the scientific community is making progress towards artificial general intelligence.[19][75] Some commentators believe AlphaGo's victory makes for a good opportunity for society to start preparing for the possible future impact of machines with general purpose intelligence. As noted by entrepreneur Guy Suter, AlphaGo only knows how to play Go and doesn't possess general-purpose intelligence; "[It] couldn't just wake up one morning and decide it wants to learn how to use firearms."[69] AI researcher Stuart Russell said that AI systems such as AlphaGo have progressed quicker and become more powerful than expected, and we must therefore develop methods to ensure they "remain under human control".[76] Some scholars, such as Stephen Hawking, warned (in May 2015 before the matches) that some future self-improving AI could gain actual general intelligence, leading to an unexpected AI takeover; other scholars disagree: AI expert Jean-Gabriel Ganascia believes that "Things like 'common sense'... may never be reproducible",[77] 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."[76] Computer scientist Richard Sutton said "I don't think people should be scared... but I do think people should be paying attention."[78]

In China, AlphaGo was a "Sputnik moment" which helped convince the Chinese government to prioritize and dramatically increase funding for artificial intelligence.[79]

In 2017, the DeepMind AlphaGo team received the inaugural IJCAI Marvin Minsky medal for Outstanding Achievements in AI. "AlphaGo is a wonderful achievement, and a perfect example of what the Minsky Medal was initiated to recognise", said Professor Michael Wooldridge, Chair of the IJCAI Awards Committee. "What particularly impressed IJCAI was that AlphaGo achieves what it does through a brilliant combination of classic AI techniques as well as the state-of-the-art machine learning techniques that DeepMind is so closely associated with. It's a breathtaking demonstration of contemporary AI, and we are delighted to be able to recognise it with this award."[80]

Go is a popular game in China, Japan and Korea, and the 2016 matches were watched by perhaps a hundred million people worldwide.[69][81] Many top Go players characterized AlphaGo's unorthodox plays as seemingly-questionable moves that initially befuddled onlookers, but made sense in hindsight:[73] "All but the very best Go players craft their style by imitating top players. AlphaGo seems to have totally original moves it creates itself."[69] AlphaGo appeared to have unexpectedly become much stronger, even when compared with its October 2015 match[82] where a computer had beaten a Go professional for the first time ever without the advantage of a handicap.[83] 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."[84] The Korea Baduk Association, the organization that oversees Go professionals in South Korea, awarded AlphaGo an honorary 9-dan title for exhibiting creative skills and pushing forward the game's progress.[85]

China's Ke Jie, an 18-year-old generally recognized as the world's best Go player at the time,[33][86] initially claimed that he would be able to beat AlphaGo, but declined to play against it for fear that it would "copy my style".[86] 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,[87] but regaining confidence after AlphaGo displayed flaws in the fourth match.[88]

Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary general of the International Go Federation, both reason that in the future, Go players will get help from computers to learn what they have done wrong in games and improve their skills.[83]

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."[89] Lee apologized for his losses, stating after game three that "I misjudged the capabilities of AlphaGo and felt powerless."[69] He emphasized that the defeat was "Lee Se-dol's defeat" and "not a defeat of mankind".[27][77] 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."[77] Lee called his game four victory a "priceless win that I (would) not exchange for anything."[27]

On Rotten Tomatoes the documentary has an average rating of 100% from 10 reviews.[90]

Michael Rechtshaffen of the Los Angeles Times gave the documentary a positive review and said: "It helps matters when you have a group of engaging human subjects like soft-spoken Sedol, who’s as intensively contemplative as the game itself, contrasted by the spirited, personable Fan Hui, the Paris-based European champ who accepts an offer to serve as an advisor for the DeepMind team after suffering a demoralizing AI trouncing". He also mentioned that with the passion of Hauschka's Volker Bertelmann, the film's producer, this documentary shows many unexpected sequences, including strategic and philosophical components.[91] (Rechtshaffen, 2017 John Defore of The Hollywood Reporter, wrote this documentary is "an involving sports-rivalry doc with an AI twist." "In the end, observers wonder if AlphaGo's odd variety of intuition might not kill Go as an intellectual pursuit but shift its course, forcing the game’s scholars to consider it from new angles. So maybe it isn't time to welcome our computer overlords, and won't be for a while - maybe they'll teach us to be better thinkers before turning us into their slaves.” [92]

Greg Kohs, the director of the film, said "The complexity of the game of Go, combined with the technical depth of an emerging technology like artificial intelligence seemed like it might create an insurmountable barrier for a film like this. The fact that I was so innocently unaware of Go and AlphaGo actually proved to be beneficial. It allowed me to approach the action and interviews with pure curiosity, the kind that helps make any subject matter emotionally accessible." Kohs also said that "Unlike the film’s human characters – who turn their curious quest for knowledge into an epic spectacle with great existential implications, who dare to risk their reputation and pride to contest that curiosity – AI might not yet possess the ability to empathize. But it can teach us profound things about our humanness – the way we play board games, the way we think and feel and grow. It’s a deep, vast premise, but my hope is, by sharing it, we can discover something within ourselves we never saw before".[93]

Hajin Lee, a former professional Go player, described this documentary as being "beautifully filmed." In addition to the story itself, the feelings and atmosphere were also conveyed through different scene arrangements. For example, the close-up shots of Lee Sedol when he realizes that the AlphaGo AI is intelligent, the atmospheric scene of the Korean commentator's distress and affliction following the first defeat, and the tension being held inside the room. The documentary also tells a story by describing the background of AlphaGo technology and the customs of the Korean Go community. She suggests some areas to be covered additionally. For instance, the details of the AI prior to AlphaGo, the confidence and pride of the professional Go players, and the shifting of perspective to the Go AI between and after the match as “If anything could be added, I would include information about the primitive level of top Go A.I.s before AlphaGo, and more about professional Go players’ lives and pride, to provide more context for Lee Sedol’s pre-match confidence, and Go players’ changing perception of AlphaGo as the match advanced”.[94](Lee, 2017).

Fan Hui, a professional Go player, and former player with AlphaGo said that “DeepMind had trained AlphaGo by showing it many strong amateur games of Go to develop its understanding of how a human plays before challenging it to play versions of itself thousands of times, a novel form of reinforcement learning which had given it the ability to rival an expert human. History had been made, and centuries of received learning overturned in the process. The program was free to learn the game for itself.[95]

James Vincent, a reporter from The Verge, comments that “It prods and pokes viewers with unsubtle emotional cues, like a reality TV show would. “Now, you should be nervous; now you should feel relieved”. The AlphaGo footage slowly captures the moment when Lee Sedol acknowledges the true power of AlphaGo AI. In the first game, he had more experience than his human-programmed AI, so he thought it would be easy to beat the AI. However, the early game dynamics were not what he expected. After losing the first match, he became more nervous and lost confidence. Afterward, he reacted to attacks by saying that he just wanted to win the match, unintentionally displaying his anger, and acting in an unusual way. Also, he spends 12 minutes on one move, while AlphaGo only takes a minute and a half to respond. AlphaGo weighs each alternative equally and consistently. No reaction to Lee's fight. Instead, the game continues as if he was not there.

James also said that “suffice to say that humanity does land at least one blow on the machines, through Lee’s so-called “divine move”. “More likely, the forces of automation we’ll face will be impersonal and incomprehensible. They’ll come in the form of star ratings we can’t object to, and algorithms we can’t fully understand. Dealing with the problems of AI will take a perspective that looks beyond individual battles. AlphaGo is worth seeing because it raises these questions” [96](Vincent, 2017)

Murray Shanahan, a professor of cognitive robotics at Imperial College London, critics that “Go is an extraordinary game but it represents what we can do with AI in all kinds of other spheres,” says Murray Shanahan, professor of cognitive robotics at Imperial College London and senior research scientist at DeepMind, says. “In just the same way there are all kinds of realms of possibility within Go that have not been discovered, we could never have imagined the potential for discovering drugs and other materials.” [95]

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, Kohls looks into how the human mind functions under pressure, the significance of errors, the actions of the computer, and its inventiveness.[97] (Frederic and Mary Ann Brussat, n.d.)