IBM100 - Deep Blue IBM100 Choose your Country and Language: On May 11, 1997, an IBM computer called IBM ® Deep Blue ® beat the world chess champion after a six-game match: two wins for IBM, one for the champion and three draws. The match lasted several days and received massive media coverage around the world. It was the classic plot line of man vs. machine. Behind the contest, however, was important computer science, pushing forward the ability of computers to handle the kinds of complex calculations needed to help discover new medical drugs; do the broad financial modeling needed to identify trends and do risk analysis; handle large database searches; and perform massive calculations needed in many fields of science. Since the emergence of artificial intelligence and the first computers in the late 1940s, computer scientists compared the performance of these “giant brains” with human minds, and gravitated to chess as a way of testing the calculating abilities of computers. The game is a collection of challenging problems for minds and machines, but has simple rules, and so is perfect for such experiments. Over the years, many computers took on many chess masters, and the computers lost.

IBM computer scientists had been interested in chess computing since the early 1950s. In 1985, a graduate student at Carnegie Mellon University, Feng-hsiung Hsu, began working on his dissertation project: a chess playing machine he called ChipTest. A classmate of his, Murray Campbell, worked on the project, too, and in 1989, both were hired to work at IBM Research. There, they continued their work with the help of other computer scientists, including Joe Hoane, Jerry Brody and C. J. Tan. The team named the project Deep Blue. The human chess champion won in 1996 against an earlier version of Deep Blue; the 1997 match was billed as a “rematch.” The champion and computer met at the Equitable Center in New York, with cameras running, press in attendance and millions watching the outcome. The odds of Deep Blue winning were not certain, but the science was solid. The IBMers knew their machine could explore up to 200 million possible chess positions per second. The chess grandmaster won the first game, Deep Blue took the next one, and the two players drew the three following games.

Game 6 ended the match with a crushing defeat of the champion by Deep Blue. The match’s outcome made headlines worldwide, and helped a broad audience better understand high-powered computing. The 1997 match took place not on a standard stage, but rather in a small television studio. The audience watched the match on television screens in a basement theater in the building, several floors below where the match was actually held. The theater seated about 500 people, and was sold out for each of the six games. The media attention given to Deep Blue resulted in more than three billion impressions around the world. Deep Blue had an impact on computing in many different industries. It was programmed to solve the complex, strategic game of chess, so it enabled researchers to explore and understand the limits of massively parallel processing. This research gave developers insight into ways they could design a computer to tackle complex problems in other fields, using deep knowledge to analyze a higher number of possible solutions. The architecture used in Deep Blue was applied to financial modeling, including marketplace trends and risk analysis; data mining—uncovering hidden relationships and patterns in large databases; and molecular dynamics, a valuable tool for helping to discover and develop new drugs.

Ultimately, Deep Blue was retired to the Smithsonian Museum in Washington, DC, but IBM went on to build new kinds of massively parallel computers such as IBM Blue Gene ®. [Read more about this Icon of Progress.] The Deep Blue project inspired a more recent grand challenge at IBM: building a computer that could beat the champions at a more complicated game, Jeopardy!. Over three nights in February 2011, this machine—named Watson—took on two of the all-time most successful human players of the game and beat them in front of millions of television viewers. The technology in Watson was a substantial step forward from Deep Blue and earlier machines because it had software that could process and reason about natural language, then rely on the massive supply of information poured into it in the months before the competition. Watson demonstrated that a whole new generation of human - machine interactions will be possible.

Deep Blue beats Kasparov at chess - Feb 10, 1996 - HISTORY.com Deep Blue beats Kasparov at chess Share this: Deep Blue beats Kasparov at chess Author Deep Blue beats Kasparov at chess URL Publisher A+E Networks In the first game of a six-game match, an IBM computer dubbed “Deep Blue” becomes the first machine to beat a reigning world chess champion, Garry Kasparov. Despite this initial upset victory, man ultimately triumphed over machine as Kasparov goes went on to win the match, 4-2. Garry Kasparov, considered one of the greatest players in the history of chess, was born April 13, 1963, in the Russian republic of Azerbaijan. At 13, he was the Russian junior chess champ and in 1985, at 22, he became the youngest world champion in history when he defeated Anatoly Karpov.

Deep Blue’s origins date back to 1985, when Carnegie Mellon University doctoral student Feng Hsiung Hsu began developing a chess-playing computer called “ChipTest.” Hsu was joined on the project by Thomas Anantharaman and Murray Campbell and the computer later came to be known as “Deep Thought,” after a machine in the science-fiction novel The Hitchhiker’s Guide to the Galaxy. The men were later hired by IBM, where they continued to work on the chess-playing computer. In 1989, Gary Kasparov easily trounced Deep Thought when they met for a 2-game match. Developers continued to refine the supercomputer, which in 1993 was renamed “Deep Blue,” a combination of Deep Thought and Big Blue, IBM’s nickname. The 6-game match between Kasparov and Deep Blue began on February 10, 1996, at the Pennsylvania Convention Center in Philadelphia. Although Deep Blue was capable of evaluating 100 million different chess positions per second, the IBM team wasn’t sure how the computer would perform in competition and Kasparov was favored to win. Instead, much to his frustration, the world chess champ lost the first game to Deep Blue.

However, the tenacious, brilliant Kasparov quickly staged a comeback by winning the second game. The third and fourth games ended in a draw, while Kasparov took the fifth and sixth games, for a score of 4-2. A heavily publicized second 6-game match between man and machine began on May 3, 1997. For this match, an upgraded Deep Blue was able to examine 200 million different chess positions per second. Kasparov took the first game while Deep Blue won the second. The third, fourth and fifth games ended in a draw. On May 11, Deep Blue won the sixth as well as the match, 3.5 to 2.5. The victory was a huge publicity boost for IBM. A disgruntled Kasparov suggested Deep Blue had been aided by some sort of human assistance during the games, charges that IBM denied. Kasparov demanded a rematch, but instead, IBM retired Deep Blue. Kasparov retained his world chess champion title until 2000. In March 2005, he announced his retirement from professional chess.

More on This Topic February 10, 1996 : Deep Blue beats Kasparov at chess Introduction In the first game of a six-game match, an IBM computer dubbed “Deep Blue” becomes the first machine to beat a reigning world chess champion, Garry Kasparov. Despite this initial upset victory, man ultimately triumphed over machine as Kasparov goes went on to win the match, 4-2. Garry Kasparov, considered one of the greatest players in the history of chess, was born April 13, 1963, in the Russian republic of Azerbaijan. At 13, he was the Russian junior chess champ and in 1985, at 22, he became the youngest world champion in history when he defeated Anatoly Karpov. Deep Blue’s origins date back to 1985, when Carnegie Mellon University doctoral student Feng Hsiung Hsu began developing a chess-playing computer called “ChipTest.” Hsu was joined on the project by Thomas Anantharaman and Murray Campbell and the computer later came to be known as “Deep Thought,” after a machine in the science-fiction novel The Hitchhiker’s Guide to the Galaxy.

The men were later hired by IBM, where they continued to work on the chess-playing computer. In 1989, Gary Kasparov easily trounced Deep Thought when they met for a 2-game match. Developers continued to refine the supercomputer, which in 1993 was renamed “Deep Blue,” a combination of Deep Thought and Big Blue, IBM’s nickname. The 6-game match between Kasparov and Deep Blue began on February 10, 1996, at the Pennsylvania Convention Center in Philadelphia. Although Deep Blue was capable of evaluating 100 million different chess positions per second, the IBM team wasn’t sure how the computer would perform in competition and Kasparov was favored to win. Instead, much to his frustration, the world chess champ lost the first game to Deep Blue. However, the tenacious, brilliant Kasparov quickly staged a comeback by winning the second game. The third and fourth games ended in a draw, while Kasparov took the fifth and sixth games, for a score of 4-2. A heavily publicized second 6-game match between man and machine began on May 3, 1997.

For this match, an upgraded Deep Blue was able to examine 200 million different chess positions per second. Kasparov took the first game while Deep Blue won the second. The third, fourth and fifth games ended in a draw. On May 11, Deep Blue won the sixth as well as the match, 3.5 to 2.5. The victory was a huge publicity boost for IBM. A disgruntled Kasparov suggested Deep Blue had been aided by some sort of human assistance during the games, charges that IBM denied. Kasparov demanded a rematch, but instead, IBM retired Deep Blue. Kasparov retained his world chess champion title until 2000. In March 2005, he announced his retirement from professional chess. Article Details: February 10, 1996 : Deep Blue beats Kasparov at chess Author February 10, 1996 : Deep Blue beats Kasparov at chess URL Swift and Slashing, Computer Topples Kasparov - The New York Times The New York Times N.Y.

/ Region |Swift and Slashing, Computer Topples Kasparov Search Continue reading the main story In brisk and brutal fashion, the I.B.M. computer Deep Blue unseated humanity, at least temporarily, as the finest chess playing entity on the planet yesterday, when Garry Kasparov, the world chess champion, resigned the sixth and final game of the match after just 19 moves, saying, ''I lost my fighting spirit.'' The unexpectedly swift denouement to the bitterly fought contest came as a surprise, because until yesterday Mr. Kasparov had been able to summon the wherewithal to match Deep Blue gambit for gambit. The manner of the conclusion overshadowed the debate over the meaning of the computer's success. Grandmasters and computer experts alike went from praising the match as a great experiment, invaluable to both science and chess (if a temporary blow to the collective ego of the human race) to smacking their foreheads in amazement at the champion's abrupt crumpling. ''It had the impact of a Greek tragedy,'' said Monty Newborn, chairman of the chess committee for the Association for Computing, which was responsible for officiating the match.

Continue reading the main story It was the second victory of the match for the computer -- there were three draws -- making the final score 3 1/2 to 2 1/2, the first time any chess champion has been beaten by a machine in a traditional match. Mr. Kasparov, 34, retains his title, which he has held since 1985, but the loss was nonetheless unprecedented in his career; he has never before lost a multigame match against an individual opponent. Advertisement Continue reading the main story Afterward, he was both bitter at what he perceived to be unfair advantages enjoyed by the computer and, in his word, ashamed of his poor performance yesterday. ''I was not in the mood of playing at all,'' he said, adding that after Game 5 on Saturday, he had become so dispirited that he felt the match was already over. Asked why, he said: ''I'm a human being. When I see something that is well beyond my understanding, I'm afraid.'' Grandmasters at the match, at the Equitable Center in midtown Manhattan, were stunned into near-speechlessness, a feat in itself, amazed not just by the resignation but by Mr.

Kasparov's poor play in the game. ''I think he didn't try his best,'' said Susan Polgar, the women's world champion, who after the game issued her own challenge to I.B.M. to play against Deep Blue. The game itself was problematic for Mr. Kasparov from the start. Playing black and needing a victory to capture the match, he was perhaps too defiant in the early going, pursuing a risky sequence of moves in a conservative opening called the Caro-Kann. He encouraged Deep Blue to sacrifice a knight, resulting in a position that left his own king exposed, and many chess experts wondered if he hadn't made a simple blunder. It was all over not too much later. Having lost his queen and with his king dangerously exposed, Mr. Kasparov abruptly stood up to resign. ''It was a gamble,'' said Michael Khodarkovsky, a close adviser to Mr. Kasparov, before the strategy collapsed. He said Mr. Kasparov was trying to capitalize on the computer's aversion to playing with a material disadvantage. ''But the computer doesn't like to play in an unbalanced position,'' Mr. Khodarkovsky said. ''He wants to win.

He didn't come to play for a draw.'' Perhaps most surprising was Mr. Kasparov's performance at the postgame news conference, which was not the exuberant celebration envisioned by the tournament sponsor, I.B.M., but rather a tense occasion in which Mr. Kasparov's griped, apologized and vowed revenge. Advertisement Continue reading the main story ''I think it is time for Deep Blue to prove this was not a single event,'' he said, suggesting that the computer enter into regular match play with top chess players. ''I personally assure you that, if it starts to play competitive chess, put it in a fair contest and I personally guarantee you I will tear it to pieces.'' Patrick Wolff, a grandmaster who is a two-time American champion, was among those experts who were nonplussed by the champion's behavior. ''His resignation was probably premature, but he was probably lost,'' Mr. Wolff said. ''I think he was terrified at the prospect of losing an honest competition, and he gave himself an excuse, that this is not real chess. Well, I have news for him. This is real chess. What we've seen today is psychological weakness of the sort I'd never expect from him.'' Mr.

Kasparov had his supporters, particularly among those who thought this was a spectacle staged by I.B.M. for the good of I.B.M. ''This was not a serious chess match,'' said Lev Alburt, a former United States champion who has said there are 100 grandmasters in the world who could beat Deep Blue. ''This was a show. If they want to prove it was more than a show, let them play anyone but Garry. If it would play against, say, Grandmaster Boris Gulko, who is not even among the top 50, I am willing to bet $10,000 the computer would lose.'' At the news conference after the game, a dark-eyed and brooding champion said that his problems began after the second game, won by Deep Blue after Mr. Kasparov had resigned what was eventually shown to be a drawn position. Mr. Kasparov said he had missed the draw because the computer had played so brilliantly that he thought it would have obviated the possibility of the draw known as perpetual check. ''I do not understand how the most powerful chess machine in the world could not see simple perpetual check,'' he said.

He added he was frustrated by I.B.M.'s resistance to allowing him to see the printouts of the computer's thought processes so he could understand how it made its decisions, and implied again that there was some untoward behavior by the Deep Blue team. Asked if he was accusing I.B.M. of cheating, he said: ''I have no idea what's happening behind the curtain. Maybe it was an outstanding accomplishment by the computer. But I don't think this machine is unbeatable.'' Mr. Kasparov, who defeated a predecessor of Deep Blue a year ago, won the first game of this year's match, but it was his last triumph, a signal that the computer's pattern of thought had eluded him. He couldn't figure out what its weaknesses were, or if he did, how to exploit them. Advertisement Continue reading the main story He said if there were another match, he would insist it not be sponsored by I.B.M., that it should be at least 10 games and 20 days long (''You have to give a human a chance to rest'') and that the previous games played by the computer must be available.

He also said he would abandon the anticomputer strategy of playing flaccid openings and return to his normal game. ''I played a friendly match,'' he said. ''I was sure I would win because I was sure the computer would make certain kinds of mistakes, and I was correct in Game 1. But after that the computer stopped making those mistakes. Game 2 had dramatic consequences, and I never recovered.'' The I.B.M. team denied there had been any hanky-panky, and the team leader, C. J. Tan, said the computer logs would be published in appropriate journals in the near future. ''We are proud to have played a role in this historic event,'' he said, in a statement at the news conference. ''Gary has a brilliant mind, and he's a very brave man. He's a man who sees the future, who understands where technology can take us.'' He said he found Mr. Kasparov's suggestion that Deep Blue engage in regular match play against top grandmasters interesting enough to consider, and that consideration would be given to a third match with Mr. Kasparov. What Mr. Kasparov does have that the computer doesn't, said Mr.

Newborn, is a pulse. Which may be both humanity's boon and its curse, as far as chess is concerned. ''One of the lessons here is that we all underestimated the physiological and psychological aspects of the match,'' Mr. Newborn said. Garry Kasparov 13th World Champion, 1985 - 1993 PCA Champion, 1993 - present Garry Kimovich Kasparov was born Gary Weinstein in Baku, Azerbaijan, USSR in 1963. Kasparov learned to play chess from his father who later died in a road accident when he was 7 years old. He subsequently changed his name to Kasparov, a Russified version of his mother's maiden name, Kasparyan. Kasparov's chess talent was apparent at an early age. In 1973 he attended the Botvinnik Chess School and Kasparov continued to make rapid progress. In 1975 at the age of 12 he became the youngest ever player to win the USSR Junior Championship. At 16 he won the World Junior Championship.

He achieved the title of Grandmaster on his 17th birthday In 1984 he challenged the current reigning World Champion, Anatoly Karpov for the title. This match was a hard fought battle and lasted 6 months, the longest in the history of chess. It was finally stopped by Florencio Campomanes, president of FIDE and a rematch ordered. In November 1985 Kasparov won the rematch against Karpov and became the youngest World Champion at the age of 22 years. After long term friction with the international chess organisation, FIDE, Kasparov set up the rival organisation, the Professional Chess Association (PCA) and arranged a World Championship match in 1993 in which he beat British Grandmaster, Nigel Short. At the same time FIDE held their official Championship match between former World Champion, Anatoly Karpov and Jan Timman which Karpov won. Both Kasparov and Karpov claim the title of World Champion. In 1996 Kasparov competed in a six game match against an IBM computer called Deep Blue. Kasparov won with a score of 4 games to 2 games.

The following year, he competed against an improved version called Deeper Blue and was defeated 3.5 games to 2.5 games. It was the first time a Grandmaster had lost a series of games to a computer. He is currently the highest rated player there has ever been in the history of chess. Illumin - Deep Blue: The History and Engineering behind Computer Chess Tags: computer science , entertainment Computer chess software available today is a staple of modern computing distractions. Few may recognize the rich history behind the development of that technology. In a revolutionary chess tournament in 1997, the chess world champion was defeated by an IBM supercomputer called Deep Blue, shocking the media and the general public. To the artificial intelligence community, however, this was a long time coming. After almost fifty years of developing adequate computing technology and formulating sufficient chess playing strategies, computer scientists were finally able to solve the “chess problem” and defeat the best human player. The specific search algorithm used was revolutionary for its time, and Deep Blue’s victory served as a defining moment in the history of the field of artificial intelligence. However, some doubts have been raised regarding computer chess’s relevance to the field of artificial intelligence.

Strong arguments have been made for both sides by prominent experts in the fields, and the fate of future computer chess studies hangs in the balance. Introduction After 50 years of research and development in conjunction with artificial intelligence, the study of computer chess culminated during two matches between Deep Blue, a chess supercomputer funded by IBM, and the Chess World Champion Garry Kasparov. The 1996 and 1997 matches were media sensations, heavily promoted as a battle of wits between man and machine. When the machine finally prevailed in 1997, the artificial intelligence field and scientific community marveled at their own triumph. However, recent experts have disagreed with the predictions of their predecessors, which suggested that such an event would herald in an era of intelligent machines, pointing to several issues of comparing chess to intellect. While continued research on artificial intelligence is necessary before it becomes an everyday reality, Deep Blue’s victory over Garry Kasparov in 1997 still remains an impressive engineering feat and a foundation for current achievements in the field. History: From Turing’s Paper Machine to Deep Blue Computer chess has been associated with the field of artificial intelligence ever since the emergence of computer science in the mid-20th century.

Many of the biggest names in both fields, such as Alan Turing, a mathematician whom many credit with founding modern computer science; John McCarthy, who helped coin the term “artificial intelligence”; and Claude Shannon, a pioneer in Boolean logic, supported the use of chess as proper starting point for the development of intellect [1]. Undeterred by the lack of technology and establishment of a precedence at the time, Turing sought to support his theory and effectively wrote an algorithm for the first chess program. Unfortunately, the program, which was put on paper and implemented by Turing himself, was perhaps better visualized than implemented, as a colleague soon easily defeated the program. Despite this unceremonious introduction, chess programs would soon flourish, gaining the recognition as a standard for research for many computer scientists [2]. James/Flickr Figure 1: A computer from the Computer History Museum that is similar to Deep Blue. Many early programmers devoted a particularly large amount of time and effort to chess because they perceived the game as a unique challenge that would provide a breakthrough for artificial intelligence. These researchers saw in the structure of chess a simplified model for large-scale problem solving. Players in chess only have the primary task of checkmating the opponent’s king.

In addition, several other constraints inherent to the game, such as the relatively small board and limited amount of pieces with distinct movement patterns, helped computer scientists define specific parameters within their programs. In terms of human comparison, the already existing hierarchy of rated chess players throughout the world gave engineers a scale with which to easily and accurately measure the success of their machine. After several decades of research yielded little progress, it is surprising that Deep Blue, which would arguably become the ultimate champion of computer chess, began with a chip that was built in six months by a Carnegie Mellon graduate student and an inexperienced chess player. In 1985, while doing secondary research for another professor, Feng-Hsiung Hsu discovered a method that could reduce the work done by the chess machine’s move generator by simplifying the program’s silicon chip design from a maximum of sixty-four distinct chips used by contemporary machines down to just one. While looking over the design of sixty-four chips, he discovered that the extensive amount of wiring could easily be reduced, decreasing the delay of the design and saving time and precious space on the chip [3]. After further studying the design sketches, Hsu discovered that another large portion of the chip was redundant and could therefore be excised completely.

Because he was only removing useless components, Hsu’s new, reduced design did not lose the speed expected with his reduction in size. In fact, Hsu’s proposal actually suggested a theoretical gain of speed by a factor of twenty due to the reduced complexity of his new chip. The single chip design that Hsu developed would evolve over time to form the backbone of Deep Blue’s hardware (see Fig. 1). Google AI wants to dominate board games - CNET CNET Google AI wants to dominate board games Google AI wants to dominate board games A computer defeats a master of the ancient board game Go. Is Monopoly far behind? Building AI that's good at playing board games isn't the end game. Photo by Blue Jean Images/Corbis In 1997, IBM's Deep Blue computer famously defeated world chess champion Garry Kasparov. Now, thanks to Google, a computer can kick your butt at the ancient board game Go. Google DeepMind, the search giant's London-based artificial intelligence arm, has developed a program called AlphaGo that can beat a human player at the two-person board game.

In fact, the program managed to sweep European champion Fan Hui in a five-game match, the first time a computer has defeated a professional player in the full-size game of Go. Go's complex and abstract nature makes it a more challenging AI project than designing a similar program for chess. The strategy game, which originated in China thousands of years ago, is played on a 19x19 grid with black and white stones and challenges players to occupy more of the board than their opponent. The machines are winning An artificial intelligence program called Google DeepMind just made history when it beat a master of the ancient Chinese board game Go. 0:47 Close Drag A Go board can be configured in a constellation of arrangements that outnumbers the atoms in the universe, according to Demis Hassabis, who runs Google DeepMind. The vast number of moves and outcomes makes it more challenging to artificial intelligence than chess, he said. Related stories Elon Musk helps fund AI to protect humanity Beating a Go champion achieves "one of the long standing grand challenges of AI," said Hassabis on a conference call Tuesday. His team published its work Wednesday in the international science journal Nature.

AlphaGo's success at the ancient game comes as artificial intelligence moves from scientific curiosity to real-world applicability. AI's potential has worried some technologists, including SpaceX CEO Elon Musk and Microsoft co-founder Bill Gates , who worry about its potential dangers. In August 2014, Musk expressed fears that AI could be more dangerous than nuclear weapons . Even famed physicist Stephen Hawking has voiced reservations about AI. Google is "very cognizant about ethical issues," said Hassabis, adding the company, which has re-organized under holding company Alphabet, agreed not to use DeepMind's technology for military purposes. Google bought DeepMind in 2014. AlphaGo uses a combination of machine learning technologies called neural networks and tree search. If you love complex algorithms, feel free to dig into the way AlphaGo plays and learns in Nature . Facebook's AI team has also been developing a program that can play the game . "Scientists have been trying to teach computers to win at Go for 20 years," said Facebook CEO Mark Zuckerberg in a post Tuesday .

"We're getting close, and in the past six months we've built an AI that can make moves in as fast as 0.1 seconds and still be as good as previous systems that took years to build." Building AI that's good at playing board games isn't the, ummm, end game. DeepMind said the same methods used to master Go could one day be used for tasks like climate modeling and disease analysis. In the short term, aspects of the technology behind AlphaGo could show up in the digital assistant on your phone or in the recommendation app you use to pick a restaurant in the next year or two, said Hassabis. AlphaGo's next challenge will be to play Lee Sedol, one of the top Go players in the world, in Seoul in March. How the Computer Beat the Go Master - Scientific American Scientific American Computing How the Computer Beat the Go Master As a leading go player falls to a machine, artificial intelligence takes a decisive step on the road to overtaking the natural variety By Christof Koch on March 19, 2016 South Korean professional Go player Lee Sedol is seen on a TV screen during the Google DeepMind Challenge Match against Google's artificial intelligence program, AlphaGo.

Credit: Credit: AP Photo/Ahn Young-joon Advertisement | God moves the player, he in turn the piece. But what god beyond God begins the round Of dust and time and sleep and agony? —Jorge Luis Borges As I write this column, a computer program called AlphaGo is beating the professional go player Lee Sedol at a highly publicized tournament in Seoul. Sedol is among the top three players in the world, having attained the highest rank of nine dan. The victory over one of humanity’s best representatives of this very old and traditional board game is a crushing 3 to 1, with one more game to come. With this defeat, computers have bettered people in the last of the classical board games, a game known for both depth and simplicity. An era is over and a new one is beginning. The methods underlying AlphaGo, and its recent victory, have startling implications for the future of machine intelligence. Coming out of nowhere The ascent of AlphaGo to the top of the go world has been stunning and quite distinct from the trajectory of machines playing chess.

Over a period of 10 years a dedicated team of hardware and software engineers, eventually hired by IBM, built and programmed a special-purpose supercomputer, named Deep Blue, that did one thing and one thing only—play chess by evaluating 200 million board positions per second. In a widely expected development, the IBM team challenged then reigning world chess champion Garry Kasparov. In a six-game match played in 1996, Kasparov prevailed against Deep Blue by three wins, two draws and one lossbut lost a year later in a historic rematch 2-1/2 to 3-1/2. Chess is a classic game of strategy, similar to tic-tac-toe (noughts and crosses), checkers (draughts), reversi (Othello), backgammon and go, in which players take turns placing or moving pieces. Unlike card games where participants only see their own as well as everybody’s discarded cards, players have full access to all relevant information with chance playing no role. The rules of go are considerably simpler than those of chess.

Black and White sides each have access to a bowl of black and white stones and each place one in turn on a 19 by 19 grid. Once placed, stones don’t move. The intent of the game, originating in China more than 2,500 years ago, is to completely surround opposite stones. Such encircled stones are considered captured and are removed from the board. Out of this sheer simplicity great beauty arises—complex battles between Black and White armies that span from the corners to the center of the board. Strictly logical games, such as chess and go, can be characterized by how many possible positions can arise, bounding their complexity. Depending on the phase of the game, players must pick one out of a small number of possible moves, called the game’s breadth or branching factor b. If it’s White’s turn, she needs to pick one of b possible moves; Black can respond to each of these with b countermoves of his own. That is, after one turn, there are already b times b or b2 moves that White needs to consider in her strategizing.

Assuming that a game of chess lasts on average d moves (called the game’s depth), the complete game tree from any one starting position—the list of all moves, countermoves, counter-countermoves and so on until one side or the other wins—contains about b times b times b..., d times in a row, or bd end positions (so-called terminal nodes or leaves of the search tree). Given that a typical chess game has a branching factor of about 35 and lasts 80 moves, the number of possible moves is vast, about 3580 (or 10123), aka the “Shannon number” after the Bell Laboratory pioneer Claude Shannon who not only invented information theory but also wrote the first paper on how to program a machine to play chess, back in 1950. Shannon’s number, at 10123, is huge, in particular considering there are only about 1080 atoms in the entire observable universe of galaxies, stars, planets, dogs, trees and people. But go’s complexity is bigger, much bigger.

With its breadth of 250 possible moves each turn (go is played on a 19 by 19 board compared to the much smaller eight by eight chess field) and a typical game depth of 150 moves, there are about 250150, or 10360 possible moves. This is a number beyond imagination and renders any thought of exhaustively evaluating all possible moves utterly and completely unrealistic. Given this virtually illimitable complexity, go is, much more than chess, about recognizing patterns that arise when clutches of stones surround empty spaces.Players perceive, consciously or not, relationships among groups of stones and talk about such seemingly fuzzy concepts as “light” and “heavy” shapes of stones, and aji, meaning latent possibilities. Such concepts, however, are much harder to capture algorithmically than the formal rules of the game. Accordingly, computer go programs struggled compared with their chess counterparts, and none had ever beat a professional human under regular tournament conditions. Such an event was prognosticated to be at least a decade away. And then AlphaGo burst into public consciousness via an article in one of the world’s most respected science magazines, Nature, on January 28 of this year.

Its software was developed by a 20-person team under the erstwhile chess child prodigy and neuroscientist turned AI pioneer, Demis Hassabis, out of his London-based company DeepMind Technologies, acquired in 2014 by Google. Most intriguingly, the Nature article revealed that AlphaGo had played against the winner of the European go championships, Fan Hui, in October 2015 and won 5 to 0 without handicapping the human player, an unheard of event. [Scientific American is part of Springer Nature.] Looking under the hood What is noteworthy is that AlphaGo’s algorithms do not contain any genuinely novel insights or breakthroughs. The software combines good old-fashioned neural network algorithms and machine-learning techniques with superb software engineering running on powerful but fairly standard hardware—48 central processing units (CPUs) augmented by eight graphical processing units (GPUs) developed to render 3-D graphics for the gaming communities and exceedingly powerful for running certain mathematical operations. At the heart of the computations are neural networks, distant descendants of neuronal circuits operating in biological brains.

Layers of neurons, arranged in overlapping layers, process the input—the positions of stones on the 19 by 19 go board—and derive increasingly more abstract representations of various aspects of the game using something called convolutional networks. This same technology has made possible recent breakout performances in automatic image recognition—automatically labeling, for example, all images posted to Facebook. For any particular board position, two neural networks operate in tandem to optimize performance. A “value network” reduces the effective depth of the search by estimating how likely a given board position will lead to a win without chasing down every node of the search tree, and a “policy network” reduces the breadth of the game, limiting the number of moves for a particular board position the network considers by learning to chose the best moves for that position. The policy network generates possible moves that the value network then judges on their likelihood to vanquish the opponent.

It is ironic that the most powerful techniques for this fully deterministic game—in which every move is entirely determined based on earlier moves—are probabilistic, based on the realization that as the vast majority of the branches of the tree can’t be feasibly explored, it’s best to pick some of the most promising branches almost at random and to evaluate them all the way to the end—that is, to a board position in which either one or the other player wins. The various nodes in the game tree can then be weighted toward those that are most likely to lead to a win. Done over and over again, such a pseudo-random sampling, termed a Monte Carlo tree search, can lead to optimal behavior even if only a tiny fraction of the complete game tree is explored. Monte Carlo techniques—born in sin at the Los Alamos National Laboratory in the late 1940s to design the first nuclear weapons—are extensively used in physics. This Monte Carlo tree technique was successfully implemented in Crazy Stone, one of the earliest programs that played go at a decent amateur level. Yet a Monte Carlo tree search by itself wasn’t good enough for these programs to compete at the world-class level.

That required giving AlphaGo the ability to learn, initially by exposing it to previously played games of professional go players, and subsequently by enabling the program to play millions of games against itself, continuously improving its performance in the process. In the first stage, a 13-layer policy neural network started as a blank slate—with no prior exposure to go. It then was trained on 30 million board positions from 160,000 real-life games taken from a go database. That number represents far more games that any professional player would encounter in a lifetime. Each board position paired with the actual move chosen by the player (which is why this technique is called supervised learning) and the connections among the networks were adjusted via standard so-called deep machine-learning techniques to make the network more likely to pick the better move the next time. The network was then tested by giving it a board position from a game it had previously never seen. It accurately, although far from perfectly, predicted the move the professional player had picked In a second stage, the policy network trained itself using reinforcement learning. This technique is a lasting legacy of behaviorism—a school of thought dominant in psychology and biology in the first half of the last century.

It professes the idea that organisms—from worms, flies and sea slugs to rats and people—learn by relating a particular action to specific stimuli that preceded it. As they do this over and over again, the organism builds up an association between stimulus and response. This can be done completely unconsciously, using rote learning. Consider training your dog to roll over and “play dead” on command. You do so by breaking this complex behavior into smaller actions—lie on the ground, turn over, extend paws into the air. As soon as the action occurs either spontaneously or because you show your dog how and she tries to emulate you, it is rewarded (or “reinforced” in the lingo) by a combination of praise or morsel of food. Done enough times, the dog will eventually act dead on command. Reinforcement learning was implemented years ago in neural networks to mimic animal behavior and to train robots. DeepMind demonstrated this last year with a vengeance when networks were taught how to play 49 different Atari 2600 video games, including Video Pinball, Star Gunner, Robotank, Road Runner, Pong, Space Invaders, Ms Pac-Man, Alien and Montezuma’s Revenge.

(In a sign of things to come, atari is a Japanese go term, signifying the imminent capture of one of more stones.) Each time it played, the DeepMind network “saw” the same video game screen, including the current score that any human player would see. The network’s output was a command to the joystick to move the cursor on the screen. Following the diktat of the programmer to maximize the game score, the algorithm did so and figured out the rules of the game over thousands and thousands trials. It learns to move, to hit alien ships and to avoid being destroyed by them. And for some games, it achieves superhuman performance. The same powerful reinforcement learning algorithm was deployed by AlphaGo for go, starting from the configuration of the policy network after the supervised learning step. In a third and final stage of training, the value network that estimates how likely a given board position is likely to lead to a win, is trained using 30 million self-generated positions that the policy network chose. It is this feature of self-play, impossible for humans to replicate (for it would require the player’s mind to split itself into two), that enables the algorithm to relentlessly improve.

A peculiarity of AlphaGo is that it will pick a strategy that maximizes the probability of winning regardless of by how much. For example, AlphaGo would prefer to win with 90 percent probability by two stones than an 85 percent probability by 50 stones. Few people would give up a slightly riskier chance to crush their opponent in favor of eking out a narrow but surer victory. The end result is a program that performed better than any competitor and beat the go master Fan Hui. Hui, however, is not among the top 300 world players —and among the upper echelons of players, differences in their abilities are so pronounced that even a lifetime of training would not enable Hui to beat somebody like Lee Sedol. Thus, based on the five publicly available games between AlphaGo and Hui, Sedol confidently predicted that he would dominate AlphaGo, winning five games to nothing or, perhaps on a bad day, four games to one. What he did not reckon is that the program he was facing in Seoul was a vastly improved version of the one Hui had encountered six months earlier, optimized by relentless self-play.

An interesting difference between Deep Blue and AlphaGo is that the evaluation engine of the former, assigning a positive (good) or a negative (bad) value to any one chessboard position, was explicitly programmed. This distinction enabled Deep Blue’s programmers to add explicit rules, such as “if this position occurs, do that,” to its repertoire of tactics. That is not possible for Deep Blue’s neural network descendent, AlphaGowhere all knowledge is encoded implicitly in the “weights” of the network. What next? Deep Blue represented a triumph of machine brawn over a single human brain. Its success was almost completely predicated on very fast processors, built for this purpose. Although its victory over Kasparov was a historic event, the triumph did not lead to any practical application or to any spin-off. Indeed, IBM retired the machine soon thereafter. The same situation is not likely to occur for AlphaGo. The program runs on off-the-shelf processors. Giving it access to more computational power (by distributing it over a network of 1,200 CPUs and GPUs) only improved its performance marginally. The feature that makes the difference is AlphaGo’s ability to split itself into two, playing against itself and continuously improving its overall performance.

At this point it is not clear whether there is any limitation to the improvement AlphaGo is capable of. (If only the same could be said of our old-fashioned brains.) It may be that this constitutes the beating heart of any intelligent system, the Holy Grail that researchers are pursuing—general artificial intelligence, rivaling human intelligence in its power and flexibility. It is likely that Hassabis’s DeepMind team is contemplating designing more powerful programs, such as versions that can teach themselves go from scratch, without having to rely on the corpus of human games as examples, versions that learn chess, programs that simultaneously play checkers, chess and go at the world-class level or ones that can tackle no-limit Texas hold ‘em poker or similar games of chance. In a very commendable move, Hassabis and his colleagues described in exhausting details the algorithms and parameter settings the DeepMind team used to generate AlphaGo in the accompanying Nature publication. This further accelerates the frenetic pace of AI research in academic and industrial laboratories worldwide. For reinforcement, algorithms based on trial and error learning can be applied to myriad problems with sufficient labeled data, be they financial markets, medical diagnostic, robotics, warfare and so on.

A new era has begun with unknown but potential monumental medium- and long-term consequences for employment patterns, large-scale surveillance and growing political and economic inequity. What of the effects of AlphaGo on go itself? Despite doomsayers to the contrary, the rise of ubiquitous chess programs revitalized chess, helping to train a generation of ever more powerful players. The same may well happen to the go community. After all, the fact that any car or motorcycle can speed faster than any runner did not eliminate running for fun. More people run marathons than ever. Indeed, it could be argued that by removing the need to continually prove oneself to be the best, humans may now more enjoy the nature of this supremely aesthetic and intellectual game in its austere splendor for its own sake. Indeed, in ancient China one of the four arts any cultivated scholar and gentleman was expected to master was the game of go. Just as a meaningful life must be lived and justified for its own intrinsic reasons, so should go be played for its intrinsic value—for the joy it gives. As the philosopher Mark Rowlands puts it, this joy can assume many forms: “There is the joy of focus, the experience of being completely immersed in what one is doing.

There is the joy of dedication, the experience of being dedicated to the deed and not the outcome, the activity and not the goal. There is the joy of enduring, the experience of playing the game as hard as you can play it, of giving everything you have to the game and leaving nothing in the tank, no matter the experiential toll this exacts. This is the joy of defiance, wild and fierce: No, you will not break me, not here, not today.” In his battle against a new and superior go force, Lee Sedol, representative for all of us, has shown this joy. This article is dedicated to him. AlphaGo seals 4-1 victory over Go grandmaster Lee Sedol | Technology | The Guardian Artificial intelligence (AI) AlphaGo seals 4-1 victory over Go grandmaster Lee Sedol DeepMind’s artificial intelligence astonishes fans to defeat human opponent and offers evidence computer software has mastered a major challenge The world’s top Go player, Lee Sedol, lost the final game of the Google DeepMind challenge match.

Photograph: Yonhap/Reuters Tuesday 15 March 2016 06.12 EDT First published on Tuesday 15 March 2016 05.45 EDT Close This article is 11 months old Google DeepMind’s AlphaGo program triumphed in its final game against South Korean Go grandmaster Lee Sedol to win the series 4-1, providing further evidence of the landmark achievement for an artificial intelligence program. Lee started Tuesday’s game strongly, taking advantage of an early mistake by AlphaGo. But in the end, Lee was unable to hold off a comeback by his opponent, which won a narrow victory. AlphaGo: its creator on the computer that learns by thinking Read more After the results were in, Google DeepMind co-founder Demis Hassabis called today’s contest “One of the most incredible games ever,” saying AlphaGo mounted a “mind-blowing” comeback after an early mistake. Demis Hassabis (@demishassabis) #AlphaGo wins game 5! One of the most incredible games ever. To comeback from the initial big mistake against Lee Sedol was mind-blowing!!!

March 15, 2016 This was the fifth game in seven days, in what was a draining, emotional battle for Lee. AlphaGo had won the first three, but Lee took the fourth game on Sunday. He remained in his seat as the game’s results were announced, his eyes swelling with tears. In a post-game press conference, he expressed regret over his defeat. “I failed,” he said. “I feel sorry that the match is over and it ended like this. I wanted it to end well.” Throughout the match, Lee won praise from observers for a determined, creative approach to AlphaGo, an opponent that is invulnerable to stress and fatigue. In Tuesday’s press conference, Chris Garlock, one of the live commentators said the match was composed of “five beautiful and historic games,” adding, “I think we’ll be studying these for years to come.” Due to Go’s complexity and the importance of reaction and intuition, it has proved harder for computers to master than simpler games such as checkers or chess. Go has too many moves for a machine to win by brute-force calculations, which is how IBM’s Deep Blue famously beat former world chess champion Garry Kasparov in 1997.

AlphaGo’s win over Lee is significant because it marks the first time an artificial intelligence program has beaten a top-ranked Go professional, a victory experts had predicted was still years away. AlphaGo beat European Go champion Fan Hui in October, but Lee was expected to be a tougher challenge. The match has brought an unusual level of attention to Go, a game that is popular in east Asia but not widely played in the west. Go insiders say they are not used to being in the spotlight. “I’ve never seen this much attention for Go, ever,” Lee Ha-jin, secretary general at the International Go Federation and guest commentator on Tuesday’s live broadcast, said. Google DeepMind has talked about applying the deep neural networks and machine learning techniques that AlphaGo used to master Go to more pressing areas such as healthcare and robotics. But with AlphaGo’s victory in the books, Hassabis was tightlipped, saying his team will need to return to the UK and spend “weeks or months” going over the results of the match before announcing their next moves.

Software program can weigh up legal evidence and moral questions of right and wrong to predict the outcome of trials Published: 23 Oct 2016 Professor praises creation of Cambridge University institute to study future of artificial intelligence Published: 19 Oct 2016 Deep learning needs to become more efficient if it is going to move from using data to categorise images of cats to diagnosing rare illnesses Published: 27 Sep 2016 Combining external memory and deep learning, DeepMind’s program learns how to do tasks independently, and could pave the way for sophisticated AI assistants Published: 12 Oct 2016 Harry Armstrong and Jared Robert Keller Published: 15 Sep 2016

Question: At what game was a former world champion famously defeated by a computer called Deep Blue in 1997?
Answer: Chess