Lee Sedol defeated by deep learning AlphaGo in historic match
The most spectacular, remarkable and pioneering match in the history of the most ancient of games has taken place all the while: the Google DeepMind challenging match where AI program AlphaGo outbraved top Go professional Lee Sedol (9p) who has been at the top of the world league for more than fifteen years. According to his own claimings Lee Sedol has a Go-board in his head day and night: "if I come up with new strategies I place stones on the board in my head, even when I watch tv, have been drinking, or playing billiards".
What worldwide is regarded as the most as the most outstanding grand challenge for artificial intelligence, namely mastering the game of Go by computer programs, may have become a closed down chapter with this match. A chapter of searching for more than half a century, inventing new algorithms, translating go principles into manageable concepts for computers, and the development of ever improving programs with only one single goal: playing without handicap against humans without completely being sweeped off the Go-board.
Deep Learning AlphaGo, itself still in its infancy, is the very beginning of a great revolution in Artificial Intelligence. Barely half a year ago the program beated for the first time in history a Go-pro in an even game: Fan Hui (2p). Now it is ready for the enormous challenge to outperform and to defeat one of the strongest players of the world. DeepMind's ultimate aim thereby is to disentangle AI completely and make the world a better place (a real advertising message).
With more than 100 million people worldwide who have watched the five games of this historic match online and the hundreds and hundreds journalists, commentators, and reporters who attended the press conferences during the match, you can imagine that uncountable many questions were dying to be asked:
Why is this match so outstanding? How well does AlphaGo play against Lee Sedol? Is the AI program able to play a reasonable game? What is hidden under the hood of AlphaGo? Is the progam able to come up with new and 'creative' moves? Has AlphaGo made some progress since the sometimes malfunctioning version against Fan Hui? How did Lee Sedol prepare himself mentally for this match against AlphaGo? Is he able to detect weaknesses in AlphaGo's way of play and to make use of them? Does Lee Sedol remain himself when playing against an opponent who cannot be seen by him and he doesn't know anything about? Is he able to withstand the psychological pressure of the hundreds of millions that are watching his go-actions closely? Is AlphaGo able to improve even further in the future?
Already being felt as the most wonderful, inspiring, overwhelming and most spectacular match of the 21st century. De games played by Lee Sedol and AlphaGo will be studied and analysed for tens of years to come and as a reference for what will be without doubt one of the biggest landmarks ever achieved in the history of AI.
In this overview article, from a bird's eye view and in a bird flight, everything about and around the historic match between AlphaGo and Lee Sedol, highlights of the games played, how both Lee Seol and Demis Hassabis (and their supporting teams) have been blindsided and blown off completely by AlphaGo's phenomenal strong way of playing as well as by the final match outcome, remarkable background details and telling photo's, what this match is really about, how the world has experienced, processed, and tries to recover from this match, the massive impact that this match is expected to have on both Go and Artificial Intelligence worldwide.
The Match details: why, what and how?
Regarded as the outstanding grand challenge for artificial intelligence, Go has been considered for more than halve a century as one of the most difficult games for computers to master due to its sheer complexity which makes brute force exhaustive search intractable (apart from the fact that there are more possible board configurations than the number of atoms in the visible universe).
On January 27th this year, pioneering news went all over the world: AI program AlphaGo from Google DeepMind wins landmark five game match against reigning European Champion Fan Hui (2p). Never before, a computer program defeated a Go-prof with 5-0 in formal games without handicap. The most challenging and complex job until now, both for computers and artificial intelligence worldwide, thus appears to be brought to completion. Despite winning the match against Fan Hui, however, AlphaGo showed several weaknesses and the DeepMind team wanted therefore to investigate if they would be able to improve and upgrade AlphaGo's play to come level with that of the top Go professionals of the world. To this end, the DeepMind team also invited Fan Hui to help them to improve the program.
On Feb. 4th Demis Hassabis, head and co-founder of Google's DeepMind, announced in a tweet that AlphaGo will play a match coming March 9-15 against the best human Go player of the last decade: Lee Sedol (9p). This will be the biggest and most spectacular, remarkable, pioneering match in the history of the most ancient of games and for sure will long be referred to as the match of the 21st century.
Most important goal of this match to the DeepMind team will be to test if an improved version of AlphaGo can be an equivalent opponent for Lee Sedol and perhaps even will be able to defeat him. Moreover, to find and fix any new weaknesses or immature types of moves of AlphaGo.
The overarching and most important long-term goal of the DeepMind team, however, is to develop generic deep learning software that is able to tackle various ultra complex problems from a wide area of research fields including health care, energy, transportation, famine, genetics, physics, and so on, to support human scientists in finding effective solutions.
Up to now, computer programs never have been able to beat the very best at Go so the match will be also a way of testing and judging the suddenly rapid progress of AI – how far these technologies have come (and perhaps how far they can go), what scientists and engineers have achieved so far in the context of AI.
One of the most intriguing and remarkable decisions of the DeepMind team while training AlpaGo has been to use a giga collection of amateur games (≥ 6d to 8-9 dan, this is about 1-2d professional rankings) from the KGS Go Server. While they could have made the choice to use strong pro games instead (including strongest 9p profs of the world). Hassabis has repeatedly stated, confirmed and emphasized that there is –not any-- strong pro game included in the database that AlphaGo used to learn and train from. And specifically: the database doesn't contain any game played by Lee Sedol. Yet, there are at least 85,000 pro games publicly accessible out there, more than half the volume of the 130,000 KGS games that were used to train AlphaGo’s base system.
So let's imagine you have almost unlimited financial resources, one of the most advanced, distributed, and generic cloud computing platform of the world, and really many mega talented employees (and daughter companies) that are beyond any doubt among the best of the best in state-of-the-art AI research world wide. What on earth could be a reason for you to explicitly –not-- use the largest collections of Go-prof games available nowadays? Usage or copy rights? Costs, or perhaps huge efforts to be able to use these games? Extremely unlikely. Perhaps, the 80,000 pro games (as compared to the 130,000 amateur games) are inadequate or too inhomogeneous for AlphaGo to learn from effectively? Very improbable and unlogical as the convolutional (read simply:coding by means of transformation) neural networks that AlphaGo is based on, are easily capable of extracting autonomously millions of characteristic features from game positions and patterns, even in case of using a relatively small number of, say 50,000 games.
Therefore, wouldn't it be more than obvious for the smart and highly motivated team behind AlphaGo, to go --exclusively-- for collections of professional Go-games, if their main and ultimate goal is to design, develop, train, test, use, and improve the strongest AI program worldwide to play Go in order to try to beat in the end the very best top professionals Go players in the world?
So there probably is a very simple, logical, inherent, natural, and above all fundamental reason for this: the DeepMind team will investigate and analyse whether the way they designed and trained AlphaGo, i.e. exclusively on the on the basis of amateur games, can result to an 'intelligent' system that is able to play moves (from time to time) that are far beyond the amateur level from which it started to learn from in the first place.
In other words: if AlphaGo will be able to beat Lee Sedol on the basis of amateur level games only (players that by no means would be able to win from Lee Sedol, not even with three stones handicap), wouldn't that prove that AlphaGo by design and training would have learned --by itself-- new, top-level pro moves and patterns that were absolutely absent and unknown in the original data from which it learned from? That AlphaGo consistently would have developed a playing strength that is demonstrably much stronger than any of the amateurs from whom these games stem? That really would be an incredible breakthrough in AI worldwide and irrefutable proof that deep learning models can become --better-- than the data you feed them (as opposed to the generally accepted idea that deep learning models --at best-- can be as good as the data you feed them).
The match over five formal games will be played at the Four Seasons Hotel in Seoul, South Korea (games will start at 13h local time: 04h GMT, with rest days on March 11th and 14th). There will be played according to chinese rules (19x19, 7.5 komi) and thinking time will be 2 hours each (plus three periods of 60-second byoyomi). Each game is expected to take around 4-5 hours. The winner of the match receives $ 1M price money. If AlphaGo will be the winner, the prize money will be donated to charities including Unicef. In any case, Lee Sedol will receive at least $150,000 for participating in all the five games, and an additional $20,000 for each win.
Hassabis explained about the match: “Go is the most profound game that mankind has ever devised. The elegantly simple rules lead to beautiful complexity. Go is a game primarily about intuition and feel rather than brute calculation which is what it makes it so hard for computers to play the game well. Working out who is winning in Go is very hard. A stone’s value comes only from its location relative to the other stones on the board, which changes with every move.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on. There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them. We are honored and excited to be playing this challenge match against Lee Sedol, a true legend of the game, and whether who wins or lose, we hope that the match will inspire new interest in Go from around the world.”
Park Chimoon, Vice Chairman of the Korean Baduk Association (KBA) said: “The whole world is interested in this event as this is the first stage where humans and computers are competing in intelligence. I am proud that this historical stage is baduk (Go). I hope Lee Sedol will win this time in order to prove humans’ remarkable intelligence and preserve the mysteries of baduk.”
The match will be live streamed on DeepMind's YouTube channel as well as broadcasted on TV throughout Asia through Korea's Baduk TV, as well as in China, Japan, and elsewhere. Among others, Match commentators will include Michael Redmond, the only professional Western Go player to achieve 9p status with over 500 professional wins under his belt, who will commentate in English, and Yoo Changhyuk (9p), Kim Sungryong (9p), Song Taegon (9p), and Lee Hyunwook (8p) will commentate in Korean alternately.
Hassabis stated that "if AlphaGo will win the match against legendary Lee Sedol, I believe that this would mean AlphaGo is better in playing Go than anyone in the world". Lee Sedol said in a first statement he is etremeley joyed and excited to take on the challenge: "I am privileged to be the one to play, but I am confident I can win".
Depending on possible further improvements and development of AlphaGo during the past few months (apart from possible adaptations in AlphaGo specifically focused on Lee Sedol's way of playing) and the enormous processing power that will be used by the DeepMind team during the match, AlphaGo's strength probably will approach that of top players like Lee Sedol.
Therefore, chances are ultra high that this will be an extremely exciting, thrilling, nerve-racking, exhausting and inspiring match. If AlphaGo will win this match against Lee Sedol, this undoubtedly will be orders of magnitude more sensational and spectacular than it was when AlphaGo won against Fan Hui (or at the time Deep Blue defeated Kasparov).
[Part 2: AlphaGo under a Magnifying Glass]
[Part 2: AlphaGo under a Magnifying Glass]
Overview article of the historic match of Deep Learning AlphaGo against Lee Sedol. This part is a brief introduction and deals with the background details of the match.
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