The last invention of humanity

Irving John Good was a British mathematician who worked with Alan Turing in the famous Hut 8 of Bletchley Park, contributing to the war effort by decrypting the messages coded by the German enigma machines. After that, he became a professor at Virginia Tech and, later in life, he was a consultant for the cult movie 2001: A Space Odyssey, by Stanley Kubrick.

Irving John Good (born Isadore Jacob Gudak to a Polish jewish family) is credited with coining the term intelligence explosion, to refer to the possibility that a super-intelligent system may, one day, be able to design an even more intelligent successor. In his own words:

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

We are still very far from being able to design an artificially intelligent (AI)  system that is smart enough to design and code even better AI systems. Our current efforts address very narrow fields, and obtain systems that do not have the general intelligence required to create the phenomenon I. J. Good was referring to. However, in some very restrict domains, we can see at work mechanisms that resemble the that very same phenomenon.

Go is a board game, very difficult to master because of the huge number of possible games and high number of possible moves at each position. Given the complexity of the game, branch and bound approaches could not be used, until recently, to derive good playing strategies. Until only a few years ago, it was believed that it would take decades to create a program that would master the game of Go, at a level comparable with the best human players.

In January 2016, DeepMind, an AI startup (which was at that time acquired by Google by a sum reported to exceed 500M dollars), reported in an article in Nature that they had managed to master the complex game of Go by using deep neural networks and a tree search engine. The system, called AlphaGo, was trained on databases of human games and eventually managed to soundly beat the best human players, becoming the best player in the world, as reported in this blog.

A couple of weeks ago, in October of 2017, DeepMind reported, in a second article in Nature, that they programmed a system, which became even more proficient at the game, that mastered the game without using any human knowledge. AlphaGo Zero did not use any human games to acquire knowledge about the game. Instead, it played millions of games (close to 30 millions, in fact, played over a period of 40 days) against another version of itself, eventually acquiring knowledge about tactics and strategies that have been slowly created by the human race for more than two millennia. By simply playing against itself, the system went from a child level (random moves) to a novice level to a world champion level. AlphaGo Zero steamrolled the original AlphaGo by 100 to 0,  showing that it is possible to obtain super-human strength without using any human generated knowledge.

In a way, the computer improved itself, by simply playing against itself until it reached perfection. Irving John Good, who died in 2009, would have liked to see this invention of mankind. Which will not be the last, yet…

Picture credits: Go board, picture taken by Hoge Rielen, available at Wikimedia Commons.

 

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It’s official: the best Go player in the world is a program, AlphaGo

Go-playing program AlphaGo, developed by Google’s DeepMind, has secured victory against 18-time world champion Lee Sedol. AlphaGo won the first three games of a five game match played in Seoul, thus securing the match.

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AlphaGo beats Lee Sedol, one the best Go players in the world

AlphaGo, the Go playing program developed by Google’s DeepMind, scored its first victory in the match against Lee Sedol.

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This win comes in the heels of AlphaGo victory over Fan Hui, the reigning 3-times European Champion,  but it has a deeper meaning, since Lee Sedol is one of the two top Go players in the world, together with Lee Changho. Go is viewed as one of the more difficult games to be mastered by computer, given the high branching factor and the inherent difficulty of position evaluation. It has been believed that computers would not master this game for many decades to come.

Ongoing coverage of the match is available in the AlphaGo website and the matches will be livestreamed on DeepMind’s YouTube channel.

AlphaGo used deep neural networks trained by a combination of supervised learning from professional games and reinforcement learning from games it played with itself. Two different networks are used, one to evaluate board positions and another one to select moves. These networks are then used inside a special purpose search algorithm.

The image shows the final position in the game, courtesy of Google’s DeepMind.

Computers finally excel at Go

 

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Go is a beautiful game, with a very large branching factor that makes it extremely hard for computers. For decades, playing this game well was outside the reach of existing programs.

We just learned that computers finally mastered Go, in a paper published in the journal Nature. By using machine learning techniques and, in particular, deep learning, the program AlphaGo, created by Google’s company DeepMind, managed to beat Fan Hui, the European Go champion, five times out of five. Whether AlphaGo is sufficiently strong to beat the best players in the world, remains to be seen. However, it already represents a very significant advance of the state of the art.

What was maybe the last bastion in table games still unconquered by computers is no more. Computers are now better than humans at all table games invented by humanity.