Enjoy AI Programming (1 Book 3)

Artificial Intelligence

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Without the ability to build one skill on another, AIs will never learn like people, or be flexible enough to master fresh problems the way humans can.

To build the new AI, the researchers drew on studies from neuroscience which show that animals learn continually by preserving brain connections that are known to be important for skills learned in the past. The lessons learned in hiding from prey are crucial for survival, and mice would not last long if the know-how was erased by the skills needed to find food.

1. The History of AI

The DeepMind AI mirrors the learning brain in a simple way. Before it moves from one task to another, it works out which connections in its neural network have been the most important for the tasks it has learned so far.

It then makes these harder to change as it learns the next skill. The researchers put the AI through its paces by letting it play 10 classic Atari games, including Breakout, Space Invaders and Defender, in random order. They found that after several days on each game, the AI was as good as a human player at typically seven of the games.

Without the new memory consolidation approach, the AI barely learned to play one of them. In watching the AI at play, the scientists noticed some interesting strategies. For instance, when it played Enduro, a car racing game that takes place through the daytime, at night, and in snowy conditions, the AI treated each as a different task.

Writing in the journal, Proceedings of the National Academy of Sciences , the researchers describe how the new AI solved problems with skills it had learned in the past. But it is not clear whether drawing on past skills made the AI perform better. While the program learned to play different games, it did not master each one as well as a dedicated AI would have.

See a Problem?

One reason the AI did not nail each game was that it sometimes failed to appreciate how important certain connections were for its playing strategy. That is still a way off. This research is an early step in that direction, and could in time help us build problem-solving systems that can learn more flexibly and efficiently.

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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. Toby Manning, the match referee for AlphaGo vs. Fan Hui, has described the program's style as "conservative". It likes to use shoulder hits , especially if the opponent is over concentrated.

AlphaGo's March victory was a major milestone in artificial intelligence research. 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. When compared with Deep Blue or with 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.

As noted by entrepreneur Guy Suter, AlphaGo itself only knows how to play Go, and doesn't possess general purpose intelligence: On the contrary, this raises hopes in many domains such as health and space exploration. In China, AlphaGo was a " Sputnik moment " which helped convince the Chinese government to prioritize and dramatically increase funding for artificial intelligence. Go is a popular game in China, Japan and Korea, and the matches were watched by perhaps a hundred million people worldwide.

AlphaGo seems to have totally original moves it creates itself. Many people drank alcohol. China's Ke Jie , an year-old generally recognized as the world's best Go player at the time, [30] [84] initially claimed that he would be able to beat AlphaGo, but declined to play against it for fear that it would "copy my style". 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.

After game two, Lee said he felt "speechless": It was AlphaGo's total victory. Facebook has also been working on its own Go-playing system darkforest , also based on combining machine learning and Monte Carlo tree search.

DeepZenGo , a system developed with support from video-sharing website Dwango and the University of Tokyo , lost 2—1 in November to Go master Cho Chikun , who holds the record for the largest number of Go title wins in Japan. A paper in Nature cited AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules. AlphaGo Master white v.

Creating AI Using Python Is Easier Than You Think

Tang Weixing 31 December , AlphaGo won by resignation. White 36 was widely praised. The AlphaGo documentary film [94] [95] raised hopes that Lee Sedol and Fan Hui would have benefitted from their experience of playing AlphaGo, but as of May their ratings were little changed; Lee Sedol was ranked 11th in the world, and Fan Hui th. From Wikipedia, the free encyclopedia. AlphaGo versus Fan Hui. AlphaGo versus Lee Sedol. Future of Go Summit.

AlphaGo versus Ke Jie.

AlphaGo Zero and AlphaZero. Retrieved 17 March Mastering the ancient game of Go with Machine Learning". Lee Sedol vs AlphaGo". Retrieved 9 December Retrieved 29 December Retrieved 10 December Retrieved 28 January Archived from the original on 1 February Archived from the original on 24 March Retrieved 27 March Retrieved 18 March Retrieved 29 January Retrieved 1 February Le Monde in French.

A.I. Love You, Vol. 01

Retrieved 15 February Retrieved 23 February Retrieved 7 February Retrieved 24 February Retrieved 22 February Retrieved 14 March — via Twitter. Retrieved 19 November Retrieved 15 March Archived from the original on 18 March Retrieved 9 March Retrieved 10 March Retrieved 8 July Demis Hassabis's Twitter account. Retrieved 4 January Retrieved 6 January Retrieved 11 December Retrieved 19 October Retrieved 13 December Retrieved 11 July Retrieved 26 June Google Cloud Platform Blog.

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