Games


Chess's New Best Player Is A Fearless, Swashbuckling Algorithm

@machinelearnbot

Chess is an antique, about 1,500 years old, according to most historians. As a result, its evolution seems essentially complete, a hoary game now largely trudging along. That's not to say that there haven't been milestones. In medieval Europe, for example, they made the squares on the board alternate black and white. In the 15th century, the queen got her modern powers.1


Predictions for Artificial Intelligence in 2018 Positive reinforcement Reinf...

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Predictions for Artificial Intelligence in 2018 Positive reinforcement Reinforcement learning takes inspiration from the ways that animals learn how certain behaviors tend to result in a positive or negative outcome. Using this approach, a computer can say, figure out how to navigate a maze by trial and error and then associate the positive outcome--exiting the maze--with the actions that led up to it. This lets a machine learn without instruction or even explicit examples. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how to play the game at an expert level.


A good year for Artificial Intelligence

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The year 2017 was a seminal year for the field of Artificial Intelligence (AI). Suddenly, everyone everywhere was talking about AI, what it means, and how it will affect human societies and economies. Jobs, warfare, healthcare, film-making, even art--no area of human enterprise seemed to be immune from discussions of the coming machine onslaught. Overall, there were three very important outcomes for the field of AI in 2017. First, technologically, the single most important breakthrough in 2017 was the development of Google Deep Mind's AlphaGo Zero.



The 3 Tricks That Made AlphaGo Zero Work – Seth Weidman – Medium

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There were many advances in Deep Learning and AI in 2017, but few generated as much publicity and interest as DeepMind's AlphaGo Zero. This program was truly a shocking breakthrough: not only did it beat the prior version of AlphaGo -- the program that beat 17 time world champion Lee Sedol just a year and a half earlier -- 100–0, it was trained without any data from real human games. Xavier Amatrain called it "more [significant] than anything…in the last 5 years" in Machine Learning. So how did DeepMind do it? In this essay, I'll try to give an intuitive idea of the techniques AlphaGo Zero used, what made them work, and what the implications for future AI research are.


Deep Learning: AlphaGo Zero Explained In One Picture

@machinelearnbot

Recently Google DeepMind announced AlphaGo Zero -- an extraordinary achievement that has shown how it is possible to train an agent to a superhuman level in the highly complex and challenging domain of Go, 'tabula rasa' -- that is, from a blank slate, with no human expert play used as training data. It thrashed the previous reincarnation 100–0, using only 4TPUs instead of 48TPUs and a single neural network instead of two. Click on the image to zoom in. To read more and access the full cheat sheet, click here.


Yearender: Silicon Valley keeps trends as Artificial Intelligence goes mainstream in 2017 - Xinhua

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SAN FRANCISCO, Dec. 31 (Xinhua) -- Artificial Intelligence (AI) has become the buzzword of 2017 as tech companies, from promising startups to big-name firms such as Google and Facebook, are snapping up the AI technology which has changed people's lives and the world. The AI technology, which has experienced a rapid development in the past decade, has outperformed human intelligence in some areas and grown into a "super human artificial intelligence." The stunning power of Artificial Intelligence shocked the whole world last year when Google's AlphaGo beat South Korea' top player, an 18-time world champion Lee Sedol in the game of Go based on a huge database and powerful algorithms. But its latest version, Alpha Go Zero, which learned to teach itself how to play Go with no human background, demonstrated an even more powerful talent by defeating 3-0 the world top-ranking player, China's Ke Jie, and a five world-champion team in another challenge game of Go in May 2017. Similarly, the AI Libratus invented by Carnegie Mellon, skinned the world's top poker players in a poker match in early 2017 by learning through self-play instead of human game data.


599

AI Magazine

Carnegie-Mellon University's Hitech chess computer scored 5-1 in the National Open Chess Championships held in Chicago March 18-20. The Championship Section in which Hitech competed, had 380 entries. The Championship Section in which Hitech competed, had 380 entries. There was a six-way tie for first with 5.5 points between: International Grandmaster Mikhail Tal (a former world champion), International Grandmaster Sergey Kudrin, FIDE Master Michael Brooks, International Master James Rizzitano, International Master Calvin Blocker, and International Grandmaster Leonid Shamkovich. Tied for seventh with 5 points were: National Master Hitech, International Grandmaster Maxim Dlugy, International Grandmaster Walter Browne, International Grandmaster Arthur Bisguier, and nine others.


Silicon Valley keeps trends as Artificial Intelligence goes mainstream

#artificialintelligence

SAN FRANCISCO, CALIFORNIA – Artificial Intelligence (AI) has become the buzzword of 2017 as tech companies, from promising startups to big-name firms such as Google and Facebook, are snapping up the AI technology which has changed people's lives and the world. The AI technology, which has experienced a rapid development in the past decade, has outperformed human intelligence in some areas and grown into a "super human artificial intelligence." The stunning power of Artificial Intelligence shocked the whole world last year when Google's AlphaGo beat South Korea' top player, an 18-time world champion Lee Sedol in the game of Go based on a huge database and powerful algorithms. But its latest version, Alpha Go Zero, which learned to teach itself how to play Go with no human background, demonstrated an even more powerful talent by defeating 3-0 the world top-ranking player, China's Ke Jie, and a five world-champion team in another challenge game of Go in May 2017. Similarly, the AI Libratus invented by Carnegie Mellon, skinned the world's top poker players in a poker match in early 2017 by learning through self-play instead of human game data.


Articles

AI Magazine

Samuel's successes included a victory by his program over a master-level player. In fact, the opponent was not a master, and Samuel himself had no illusions about his program's strength. This single event, a milestone in AI, was magnified out of proportion by the media and helped to create the impression that checkers was a solved game. Nevertheless, his work stands as a major achievement in machine learning and AI. Since 1950, the checkers world has been dominated by Tinsley.