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DeepMind's AI is helping to re-write the rules of chess

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In the game between chess and artificial intelligence, Google DeepMind's researchers have made yet another move, this time teaming up with ex-chess world champion Vladimir Kramnik to design and trial new AI-infused variants of the game. With the objective of improving the design of balanced sets of game rules, the research team set out to discover the best tweaks they could possibly give to the centuries-old board game, in an ambitious effort to refresh chess dynamics thanks to AI. The scientists used AlphaZero, an adaptive learning system that can teach itself new rules from scratch and achieve superhuman levels of play, to test the outcomes of nine different chess variants that they pre-defined with Kramnik's help. For each variant, AlphaZero played tens of thousands of games against itself, analyzing every possible move for any given chessboard condition, and generating new strategies and gameplay patterns. Kramnik and the researchers then assessed what games between human players might look like if these variants were adopted, to find out whether different sets of rules might improve the game.


The AI-Improved CX

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In 1990, a fellow chess player and I attended one of Gary Kasparov's several matches in New York City. Kasparov had become the youngest ever World Chess Champion in 1985, at the age of 22. In 1997, Kasparov also became the first World Chess Champion to be defeated by a computer, losing to IBM's Deep Blue in a stunning six-game match. Computers have become so good at chess that phones are now banned at chess tournaments, and players are very carefully monitored for any sort of digital activity or connection. Today, however, there is a new kind of chess becoming competitively dominant. Called "Advanced Chess," or more colloquially "Centaur Chess," it involves teaming human beings with computers, and it has been actively supported by Kasparov.


Is AI an Existential Threat?

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When discussing Artificial Intelligence (AI), a common debate is whether AI is an existential threat. The answer requires understanding the technology behind Machine Learning (ML), and recognizing that humans have the tendency to anthropomorphize. We will explore two different types of AI, Artificial Narrow Intelligence (ANI) which is available now and is cause for concern, and the threat which is most commonly associated with apocalyptic renditions of AI which is Artificial General Intelligence (AGI). To understand what ANI is you simply need to understand that every single AI application that is currently available is a form of ANI. These are fields of AI which have a narrow field of specialty, for example autonomous vehicles use AI which is designed with the sole purpose of moving a vehicle from point A to B. Another type of ANI might be a chess program which is optimized to play chess, and even if the chess program continuously improves itself by using reinforcement learning, the chess program will never be able to operate an autonomous vehicle.


Richard Feynman on Artificial General Intelligence

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In a lecture held by Nobel Laureate Richard Feynman (1918–1988) on September 26th, 1985, the question of artificial general intelligence (also known as "strong-AI") comes up. Do you think there will ever be a machine that will think like human beings and be more intelligent than human beings? Below is a structured transcript of Feynman's verbatim response. With the advent of machine learning via artificial neural nets, it's fascinating to hear Feynman's thoughts on the subject and just how close he gets, even 35 years ago. Estimated reading time is 8 minutes.


Creating A Chess AI using Deep Learning

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When Gary Kasparov was dethroned by IBM's Deep Blue chess algorithm, the algorithm did not use Machine Learning, or at least in the way that we define Machine Learning today. This article aims to use Neural Networks to create a successful chess AI, by using Neural Networks, a newer form of machine learning algorithms. Using a chess dataset with over 20,000 instances (contact at victorwtsim@gmail.com for dataset), the Neural Network should output a move, when given a chess-board. These libraries are the prerequisites to create the program: os and pandas are to access the dataset, python-chess is an "instant" chess-board to test the neural network. Numpy is necessary to perform matrix manipulation.


DeepMind's AI is helping to re-write the rules of the chess

ZDNet

In the game between chess and artificial intelligence, Google DeepMind's researchers have made yet another move, this time teaming up with ex-chess world champion Vladimir Kramnik to design and trial new AI-infused variants of the game. With the objective of improving the design of balanced sets of game rules, the research team set out to discover the best tweaks they could possibly give to the centuries-old board game, in an ambitious effort to refresh chess dynamics thanks to AI. The scientists used AlphaZero, an adaptive learning system that can teach itself new rules from scratch and achieve superhuman levels of play, to test the outcomes of nine different chess variants that they pre-defined with Kramnik's help. For each variant, AlphaZero played tens of thousands of games against itself, analyzing every possible move for any given chessboard condition, and generating new strategies and game-play patterns. Kramnik and the researchers then assessed what games between human players might look like if these variants were adopted, to find out whether different sets of rules might improve the game.


Retired Chess Grandmaster, AlphaZero AI Reinvent Chess

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Russian chess grandmaster Vladimir Kramnik is working with DeepMind's chess program, AlphaZero, to analyze new variants in an attempt to reinvent the popular strategy board game. Vladimir Kramnik, Classical World Chess Champion from 2000 to 2006, has proposed nine new chess variants last Wednesday, September 9. He has worked together with the artificial intelligence (AI) laboratory DeepMind, a subsidiary of Google's parent company Alphabet Inc, to evaluate his proposals with the help of the AlphaZero AI. In a report submitted to Cornell University, Nenad Tomašev, Ulrich Paquet, and Demis Hassabis from DeepMind worked with grandmaster Vladimir Kramnik to assess game balance in the new variations with help from AlphaZero. It defines AlphaZero as "a reinforcement learning system that can learn near-optimal strategies for any rules set from scratch without any human supervision, and provides an in silico alternative for game balance assessment."


AI ruined chess. Now it's making the game beautiful again

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Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.


Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess

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It is non-trivial to design engaging and balanced sets of game rules. Modern chess has evolved over centuries, but without a similar recourse to history, the consequences of rule changes to game dynamics are difficult to predict. AlphaZero provides an alternative in silico means of game balance assessment. It is a system that can learn near-optimal strategies for any rule set from scratch, without any human supervision, by continually learning from its own experience. In this study we use AlphaZero to creatively explore and design new chess variants.


AI Ruined Chess. Now, It's Making the Game Beautiful Again

#artificialintelligence

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.