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Chess


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.


Creating A Chess AI using Deep Learning

#artificialintelligence

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.


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.


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.


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

WIRED

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.


An Algorithm for Automatically Updating a Forsyth-Edwards Notation String Without an Array Board Representation

arXiv.org Artificial Intelligence

We present an algorithm that correctly updates the Forsyth-Edwards Notation (FEN) chessboard character string after any move is made without the need for an intermediary array representation of the board. In particular, this relates to software that have to do with chess, certain chess variants and possibly even similar board games with comparable position representation. Even when performance may be equal or inferior to using arrays, the algorithm still provides an accurate and viable alternative to accomplishing the same thing, or when there may be a need for additional or side processing in conjunction with arrays. Furthermore, the end result (i.e. an updated FEN string) is immediately ready for export to any other internal module or external program, unlike with an intermediary array which needs to be first converted into a FEN string for export purposes. The algorithm is especially useful when there are no existing array-based modules to represent a visual board as it can do without them entirely. We provide examples that demonstrate the correctness of the algorithm given a variety of positions involving castling, en passant and pawn promotion.


Computer chess: how the ancient game revolutionised AI

The Guardian

Tue 19 May 2020 06.14 EDT Last modified on Tue 19 May 2020 06.16 EDT When legendary chess grandmaster Garry Kasparov found himself beaten by IBM's Deep Blue supercomputer, it was seen as a seminal moment in the evolution of artificial intelligence. A road trodden by war heroes and student researchers alike, whose singular desire to create a program that could beat the very best in the world would shape an entire science. Early origins Chess lends itself well to computer programming. Where other games can depend more on gut instinct or physical skill, chess is a game of strict binary rules – a move is either correct or it isn't. It's a game where multiple permutations, strategies and responses to moves and gambits could all be pre-programmed.


Humans and AI: Future Best Friends

#artificialintelligence

It is not that hard to believe, how just two decades ago Deep Blue a computer beat a chess grandmaster Gary Kasparov. AI is enhancing itself and is becoming better at numerous "human" jobs -- diagnosing disease, translating languages, providing customer service -- and it's improving fast. This is raising reasonable fears amongst workers and upcoming students. According to The Guardian, 76% of Americans fear that their job will be lost to AI. While it's speculated AI will take over 1.8 million human jobs by the year 2020, however, the technology is also expected to create a 2.3 million new kinds of jobs, many of which will involve the collaboration between humans and AI.


No, You Won't Work Alongside Robots

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In 1997, IBM's Deep Blue defeated the reigning world chess champion Garry Kasparov. The world was in shock. It seemed computers, thus far thought to be little more than glorified calculators, had finally intruded upon the human domain of imagination and creativity. The worry was in vain. Deep Blue had no capacity for ingenuity.


The Games That AI Won

#artificialintelligence

Some tasks that AI does are actually not impressive. Think about your camera recognizing and auto-focusing on faces in pictures. That technology has been around since 2001, and it doesn't tend to excite people. Well, because you can do that too, you can focus your eyes on someone's face very easily. In fact, it's so easy you don't even know how you do it.