Chess champion Garry Kasparov who was replaced by AI says most US jobs are next


Garry Kasparov dominated chess until he was beaten by an IBM supercomputer called Deep Blue in 1997. The event made "man loses to computer" headlines the world over. Kasparov recently returned to the ballroom of the New York hotel where he was defeated for a debate with AI experts. Wired's Will Knight was there for a revealing interview with perhaps the greatest human chess player the world has ever known. "I was the first knowledge worker whose job was threatened by a machine," says Kasparov, something he foresees coming for us all.

AI Is Now the Undisputed Champion of Computer Chess


It was a war of titans you likely never heard about. One year ago, two of the world's strongest and most radically different chess engines fought a pitched, 100-game battle to decide the future of computer chess. On one side was Stockfish 8. This world-champion program approaches chess like dynamite handles a boulder--with sheer force, churning through 60 million potential moves per second. Of these millions of moves, Stockfish picks what it sees as the very best one--with "best" defined by a complex, hand-tuned algorithm co-designed by computer scientists and chess grandmasters.

Defeated Chess Champ Garry Kasparov Has Made Peace With AI


Garry Kasparov is perhaps the greatest chess player in history. For almost two decades after becoming world champion in 1985, he dominated the game with a ferocious style of play and an equally ferocious swagger. Outside the chess world, however, Kasparov is best known for losing to a machine. In 1997, at the height of his powers, Kasparov was crushed and cowed by an IBM supercomputer called Deep Blue. The loss sent shock waves across the world, and seemed to herald a new era of machine mastery over man.

What Does "Artificial Intelligence" Really Mean?


Before IBM's Deep Blue computer program defeated world champion Garry Kasparov in chess in 1997, ... [ ] many AI pundits believed that machines would never possess the creativity required to rival humans at the game. Years ago, Marvin Minsky coined the phrase "suitcase words" to refer to terms that have a multitude of different meanings packed into them. He gave as examples words like consciousness, morality and creativity. "Artificial intelligence" is a suitcase word. Commentators today use the phrase to mean many different things in many different contexts.

Bootstrapping from Game Tree Search

Neural Information Processing Systems

In this paper we introduce a new algorithm for updating the parameters of a heuristic evaluation function, by updating the heuristic towards the values computed by an alpha-beta search. Our algorithm differs from previous approaches to learning from search, such as Samuels checkers player and the TD-Leaf algorithm, in two key ways. First, we update all nodes in the search tree, rather than a single node. Second, we use the outcome of a deep search, instead of the outcome of a subsequent search, as the training signal for the evaluation function. We implemented our algorithm in a chess program Meep, using a linear heuristic function.

Iterative ranking from pair-wise comparisons

Neural Information Processing Systems

The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining ranking, finding'scores' for each object (e.g. In this paper, we propose a novel iterative rank aggregation algorithm for discovering scores for objects from pairwise comparisons. The algorithm has a natural random walk interpretation over the graph of objects with edges present between two objects if they are compared; the scores turn out to be the stationary probability of this random walk.

What is the difference between supervised and unsupervised machine learning?


This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we use daily use machine learning algorithms, including AI assistants, web search and machine translation. Your social media news feed is powered by a machine learning algorithm. The recommended videos you see on YouTube and Netflix are the result of a machine learning model.

ZugZwang Academy Chess Classes in Bangalore Chess Education & Coaching


Is today's schooling preparing your child to be a creator? Fluid Intelligence is the ability to solve problems one has never faced before. We believe this is the single most important ability that will make a huge difference in the life of any child. The most important thinking skills such as decision making, problem solving and logical reasoning is what helps build fluid intelligence. We are specialists in working with children from a very young age to develop their fluid intelligence for a life long and ever lasting impact.

How Artificial Intelligence Could Help Video Gamers Create the Exact Games They Want to Play

TIME - Tech

For video game fans, the concept of artificial intelligence (AI) is just as familiar as extra lives, respawns, and end bosses. Gamers have spent decades going up against computer-controlled opponents, whether a Pong paddle trying to prevent them from scoring a point or Bowser trying to stop Mario from rescuing Princess Peach. But recent developments in AI are pushing the gaming field even further, as researchers develop algorithms that can help fans make exciting new titles on their own. The history of AI and that of gaming are inexorably intertwined. Early AI researchers saw games like chess as markers of intelligence, and thus perfect testing grounds for their work.

Why are Machine Learning Projects so Hard to Manage? - KDnuggets


I've watched lots of companies attempt to deploy machine learning -- some succeed wildly, and some fail spectacularly. One constant is that machine learning teams have a hard time setting goals and setting expectations. Is it harder to beat Kasparov at chess or pick up and physically move the chess pieces? Computers beat the world champion chess player over twenty years ago, but reliably grasping and lifting objects is still an unsolved research problem. Humans are not good at evaluating what will be hard for AI and what will be easy.