Chess is one of the world's most popular games. Its popularity and complexity make it an interesting research domain for artificial intelligence. The number of board positions we can get to from the initial board state is larger than the number of atoms in the universe! Chess playing machines have been the subject of human interest for hundreds of years, but only on the last few decades have they been able to compete with (and beat) the world champions. Chess programs now have their own tournaments.
IBM's Deep Blue wasn't supposed to defeat Chess grandmaster Gary Kasparov when the two of them had their 1997 rematch. Computer experts of the time said machines would never beat us at strategy games because human ingenuity would always triumph over brute-force analysis. After Kasparov's loss, the experts didn't miss a beat. They said Chess was too easy and postulated that machines would never beat us at Go. Champion Lee Sedol's loss against DeepMind's AlphaGo proved them wrong there. Then the experts said AI would never beat us at games where strategy could be overcome by human creativity, such as poker.
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.
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.
Most chess computers play a purely mathematical strategy in a game yet to be solved. They are raw calculators and look like it too. AlphaZero, at least in style, appears to play every bit like a human. It makes long-term positional plays as if it can visualize the board; spectacular piece sacrifices that no computer could ever possibly pull off, and exploitative exchanges that would make a computer, if it were able, cringe with complexity. In short, AlphaZero is a genuine intelligence.
More than a decade has passed since the British government issued an apology to the mathematician Alan Turing. The tone of pained contrition was appropriate, given Britain's grotesquely ungracious treatment of Turing, who played a decisive role in cracking the German Enigma cipher, allowing Allied intelligence to predict where U-boats would strike and thus saving tens of thousands of lives. Unapologetic about his homosexuality, Turing had made a careless admission of an affair with a man, in the course of reporting a robbery at his home in 1952, and was arrested for an "act of gross indecency" (the same charge that had led to a jail sentence for Oscar Wilde in 1895). Turing was subsequently given a choice to serve prison time or undergo a hormone treatment meant to reverse the testosterone levels that made him desire men (so the thinking went at the time). Turing opted for the latter and, two years later, ended his life by taking a bite from an apple laced with cyanide.
Artificial intelligence (AI) has made astonishing progress in the last decade. AI can now drive cars, diagnose diseases from medical images, recommend movies, even whom you should date, make investment decisions, and create art that people have sold at auction. A lot of research today, however, focuses on teaching AI to do things the way we do them. For example, computer vision and natural language processing – two of the hottest research areas in the field – deal with building AI models that can see like humans and use language like humans. But instead of teaching computers to imitate human thought, the time has now come to let them evolve on their own, so instead of becoming like us, they have a chance to become better than us.
Many prominent thinkers have warned of the risks inherent in AI. However, they also point to its vast potential to free us from mundane tasks, to gain deeper insights and to boost productivity. There's no doubt that AI has huge potential to improve our everyday lives – at least before the robots take over. Because it already has, and for decades. We can thank AI for Deep Blue, the first computer chess playing program, for driverless cars and, speaking personally, for Microsoft email spam filters.
A workshop held in 1956 at Dartmouth College, Hanover, NH, is usually considered the beginning of artificial intelligence. Participants included John McCarthy and Marvin Minski. Alan Turing and Konrad Zuse, who already dealt with this topic in the 1940s, are also mentioned as the founders of this discipline. For decades, machine chess was considered the highlight of artificial intelligence. It was not until 1997 that IBM's Deep Blue program was able to beat then-world chess champion Garry Kasparov.
'It's not opening the gates of hell, but it's not a paradise,' Kasparov says about AI. Artificial intelligence learns from us, so we should really fear bad actors, not killer robots. Garry Kasparov was one of the first victims of the AI automation revolution. His loss to IBM's Deep Blue made him the first human chess champion to lose a match to a computer. But Kasparov is not jaded; his book, Deep Thinking, explores how AI can actually help us become more human. The real challenge, Kasparov told me at SXSW in Austin earlier this month, is keeping these tools from the humans who want to use them to do harm. And in that regard, we may already be too late. Dan Costa: After a career playing chess and battling Deep Blue, you've since then become a chess AI expert of sorts.
Reactive Machines – This AI system doesn't have its memory that is why it cannot store things. The basic example for reactive machines are Deep blue, which is the IBM Chess Program, this example is relevant here because Deep Blue can easily identify the pieces on the chessboard and can easily make the predictions about the game, but doesn't have memory which enables Deep Blue to use its past experience for informing the future ones. It can only analysis the possible moves of both the players and can choose the most strategic move. Limited Memory – This AI system has limited memory because of that, they are able to use their past experience for informing future decisions. Some of its decision-making functions are used in the self-driving car.