On the other side was a new program called AlphaZero (the "zero" meaning no human knowledge in the loop), a chess engine in some ways very much weaker than Stockfish--powering through just 1/100th as many moves per second as its opponent. The AI engine won the match (winning 28 games and drawing the rest) with dazzling sacrifices, risky moves, and a beautiful style that was completely new to the world of computer chess. British chess grandmaster Matthew Sadler and mathematician and chessmaster Natasha Regan are still piecing together how AlphaZero's strategy works in their new book, Game Changer. We're breaking open two moves in just one of the games to show the aggressive style, what it does, and what humans can learn from our new chess champion. By move 42, AlphaZero has sacrificed even more pawns, and is marching another poor, disposable sucker toward oblivion.

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.

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters, in the long run, is the leveraging of computation. These two need not run counter to each other, but in practice, they tend to.

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. It was New York, 1997 and for the first time ever a computer had beaten a world champion under tournament conditions. This was the culmination of a journey in which the first stirrings of what we now call artificial intelligence and machine learning were born. 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.

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.

In 1997 IBM Supercomputer Deep Blue defeated world chess champion Garry Kasparov by a four games to two score in a six game series. This was a landmark moment in the development of what we might call "thinking machines", as a computer had proven itself better than the best human in what was then the world's most prestigious strategy game. The benchmark of a machine defeating a human at chess has mattered for hundreds of years. Famously, the mechanical Turk developed in 1770 thrilled and confounded luminaries as notable as Napoleon Bonaparte and Benjamin Franklin. Simply an elaborate hoax, the human-powered, not-quite-automaton fooled the public for almost 100 years.

U of Alberta created the first Computing Science department in Canada in 1964. It has a long tradition of research in AI (is rated 3rd in the world in machine learning). It has also led in the development of AI for strategy games. The results can be commercialized in non-game applications as well. Among these are Checkers, Chess, Go and Poker, The evening's talks were by Jonathan Schaeffer (computer chess) and Ryan Hayward (the strategy game Hex).

Chess is a complicated game. It's a game of strategy between two opponents, but with no hidden information and all of the potential moves known by both players at the outset. With each turn, players communicate their intent and try to anticipate the possible countermoves. The ability to envision several moves in advance is a recipe for victory, and one that mathematicians and logicians have long found intriguing. Despite some early mechanical chess-playing machines--and at least one chess-playing hoax--mechanized chess play remained hypothetical until the advent of digital computing.

With interest soaring in machine learning and its role in all kinds of games, chess will be in the spotlight at the prestigious MIT Sloan Sports Analytics Conference this year, organizers announced today. The chess program is scheduled for Saturday, March 2. Chess.com's The panel places chess at the famous Sloan conference, which has deeply influenced the landscape of sports and social science analytics in recent years. The session is called Chess AI Transformation: How Self Learning AI Taught Chess Computers (and Humans) a Lesson. "The game of chess continues to act as a barometer for the leading edge of artificial intelligence, and [...] artificial intelligence continues to fundamentally transform the game at the highest levels," according to the conference promotional materials.

This article is reproduced with kind permission of Spiegel Online, where it first appeared. The author was told to make the series personal, describe the development of chess programming not as an academic treatise but as a personal story of how he had experienced it. For some ChessBase readers a number of the passages will be familiar, since the stories have been told before on our pages. For others this can serve as a roadmap through one of the great scientific endeavors of our time. It was the mid 1990s.