2/22/2011 – In 1997 Deep Blue defeated the world chess champion Garry Kasparov in a six-game match. It was considered a stunning achievement and a significant step forward in the field of artificial intelligence. Fourteen years later another IBM computer that is about 100 times faster than Deep Blue beat the best humans in an exhibition game of Jeopardy – and received world-wide attention.
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Your personal chess trainer. Your toughest opponent. Your strongest ally. FRITZ 20 is more than just a chess engine – it is a training revolution for ambitious players and professionals. Whether you are taking your first steps into the world of serious chess training, or already playing at tournament level, FRITZ 20 will help you train more efficiently, intelligently and individually than ever before.
This video course provides a comprehensive and practical White repertoire in the Ruy Lopez! Through instructive model games and in-depth theoretical explanations, you will learn how to confidently handle both main lines and sidelines.
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Facing certain defeat at the hands of a room-size I.B.M. computer on Wednesday
evening, Ken Jennings, famous for winning 74 games in a row on the TV quiz show,
acknowledged the obvious. “I, for one, welcome our new computer overlords,”
he wrote on his video screen, borrowing a line from a “Simpsons”
episode. For I.B.M., the showdown was not merely a well-publicized stunt and
a $1 million prize, but proof that the company has taken a big step toward a
world in which intelligent machines will understand and respond to humans, and
perhaps inevitably, replace some of them.
In its “Jeopardy!” project, I.B.M. researchers were tackling a
game that requires not only encyclopedic recall, but also the ability to untangle
convoluted and often opaque statements, a modicum of luck, and quick, strategic
button pressing. More than anything, the contest was a vindication for the academic
field of computer science, which began with great promise in the 1960s with
the vision of creating a thinking machine and which became the laughingstock
of Silicon Valley in the 1980s, when a series of heavily financed start-up companies
went bankrupt.
For I.B.M., the future will happen very quickly, company executives said. On
Thursday it plans to announce that it will collaborate with Columbia University
and the University of Maryland to create a physician’s assistant service
that will allow doctors to query a cybernetic assistant. The company also plans
to work with Nuance Communications Inc. to add voice recognition to the physician’s
assistant, possibly making the service available in as little as 18 months.
I.B.M. executives also said they are in discussions with a major consumer electronics
retailer to develop a version of Watson, named after I.B.M.’s founder,
Thomas J. Watson, that would be able to interact with consumers on a variety
of subjects like buying decisions and technical support.
First Came the Machine That Defeated a Chess Champion
Before there was Watson, there was Deep Blue
In 1997, Deep Blue, another computer built by I.B.M., defeated the world chess
champion, Garry Kasparov, in a six-game match. At the time, it was considered
a stunning achievement and a significant step forward in the field of artificial
intelligence. Some people said that a new era would be ushered in, one in which
computers would perform many tasks — like air traffic control —
that it once seemed only humans could do. That era has not quite materialized.
But almost 14 years later, chess programs running on an average desktop computer
can play better than Deep Blue, making its victory no longer seem as implausible.
And while the research that went into making Deep Blue has not itself transformed
society, the lessons from designing and building it have had practical applications,
said Murray S. Campbell, one of Deep Blue’s developers, who still works
at I.B.M.
Playing chess was a “deep computing” problem, Dr. Campbell said,
the kind that involves processing and analyzing large amounts of data. Based
on what it learned from Deep Blue, I.B.M. created a Deep Computing Institute
to analyze and solve complex computing problems, like those posed by credit
card transactions, telephone call centers and weather analysis.
What the researchers who created Deep Blue did not even try to do was to develop
a system that could really identify, parse and mimic human behavior. “We
didn’t know what to do with language and speech and vision and so on,”
Dr. Campbell said. “It was so hard. Where Deep Blue left off is where
Watson picks up. “Watson is finally tackling these real world issues,”
Dr. Campbell said. “It is tackling it head-on and not trying to avoid
it.”
Part of what makes that possible is that Watson is much more powerful than
Deep Blue. It has 2,800 microprocessors; Deep Blue had 30 general-purpose processors
and 480 custom ones. Watson can calculate up to 80 teraflops, or 80 trillion
operations, a second, which is about 100 times faster than Deep Blue.
Still, what Watson does is not all that dissimilar from what Deep Blue did,
Dr. Campbell said. “We don’t claim that Watson is thinking in the
way people think. It is working on a computational problem at its core.”
Deep Blue never played again. Part of it is now in the Smithsonian. “We’d
accomplished what we set out to do,” Dr. Campbell said.
In this video course experts examine the games of Bent Larsen. Let them show you which openings Larsen chose, where his strength in middlegames were, how he outplayed his opponents in the endgame & you’ll get a glimpse of his tactical abilities!
From the 2026 Candidates Tournament, featuring a video review by Dorian Rogozenco, to Jan Werle’s opening video on the French Tarrasch Defence, and Oliver Reeh’s tactical column ‘Top Grandmasters at Work’. Analyses by Giri, So, Wei Yi and many others.
You will learn how Black's dynamic piece activity and structural counterplay more than compensate for White's extra tempo in the colour-reversed setups.
In this course, you’ll learn how to take the initiative against the London and prevent White from comfortably playing their usual system by playing 1.d4 Nf6 2.Bf4 Nh5.
London System Powerbase 2026 is a database and contains in all 11 285 games from Mega 2026 and the Correspondence Database 2026, of which 282 are annotated.
The London System Powerbook 2026 is based on more than 410 000 games or game fragments from different opening moves and ECO codes; what they all have in common is that White plays d4 and Bf4 but does not play c4.
In this course, Grandmaster Elisabeth Pähtz presents the London System, a structured and ambitious approach based on the immediate Bf4, leading to rich and dynamic positions.
€59.90
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