Is Deep Blue the same computer from last year?
Yes and no. Deep Blue is technically the same machine as last year - hence it retains the name "Deep Blue." But this year's model has been significantly enhanced "under the hood" to make it an even better chess player.

What are the differences between last year's rendition of Deep Blue and this year's model?
There are two main differences between the Deep Blue of last year and the current version. First of all, this year Deep Blue will be running on a faster system - the latest version of the SP - which uses 30 P2SC or Power Two Super Chip processors. As a result, it will run about twice as fast as last year's system. And as Murray Campbell says, "...in chess programs, speed is very important. The faster you are, the stronger you play."

Secondly, Deep Blue's "chess knowledge" has improved since last year. Working with international grandmaster Joel Benjamin, the development team has spent the past several months educating Deep Blue about some of the finer points of the game.

Last year, Deep Blue averaged about 100 million chess positions per second. This means it examined and evaluated 100 million different chess positions every second. This year, the developers estimate that Deep Blue will work about twice as quickly - that is, 200 million chess positions per second.

Incidentally, Garry Kasparov can evaluate approximately three positions per second.

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Has this year's new and improved version of Deep Blue played against the version from last year?
Yes. Although Kasparov might not be too excited about the results, the current version of Deep Blue has dominated last year's model in several test games staged by the development team.

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Why would IBM spend millions of dollars and five years building the world's most powerful chess playing computer?
Massively parallel, special-purpose computing like that found in Deep Blue could certainly be of great use to people if applied to finance, medicine, education, etc. Imagine an evaluative capability like Deep Blue's that could help an investor manage a portfolio, a huge retailer manage inventory, or a government deploy resources. These types of things justify the spending behind Deep Blue. Chess and Kasparov are merely ways of benchmarking progress.

Says IBM research scientist Murray Campbell, "Well, we're taking some of the lessons we learned from building this system and applying it to other complex and difficult problems that require a tremendous amount of computational power. The computer that we've brought [to the event], Deep Blue, is capable of doing extraordinary amounts of computation in order to choose a good chess move. And, applying and creating a system for other problems would be the ultimate goal of a system like this.

"For example, we're looking at developing a system that can accelerate the molecular dynamics problem, that is the interaction between atoms and molecules, in order to predict the behavior of those molecules. For example, the pharmaceutical companies would use this in synthesizing drugs, predicting the behavior of drugs, before they even have to go to the trouble of actually synthesizing them and testing them. They can predict some of their properties beforehand. And accelerating that process would be a very valuable addition to the technology of today."

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But why chess?
Computers have been made to play many other games besides chess, but none of these are nearly as interesting from a research point of view. There's no scientific interest in pursuing games of chance, like roulette or backgammon, since "meaning" is reduced to the vicissitudes of the wheel or dice. Among strategy games, things like checkers or tic-tac-toe are at a lower level than chess. They are purely tactical games that can easily be co-opted by a computer program. The ancient Oriental game Go is much more difficult than chess for a computer to play well. The very best Go programs written have been able to play only a very mediocre game.

With its 64 squares and limited patterns of movement, chess isn't terribly complicated from a mathematical perspective. A computer's ability to calculate makes it relatively easy to write a program that will play a decent game of chess. There are plenty of these, and most of them are adequate enough to beat a vast majority of the world's players, since they are prone to oversights and blunders.

Playing at the grandmaster level, things start getting interesting for the programmers. Grandmasters' ingenuity at confounding machines is a challenge. Today, only a tiny group of people remains who can pose serious problems for Deep Blue. Of these, Garry Kasparov is supreme.

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Does Deep Blue use artificial intelligence?
The short answer is "no." Earlier computer designs that tried to mimic human thinking weren't very good at it. No formula exists for intuition. So Deep Blue's designers have gone "back to the future." Deep Blue relies more on computational power and a simpler search and evaluation function.

The long answer is "no." "Artificial Intelligence" is more successful in science fiction than it is here on earth, and you don't have to be Isaac Asimov to know why it's hard to design a machine to mimic a process we don't understand very well to begin with. How we think is a question without an answer. Deep Blue could never be a HAL-2000 if it tried. Nor would it occur to Deep Blue to "try."

"If you go back to HAL in 1968," says Deep Blue development team member Joe Hoane, "2001 came out and a lot of people were introduced to the idea that well, you could have a relationship with a computer. HAL in the movie had a personality and, in 1968, people started to realize that computers are getting interesting, that maybe we've reached another milestone where computers are getting really interesting... solving really interesting problems that we couldn't otherwise solve."

Deep Blue's strengths are the strengths of a machine. It has more chess information to work with than most computers and all but a few chess masters. It never forgets or gets distracted. And its orders of magnitude are better at processing the information at hand than anything yet devised for the purpose.

"There is no psychology at work" in Deep Blue, says IBM research scientist Murray Campbell. Nor does Deep Blue "learn" its opponent as it plays. Instead, it operates much like a turbocharged "expert system," drawing on vast resources of stored information (For example, a database of opening games played by grandmasters over the last 100 years) and then calculating the most appropriate response to an opponent's move. Deep Blue is stunningly effective at solving chess problems, but it is less "intelligent" than the stupidest person. It doesn't think, it reacts. And that's where Garry Kasparov sees his advantage. Speaking of an earlier IBM chess computer, which he defeated in 1989, Kasparov said, "Chess gives us a chance to compare brute force with our abilities."

Deep Blue applies brute force aplenty, but the "intelligence" is the old-fashioned kind. Think about the 100 years of grandmaster games. Kasparov isn't playing a computer, he's playing the ghosts of grandmasters past. That Deep Blue can organize such a storehouse of knowledge -- and apply it on the fly to the ever-changing complexities on the chessboard -- is what makes this particular heap of silicon an arrow pointing to the future.

The worlds of science and enterprise are full of problems with so many variables they can't be solved in real time. A system like Deep Blue that can accelerate solutions by powers of 10 is going to make a difference far beyond the chessboard. (And P.S. - That so much of Deep Blue's innards are "general-purpose" industry-standard hardware is good news to any organization faced with a 7-figure problem on a 6-figure budget.)

The way that the PowerPC chips inside Deep Blue work in parallel to break down and solve a chess-board problem is a pretty good analog for the way many scientists, working independently, advance our total understanding of the universe, or genetics...

Or the way business people confront the complexities of, say, running an airline. Figuring THE best way to schedule 570 planes of 25 different types to 150 destinations for best passenger revenue and most efficient fueling, maintenance, crew deployment, and turnaround servicing is a towering problem. On that scale, the difference between a pretty good solution and the best solution is measured in billions.

The shifting complexities of the chessboard are the airline problem in miniature. For computer scientists, chess is a laboratory benchmark. Back in computing's Jurassic age, in 1950, Claude Shannon, the chief architect of information theory, put it this way: "The chess-playing problem is sharply defined, both in the allowed operations and in the ultimate goal. It is neither so simple as to be trivial, nor too difficult for satisfactory solution."

Satisfactory solutions - to problems far beyond the chessboard - are closer than ever before as a result of the research that has gone into the Deep Blue system. And who knows? As more possibilities open before us, some of those science fiction predictions may come true. But it won't be because of any artificial intelligence. It will be because systems like Deep Blue helped us make better use of the real thing.

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Why is it so difficult for Deep Blue to beat Kasparov when computers can calculate the moves so blindingly fast?
Chess is a simple game, but not as easy as it looks. Most chess devotees think of it as an art or a sport, with certain unquantifiable attributes. The majority of grandmasters believe that Kasparov will not be beaten this year by Deep Blue, and many believe that he will never be beaten by a computer.

Although chess has a finite number of possible outcomes that the computer must analyze, there are subtleties that do not easily subject themselves to objective analysis. Material is easy to evaluate, but what happens when a human player offers a gambit? In evaluating whether material gain makes up for a possible loss of positional strength, the computer is no longer comparing apples to apples. Sophisticated programs like Deep Blue must have ways of evaluating gambits, and declining them if necessary. In the past, offering gambits, directing the game into positions with maximum subtlety, and other such strategies have allowed top players like Kasparov to beat computers. Last year, Deep Blue showed an ability to correctly detect gambits but faltered when Kasparov changed strategies mid-game. The Deep Blue development team suggests that this strategy will be adequately defensed by the computer. But Kasparov may be planning other ingenious tactics for this year's match that Deep Blue will be unable to handle.

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How does Deep Blue "think" about chess?
There are four basic chess values that Deep Blue must consider before deciding on a move. They are material, position, King safety and tempo.

Material is easy. The rule of thumb is that if a pawn is considered to be worth a value of 1, pieces (knights and bishops) are worth 3 each, a rook is worth 5, and the Queen 9. The King, of course, is beyond value, since his loss means the end of the game. This varies slightly in certain situations -- retaining the Bishop pair in the end game generally increases their value beyond 6, for example - but the laws of material are fairly constant.

Position is more complex. In the old days, it was thought that control of the center was all that mattered. Nearly all grandmaster games before the 20th century began with Pawn to King 4 or Pawn to Queen 4. Control of the center is still important, but certain grandmasters in this century found some effective "hypermodern" openings that delay development of the center, with the idea that the opponent will overextend his position and leave himself vulnerable for attack.

The simplest way to understand position is by looking at your pieces and counting the number of safe squares that they can attack. The more squares they control, the stronger the position. Thus, a seemingly quiet pawn move can be very strong if it opens many new squares for a more powerful piece behind it.

The defensive aspect of position is the safety of the King. This is self-explanatory. A computer must assign a value to the safety of the King's position in order to know how to make a purely defensive move.

Tempo is related to position but focuses on the race to develop control of the board. A player is said to "lose a tempo" if he dillydallies while the opponent is making more productive advances.

The programmers have defined how Deep Blue's program evaluates these factors. The computer then searches through all the legal moves and chooses the one that yields the highest value.

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Does Deep Blue use psychology?
Psychology, any top chess player will tell you, is an important key to winning chess. But Deep Blue has no psychological perception, can neither intimidate nor be intimidated, and experiences no joy from winning or sadness from losing.

This is the key difference between a chess computer and a person. Deep Blue will not look over the board and see the glare of the world champion. Kasparov can growl, sneer, call Deep Blue names and engage in any intimidation tactic he wants, and the computer will go right on crunching data in the same impersonal way.

One interesting aspect of Deep Blue that could be said to have psychological residue is its program for using the clock. Given a total of 3.5 hours to make all its moves, it can ration time in a variety of ways. It can average the number of moves and attempt to deviate from that only by a small margin. Or it can move very fast, forcing Kasparov to respond. Or it can take an inordinate amount of time over one move, calculating many trillions of possible games, forcing Kasparov to wait and possibly become bored or agitated.

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Does it factor in Kasparov's tendencies?
No one (except the members of the Deep Blue research team) knows about the types of improvements Deep Blue has undergone since last year's match. Kasparov beat Deep Blue last year by employing his signature ability to switch strategies mid-game. The developers have hinted that Kasparov will not be successful in this year's match if he chooses to utilize the same strategy.

Traditionally, in man-machine games, computers have failed to pick up the nuance of the competitor's favorite opening, end game or a trademarked stratagem. One remarkable game between Deep Thought and Karpov could have been a draw forced by the computer, but the machine thought it saw a win based on material advantage and ended up losing to the number two player in the world. It should have taken the draw, which at the time would have been the highest achievement ever by a computer.

It's a given that Deep Blue has in its memory the moves that Kasparov played in the many past games, including last year's match. Further, it has the ability to evaluate these games the same way a human player would, looking for roads not taken and possible mistakes. But, unlike a human, it doesn't anticipate Kasparov's tendencies; rather, it simply reacts to what happens on the board. Deep Blue's method is simple: It looks at the position and makes the move it deems best, without factoring in any perceived tendency.

In the past, computers' lack of ability to understand a grandmaster's tendencies favored the human. Kasparov, if he follows his past pattern against Deep Blue, will again attempt to use this to his advantage. He will try to steer the computer into end games with which he is intimately familiar, especially end games that confound the computer into "thinking" it is ahead, even when it's actually behind.

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Why is chess, along with music and mathematics, one of the intellectual endeavors where children with little experience can excel?
Chess is a discipline that does not require many fundamental building blocks. Like a young Mozart in music or a nine-year-old who solves graduate school problems in trigonometry, a young chess player can often achieve truly remarkable feats with little experience. There's still plenty of mystery about what makes a gifted child, but much work has been done in the types of pattern recognition that are behind a chess prodigy's ability. As chess people note, some kids just "see the board." A child with an acute ability to see patterns need only gain an understanding of the few simple rules of chess to become a formidable player. A gifted child can look moves ahead and determine the best strategies with an effortlessness that is astounding.

Some of the best-known stories about chess prodigies concern the Cuban world champion Jose Raul Capablanca. One of these stories tells of the time the young Capablanca watched his father and a friend playing a friendly game. At one point, Jose noticed his father move a knight from one light square to another. Afterward, he told his father about the illegal move. His father dismissed his son's remark, thinking the boy didn't even know the rules. Jose Raul promptly challenged his father and beat him twice. While charming, this story may not seem out of the ordinary, except for one thing. Jose was four years old at the time!

Still, much in chess must be learned or comes with experience. Bobby Fischer, after becoming a grandmaster at age 15, would still wait over a decade before winning the championship.

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Why aren't older, more experienced players at the top levels of the game? Is chess a young person's game?
In countless western movies, we've shared the anxiety of the aging gunslinger who has to face a barrage of challenges from younger, quicker rivals. Fatigue sets in, you have one bad day, and you become a notch on someone's belt.

Chess is a bit like gunfighting. It's intensely psychological and requires a rigorous training discipline. Law school graduates who study for the bar exam know what it's like to cram -- but most of the top chess players cram like that every day of their lives.

Just as tennis players and gymnasts seem to be peaking at earlier ages, so it is with chess players. In 1985, Kasparov became the youngest world champion at age 22. The days when a Botvinnik could recapture the throne at age 50 are almost surely gone.

Today, with the exception of Karpov and Kasparov, the largest concentration of talent falls between the ages of 18 to 25. Training and matches are simply more rigorous than 20 years ago, computer technology has added an additional factor, and students of the game are starting younger. Judit Polgar and Peter Leko, both from Hungary, beat Fischer's record in becoming the youngest GMs ever (at ages 15 and 14, respectively). This could signal a trend toward world champions even younger than Kasparov was.

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Why are there so few women at the top levels of chess?
There are many theories about this. One argument says that chess appeals more to boys than girls, due to the warlike comparisons it invokes. Child psychologists note that girls generally prefer to play games where cooperation, not domination, is the goal. At the higher levels of chess, the demands of the game nearly preclude any social life at all, and marriage and domesticity of the classical kind are pretty much out of the question.

Another argument says that women cannot achieve levels in mathematics, music and other disciplines where abstraction and pattern recognition are at a premium. Kasparov makes this argument in his autobiography, Child of Change, and has made no secret that he believes no woman will never play at the level of the top men. Still, the achievements of Judit Polgar have silenced many critics. Polgar recently moved into the Top 10 rankings in the world, the first woman ever to do so, and her example has driven a deep stake into the argument of gender inferiority.

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Why has chess been so dominated by players from the former Soviet Union?
Players from the former Soviet Union (Kasparov, of Armenian descent, hails from Azerbaidzhan) have dominated chess since World War II, with the brief exception of American Bobby Fischer's reign. Botvinnik's ascension to champion represents the dawn of the Soviet School of Chess, where players were trained in a disciplined regimen, secrets and innovations were hoarded, and potential champions were heavily funded by the state.

Despite the exodus of many grandmasters to Israel, Europe, and the U.S., chess is still very much a part of the Russian culture. Walk up to any émigré today, ask him if he plays chess -- and he will know at least how to move the pieces. Probably he has an uncle who was the hometown champion 30 years ago. The disciplined, mathematical style of Russian education is also thought to be one of the key reasons the country turns out so many world-class engineers, scientists, musicians and chess players.

With the fall of the Soviet Union, this dominance may begin to erode. Anand, the most recent challenger for Kasparov's title, is from India. Many other star players are coming up through the ranks, hailing from places like Great Britain, Hungary, Canada and the United States.

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