The following article was sent to an Email list dedicated to blind chess 
players, and I find it interesting enough to forward to others, so here it is:

New York Review of Books


Volume 57, Number 2 · February 11, 2010
The Chess Master and the Computer
By Garry Kasparov
Chess Metaphors: Artificial Intelligence and the Human Mind
by Diego Rasskin-Gutman, translated from the Spanish by Deborah Klosky
MIT Press, 205 pp., $24.95

In 1985, in Hamburg, I played against thirty-two different chess computers
at the same time in what is known as a simultaneous exhibition. I walked
from one machine to the next, making my moves over a period of more than
five hours. The four leading chess computer manufacturers had sent their top
models, including eight named after me from the electronics firm Saitek.

It illustrates the state of computer chess at the time that it didn't come
as much of a surprise when I achieved a perfect 32-0 score, winning every
game, although there was an uncomfortable moment. At one point I realized
that I was drifting into trouble in a game against one of the "Kasparov"
brand models. If this machine scored a win or even a draw, people would be
quick to say that I had thrown the game to get PR for the company, so I had
to intensify my efforts. Eventually I found a way to trick the machine with
a sacrifice it should have refused. From the human perspective, or at least
from my perspective, those were the good old days of man vs. machine chess.

Eleven years later I narrowly defeated the supercomputer Deep Blue in a
match. Then, in 1997, IBM redoubled its efforts-and doubled Deep Blue's
processing power-and I lost the rematch in an event that made headlines
around the world. The result was met with astonishment and grief by those
who took it as a symbol of mankind's submission before the almighty
computer. ("The Brain's Last Stand" read the Newsweek headline.) Others
shrugged their shoulders, surprised that humans could still compete at all
against the enormous calculating power that, by 1997, sat on just about
every desk in the first world.



It was the specialists-the chess players and the programmers and the
artificial intelligence enthusiasts-who had a more nuanced appreciation of
the result. Grandmasters had already begun to see the implications of the
existence of machines that could play-if only, at this point, in a select
few types of board configurations-with godlike perfection. The computer
chess people were delighted with the conquest of one of the earliest and
holiest grails of computer science, in many cases matching the mainstream
media's hyperbole. The 2003 book Deep Blue by Monty Newborn was blurbed as
follows: "a rare, pivotal watershed beyond all other triumphs: Orville
Wright's first flight, NASA's landing on the moon...."

The AI crowd, too, was pleased with the result and the attention, but
dismayed by the fact that Deep Blue was hardly what their predecessors had
imagined decades earlier when they dreamed of creating a machine to defeat
the world chess champion. Instead of a computer that thought and played
chess like a human, with human creativity and intuition, they got one that
played like a machine, systematically evaluating 200 million possible moves
on the chess board per second and winning with brute number-crunching force.
As Igor Aleksander, a British AI and neural networks pioneer, explained in
his 2000 book, How to Build a Mind:

By the mid-1990s the number of people with some experience of using
computers was many orders of magnitude greater than in the 1960s. In the
Kasparov defeat they recognized that here was a great triumph for
programmers, but not one that may compete with the human intelligence that
helps us to lead our lives.

It was an impressive achievement, of course, and a human achievement by the
members of the IBM team, but Deep Blue was only intelligent the way your
programmable alarm clock is intelligent. Not that losing to a $10 million
alarm clock made me feel any better.

My hopes for a return match with Deep Blue were dashed, unfortunately. IBM
had the publicity it wanted and quickly shut down the project. Other chess
computing projects around the world also lost their sponsorship. Though I
would have liked my chances in a rematch in 1998 if I were better prepared,
it was clear then that computer superiority over humans in chess had always
been just a matter of time. Today, for $50 you can buy a home PC program
that will crush most grandmasters. In 2003, I played serious matches against
two of these programs running on commercially available multiprocessor
servers-and, of course, I was playing just one game at a time-and in both
cases the score ended in a tie with a win apiece and several draws.

Inevitable or not, no one understood all the ramifications of having a
super-grandmaster on your laptop, especially what this would mean for
professional chess. There were many doomsday scenarios about people losing
interest in chess with the rise of the machines, especially after my loss to
Deep Blue. Some replied to this with variations on the theme of how we still
hold footraces despite cars and bicycles going much faster, a spurious
analogy since cars do not help humans run faster while chess computers
undoubtedly have an effect on the quality of human chess.

Another group postulated that the game would be solved, i.e., a
mathematically conclusive way for a computer to win from the start would be
found. (Or perhaps it would prove that a game of chess played in the best
possible way always ends in a draw.) Perhaps a real version of HAL 9000
would simply announce move 1.e4, with checkmate in, say, 38,484 moves. These
gloomy predictions have not come true, nor will they ever come to pass.
Chess is far too complex to be definitively solved with any technology we
can conceive of today. However, our looked-down-upon cousin, checkers, or
draughts, suffered this fate quite recently thanks to the work of Jonathan
Schaeffer at the University of Alberta and his unbeatable program Chinook.

The number of legal chess positions is 1040, the number of different
possible games, 10120. Authors have attempted various ways to convey this
immensity, usually based on one of the few fields to regularly employ such
exponents, astronomy. In his book Chess Metaphors, Diego Rasskin-Gutman
points out that a player looking eight moves ahead is already presented with
as many possible games as there are stars in the galaxy. Another staple, a
variation of which is also used by Rasskin-Gutman, is to say there are more
possible chess games than the number of atoms in the universe. All of these
comparisons impress upon the casual observer why brute-force computer
calculation can't solve this ancient board game. They are also handy, and I
am not above doing this myself, for impressing people with how complicated
chess is, if only in a largely irrelevant mathematical way.

This astronomical scale is not at all irrelevant to chess programmers.
They've known from the beginning that solving the game-creating a provably
unbeatable program-was not possible with the computer power available, and
that effective shortcuts would have to be found. In fact, the first chess
program put into practice was designed by legendary British mathematician
Alan Turing in 1952, and he didn't even have a computer! He processed the
algorithm on pieces of paper and this "paper machine" played a competent

Rasskin-Gutman covers this well-traveled territory in a book that achieves
its goal of being an overview of overviews, if little else. The history of
the study of brain function is covered in the first chapter, tempting the
reader to skip ahead. You might recall axons and dendrites from high school
biology class. We also learn about cholinergic and aminergic systems and
many other things that are not found by my computer's artificially
intelligent English spell-checking system-or referenced again by the author.
Then it's on to similarly concise, if inconclusive, surveys of artificial
intelligence, chess computers, and how humans play chess.

There have been many unintended consequences, both positive and negative, of
the rapid proliferation of powerful chess software. Kids love computers and
take to them naturally, so it's no surprise that the same is true of the
combination of chess and computers. With the introduction of super-powerful
software it became possible for a youngster to have a top- level opponent at
home instead of need ing a professional trainer from an early age. Countries
with little by way of chess tradition and few available coaches can now
produce prodigies. I am in fact coaching one of them this year,
nineteen-year-old Magnus Carlsen, from Norway, where relatively little chess
is played.

The heavy use of computer analysis has pushed the game itself in new
directions. The machine doesn't care about style or patterns or hundreds of
years of established theory. It counts up the values of the chess pieces,
analyzes a few billion moves, and counts them up again. (A computer
translates each piece and each positional factor into a value in order to
reduce the game to numbers it can crunch.) It is entirely free of prejudice
and doctrine and this has contributed to the development of players who are
almost as free of dogma as the machines with which they train. Increasingly,
a move isn't good or bad because it looks that way or because it hasn't been
done that way before. It's simply good if it works and bad if it doesn't.
Although we still require a strong measure of intuition and logic to play
well, humans today are starting to play more like computers.

The availability of millions of games at one's fingertips in a database is
also making the game's best players younger and younger. Absorbing the
thousands of essential patterns and opening moves used to take many years, a
process indicative of Malcolm Gladwell's "10,000 hours to become an expert"
theory as expounded in his recent book Outliers. (Gladwell's earlier book,
Blink, rehashed, if more creatively, much of the cognitive psychology
material that is re-rehashed in Chess Metaphors.) Today's teens, and
increasingly pre-teens, can accelerate this process by plugging into a
digitized archive of chess information and making full use of the
superiority of the young mind to retain it all. In the pre-computer era,
teenage grandmasters were rarities and almost always destined to play for
the world championship. Bobby Fischer's 1958 record of attaining the
grandmaster title at fifteen was broken only in 1991. It has been broken
twenty times since then, with the current record holder, Ukrainian Sergey
Karjakin, having claimed the highest title at the nearly absurd age of
twelve in 2002. Now twenty, Karjakin is among the world's best, but like
most of his modern wunderkind peers he's no Fischer, who stood out head and
shoulders above his peers-and soon enough above the rest of the chess world
as well.

Excelling at chess has long been considered a symbol of more general
intelligence. That is an incorrect assumption in my view, as pleasant as it
might be. But for the purposes of argument and investigation, chess is, in
Russkin-Gutman's words, "an unparalleled laboratory, since both the learning
process and the degree of ability obtained can be objectified and
quantified, providing an excellent comparative framework on which to use
rigorous analytical techniques."

Here I agree wholeheartedly, if for different reasons. I am much more
interested in using the chess laboratory to illuminate the workings of the
human mind, not the artificial mind. As I put it in my 2007 book, How Life
Imitates Chess, "Chess is a unique cognitive nexus, a place where art and
science come together in the human mind and are then refined and improved by
experience." Coincidentally the section in which that phrase appears is
titled "More than a metaphor." It makes the case for using the
decision-making process of chess as a model for understanding and improving
our decision-making everywhere else.

This is not to say that I am not interested in the quest for intelligent
machines. My many exhibitions with chess computers stemmed from a desire to
participate in this grand experiment. It was my luck (perhaps my bad luck)
to be the world chess champion during the critical years in which computers
challenged, then surpassed, human chess players. Before 1994 and after 2004
these duels held little interest. The computers quickly went from too weak
to too strong. But for a span of ten years these contests were fascinating
clashes between the computational power of the machines (and, lest we
forget, the human wisdom of their programmers) and the intuition and
knowledge of the grandmaster.

In what Rasskin-Gutman explains as Moravec's Paradox, in chess, as in so
many things, what computers are good at is where humans are weak, and vice
versa. This gave me an idea for an experiment. What if instead of human
versus machine we played as partners? My brainchild saw the light of day in
a match in 1998 in León, Spain, and we called it "Advanced Chess." Each
player had a PC at hand running the chess software of his choice during the
game. The idea was to create the highest level of chess ever played, a
synthesis of the best of man and machine.

Although I had prepared for the unusual format, my match against the
Bulgarian Veselin Topalov, until recently the world's number one ranked
player, was full of strange sensations. Having a computer program available
during play was as disturbing as it was exciting. And being able to access a
database of a few million games meant that we didn't have to strain our
memories nearly as much in the opening, whose possibilities have been
thoroughly catalogued over the years. But since we both had equal access to
the same database, the advantage still came down to creating a new idea at
some point.

Having a computer partner also meant never having to worry about making a
tactical blunder. The computer could project the consequences of each move
we considered, pointing out possible outcomes and countermoves we might
otherwise have missed. With that taken care of for us, we could concentrate
on strategic planning instead of spending so much time on calculations.
Human creativity was even more paramount under these conditions. Despite
access to the "best of both worlds," my games with Topalov were far from
perfect. We were playing on the clock and had little time to consult with
our silicon assistants. Still, the results were notable. A month earlier I
had defeated the Bulgarian in a match of "regular" rapid chess 4-0. Our
advanced chess match ended in a 3-3 draw. My advantage in calculating
tactics had been nullified by the machine.

This experiment goes unmentioned by Russkin-Gutman, a major omission since
it relates so closely to his subject. Even more notable was how the advanced
chess experiment continued. In 2005, the online chess-playing site hosted what it called a "freestyle" chess tournament in which
anyone could compete in teams with other players or computers. Normally,
"anti-cheating" algorithms are employed by online sites to prevent, or at
least discourage, players from cheating with computer assistance. (I wonder
if these detection algorithms, which employ diagnostic analysis of moves and
calculate probabilities, are any less "intelligent" than the playing
programs they detect.)

Lured by the substantial prize money, several groups of strong grandmasters
working with several computers at the same time entered the competition. At
first, the results seemed predictable. The teams of human plus machine
dominated even the strongest computers. The chess machine Hydra, which is a
chess-specific supercomputer like Deep Blue, was no match for a strong human
player using a relatively weak laptop. Human strategic guidance combined
with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to
be not a grandmaster with a state-of-the-art PC but a pair of amateur
American chess players using three computers at the same time. Their skill
at manipulating and "coaching" their computers to look very deeply into
positions effectively counteracted the superior chess understanding of their
grandmaster opponents and the greater computational power of other
participants. Weak human + machine + better process was superior to a strong
computer alone and, more remarkably, superior to a strong human + machine +
inferior process.

The "freestyle" result, though startling, fits with my belief that talent is
a misused term and a misunderstood concept. The moment I became the youngest
world chess champion in history at the age of twenty-two in 1985, I began
receiving endless questions about the secret of my success and the nature of
my talent. Instead of asking about Sicilian Defenses, journalists wanted to
know about my diet, my personal life, how many moves ahead I saw, and how
many games I held in my memory.

I soon realized that my answers were disappointing. I didn't eat anything
special. I worked hard because my mother had taught me to. My memory was
good, but hardly photographic. As for how many moves ahead a grandmaster
sees, Russkin-Gutman makes much of the answer attributed to the great Cuban
world champion José Raúl Capablanca, among others: "Just one, the best one."
This answer is as good or bad as any other, a pithy way of disposing with an
attempt by an outsider to ask something insightful and failing to do so.
It's the equivalent of asking Lance Armstrong how many times he shifts gears
during the Tour de France.

The only real answer, "It depends on the position and how much time I have,"
is unsatisfying. In what may have been my best tournament game at the 1999
Hoogovens tournament in the Netherlands, I visualized the winning position a
full fifteen moves ahead-an unusual feat. I sacrificed a great deal of
material for an attack, burning my bridges; if my calculations were faulty I
would be dead lost. Although my intuition was correct and my opponent,
Topalov again, failed to find the best defense under pressure, subsequent
analysis showed that despite my Herculean effort I had missed a shorter
route to victory. Capablanca's sarcasm aside, correctly evaluating a small
handful of moves is far more important in human chess, and human
decision-making in general, than the systematically deeper and deeper search
for better moves-the number of moves "seen ahead"-that computers rely on.

There is little doubt that different people are blessed with different
amounts of cognitive gifts such as long-term memory and the visuospatial
skills chess players are said to employ. One of the reasons chess is an
"unparalleled laboratory" and a "unique nexus" is that it demands high
performance from so many of the brain's functions. Where so many of these
investigations fail on a practical level is by not recognizing the
importance of the process of learning and playing chess. The ability to work
hard for days on end without losing focus is a talent. The ability to keep
absorbing new information after many hours of study is a talent. Programming
yourself by analyzing your decision-making outcomes and processes can
improve results much the way that a smarter chess algorithm will play better
than another running on the same computer. We might not be able to change
our hardware, but we can definitely upgrade our software.

With the supremacy of the chess machines now apparent and the contest of
"Man vs. Machine" a thing of the past, perhaps it is time to return to the
goals that made computer chess so attractive to many of the finest minds of
the twentieth century. Playing better chess was a problem they wanted to
solve, yes, and it has been solved. But there were other goals as well: to
develop a program that played chess by thinking like a human, perhaps even
by learning the game as a human does. Surely this would be a far more
fruitful avenue of investigation than creating, as we are doing, ever-faster
algorithms to run on ever-faster hardware.

This is our last chess metaphor, then-a metaphor for how we have discarded
innovation and creativity in exchange for a steady supply of marketable
products. The dreams of creating an artificial intelligence that would
engage in an ancient game symbolic of human thought have been abandoned.
Instead, every year we have new chess programs, and new versions of old
ones, that are all based on the same basic programming concepts for picking
a move by searching through millions of possibilities that were developed in
the 1960s and 1970s.

Like so much else in our technology-rich and innovation-poor modern world,
chess computing has fallen prey to incrementalism and the demands of the
market. Brute-force programs play the best chess, so why bother with
anything else? Why waste time and money experimenting with new and
innovative ideas when we already know what works? Such thinking should
horrify anyone worthy of the name of scientist, but it seems, tragically, to
be the norm. Our best minds have gone into financial engineering instead of
real engineering, with catastrophic results for both sectors.

Perhaps chess is the wrong game for the times. Poker is now everywhere, as
amateurs dream of winning millions and being on television for playing a
card game whose complexities can be detailed on a single piece of paper. But
while chess is a 100 percent information game-both players are aware of all
the data all the time-and therefore directly susceptible to computing power,
poker has hidden cards and variable stakes, creating critical roles for
chance, bluffing, and risk management.

These might seem to be aspects of poker based entirely on human psychology
and therefore invulnerable to computer incursion. A machine can trivially
calculate the odds of every hand, but what to make of an opponent with poor
odds making a large bet? And yet the computers are advancing here as well.
Jonathan Schaeffer, the inventor of the checkers-solving program, has moved
on to poker and his digital players are performing better and better against
strong humans-with obvious implications for online gambling sites.

Perhaps the current trend of many chess professionals taking up the more
lucrative pastime of poker is not a wholly negative one. It may not be too
late for humans to relearn how to take risks in order to innovate and
thereby maintain the advanced lifestyles we enjoy. And if it takes a
poker-playing supercomputer to remind us that we can't enjoy the rewards
without taking the risks, so be it.

In God we trust!
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