Garry Kasparov reviews "Chess Metaphors - Artificial Intelligence and the Human Mind"
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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.
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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 game.
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 Le�n, 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 Playchess.com 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 Jos� Ra�l 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.
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