Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins

by Garry Kasparov

Jump forward another twenty years to today, to 2017, and you can download any number of free chess apps for your phone that rival any human Grandmaster. You can easily imagine a robot in my place in Hamburg, circling inside the tables and defeating thirty-two of the world’s best human players at the same time. The tables have turned, as they always do in our eternal race with our own technology.

wondered, 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. It didn’t quite go according to plan, as we’ll see later, but the fascinating results of these “centaur” competitions convinced me that chess still had a lot to offer the worlds of human cognition and artificial intelligence.

The first real chess program actually predates the invention of the computer and was written by no less a luminary than Alan Turing, the British genius who cracked the Nazi Enigma code. In 1952, he processed a chess algorithm on slips of paper, playing the role of CPU himself, and this “paper machine” played a competent game. This connection went beyond Turing’s personal interest in chess. Chess had a long-standing reputation as a unique nexus of the human intellect, and building a machine that could beat the world champion would mean building a truly intelligent machine.

We laughed at the idea that stiff wood and metal could replicate the soaring grace of the birds. Eventually we have had to concede that there is no physical labor that couldn’t be replicated, or mechanically surpassed.

The elevator operators’ union was seventeen thousand strong in 1920, although its ability to paralyze cities with strikes like the one its members staged in New York in September 1945 surely cost them more than a few mourners when automatic push-button elevators began to replace them in the 1950s. According to the Associated Press, “Thousands struggled up stairways that seemed endless, including the Empire State Building, tallest structure in the world.” Good riddance, you might imagine. But the worries about operator-less elevators were quite similar to the concerns we hear today about driverless cars.

The machines have finally come for the white collared, the college graduates, the decision makers. And it’s about time.

Machines that replace physical labor have allowed us to focus more on what makes us human: our minds. Intelligent machines will continue that process, taking over the more menial aspects of cognition and elevating our mental lives toward creativity, curiosity, beauty, and joy. These are what truly make us human, not any particular activity or skill like swinging a hammer—or even playing chess.

For every truth around elite chess players—we do have good memories and concentration skills—there are at least a dozen misconceptions, both positive and negative. Connections between chess skill and general intelligence are weak at best. There is no more truth to the thought that all chess players are geniuses than in saying that all geniuses play chess.

The willingness to keep trying new things—different methods, uncomfortable tasks—when you are already an expert at something is what separates good from great. Focusing on your strengths is required for peak performance, but improving your weaknesses has the potential for the greatest gains. This is true for athletes, executives, and entire companies. Leaving your comfort zone involves risk, however, and when you are already doing well the temptation to stick with the status quo can be overwhelming, leading to stagnation.

Along with having no luck element, chess is a 100 percent information game; both sides know everything about the position all the time. There are no excuses in chess, no guesses, nothing out of the players’ control. Because of these factors, chess mercilessly punishes disparities in skill level, making it less friendly to newcomers who often don’t have opponents of similar level at hand.

I knew dozens of elite players and they were, if not “normal,” whatever that means, all quite different from one another. Even selecting only from the world champions, they ranged from the mellow musicality of Vasily Smyslov to the chain-smoking and wisecracking of Mikhail Tal. Botvinnik was a stern professional from dawn to dusk in his suit and tie while Spassky had the air of a bon vivant and would occasionally show up to his games in tennis whites. My own nemesis for five consecutive world championship matches, Karpov, was considered ice to my fire, both on and off the board. His soft-spoken demeanor and dependable character matched his quiet, boa constrictor chess style, while my exuberance and outspokenness mirrored my dynamic attacking play. The only thing all of us had in common was being very good at chess.

Our attitude matters, and not because we can stop the march of technological progress even if we wanted to, but because our perspective on disruption affects how well prepared for it we will be. There is plenty of room between the utopian and dystopian visions of the fully automated and artificially intelligent future we are heading into at rapidly increasing speed. Each of us has a choice to make: to embrace these new challenges, or to resist them. Will we help shape the future and set the terms of our relationship with new technology or will we let others force the terms on us?

scientific minds insisted that if a machine could be taught to play chess well, surely the secrets of human cognition would be unlocked at last. This sort of thinking is a trap into which every generation falls when it comes to machine intelligence. We confuse performance—the ability of a machine to replicate or surpass the results of a human—with method, how those results are achieved. This fallacy has proved irresistible in the domain of higher intelligence that is unique to Homo sapiens.

This romanticizing and anthropomorphizing of machine intelligence is natural. It’s logical to look at available models when building something, and what better model for intelligence than the human mind? But time and again, attempts to make machines that think like humans have failed, while machines that prioritize results over method have succeeded.

Beyond science fiction, the matter of whether a machine can be intelligent didn’t really arise among technologists and the general public until the digital took over from the mechanical and analog in the 1940s and vacuum tubes gave way to semiconductors in the 1950s. It was as if ghosts could be imagined in the machines as soon as their processes could no longer be followed by the naked eye. Mechanical calculators had been around since the seventeenth century and key-driven desktop versions were produced in the thousands by the middle of the nineteenth. Programmable mechanical calculators were designed by Charles Babbage in 1834, and the first “computer” program for one was written by Ada Lovelace in 1843.

We’d come a long way since the fourth century BC, when Aristotle believed the brain was a sort of cooling organ while the senses and intelligence resided in the heart, something to remember the next time you hear the phrase “learn something by heart.”

In this self-interview in his 1976 book, Tal is discussing what was going through his head while he was contemplating a knight sacrifice in a game against another Soviet Grandmaster. Ideas piled up one after another. I would transport a subtle reply to my opponent, which worked in one case, to another situation where it would naturally prove quite useless. As a result, my head became filled with a completely chaotic pile of all sorts of moves, and the famous “tree of variations,” from which the trainers recommend that you cut off the small branches, in this case spread with unbelievable rapidity. And then suddenly, for some reason, I remembered the classic couplet by [well-known Soviet children’s poet] Korney Chukovsky: Oh, what a difficult job it was To drag out of the marsh the hippopotamus. I don’t know from what associations the hippopotamus got onto the chess board, but although the spectators were convinced that I was continuing to study the position, I was trying at this time to work out: Just how would you drag a hippopotamus out of the marsh? I remember how jacks figured in my thoughts, as well as levers, helicopters, even a rope ladder. After a lengthy consideration, I admitted defeat as an engineer, and thought spitefully, “Well, let it drown!” And suddenly the hippopotamus disappeared. Went off from the chess board just as he had come on. Of his own accord. And straightaway the position did not appear to be so complicated. Now I somehow realized that it was not...

Concentration and mental organization are essential for professional chess players, but I suspect that we rely on such intuitive leaps more often than we would like to admit.

The total number of legal positions in a game of chess is comparable to the number of atoms in our solar system.

Romanticizing the loss of jobs to technology is little better than complaining that antibiotics put too many grave diggers out of work.

If we feel like we are being surpassed by our own technology it’s because we aren’t pushing ourselves hard enough, aren’t being ambitious enough in our goals and dreams. Instead of worrying about what machines can do, we should worry more about what they still cannot do.

The basic set of values was established two centuries ago: pawns are worth one; knights and bishops are worth three; rooks are worth five; the queen is worth nine. The king is trickier because, while it’s not so powerful in terms of mobility, it must be protected at all costs. The king cannot be captured and if it cannot escape inevitable capture, the game is over: checkmate. One trick is to assign the king a value of one million so the program knows not to put it in danger.

Eventually they learn from experience that, while material is important, it doesn’t matter how many of your opponent’s pieces you’ve captured if your king is getting checkmated. Even the scale of material values is full of exceptions based on the type of position on the board. For example, a well-placed knight can be worth as much or more than a rook with limited scope.

Early chess machines couldn’t learn from experience the way people can. Those greedy kids are learning each time they get checkmated. Even when they lose horribly, they are accumulating useful patterns in their memory. Computers, meanwhile, would make the same mistake over and over, something their human opponents understood and exploited quite well.

Key voor AI

I won the match, putting me a step away from my first world championship contest with Anatoly Karpov the next year, and was also given an Acorn home computer to take back to Baku. I flew on Aeroflot sitting next to the Soviet ambassador, and my fragile new trophy had its own VIP seat and blanket.

It was with a sinking feeling that I realized the smile on Frederic’s face meant that the household Hopper champion was the three-year-old. I was incredulous. “You can’t mean Tommy!” My fears were confirmed when Frederic led his little boy over to the computer and sat him down next to us as the game loaded. Since I was the guest they let me go first and I rose to the occasion with a personal best of nineteen thousand points. My success was short-lived, however, as Tommy took his turn. His little fingers were a blur and it wasn’t long before the score read twenty thousand, then thirty thousand. I conceded defeat to avoid having to sit watching through dinnertime

When you have had success, when the status quo favors you, it becomes very hard to voluntarily change your ways. In my lectures to business audiences I call this the “gravity of past success,”

Fighting against disruption and change is also a standard business practice, one that is usually employed by a market leader trying to protect that lead.

This fits the axiom of Bill Gates, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”

I speak regularly about the difference between strategy and tactics, and why it’s essential to first understand your long-term goals so you don’t confuse them with reactions, opportunities, or mere milestones. The difficulty of doing this is why even small companies need mission statements and regular checkups to make sure they are staying on course. Adapting to circumstances is important, but if you change your strategy all the time you don’t really have one.

What does it even mean to say something matters to a machine? Either a result is significant or it’s not, based on what it has been told is significant, and humans have to establish these values for them. At least, that’s the way it has been for a long time. But our machines are starting to move from surprising us with results to surprising us with the methods they use to find results, and that is a huge difference.

Binet’s insights into the differences between innate talent and acquired knowledge and experience defined the field. “One becomes a good player,” he wrote. “But one is born an excellent player.”

Humans don’t learn a native language from grammar books, after all. The trajectory so far has been as follows: We create a machine that follows strict rules in order to imitate human performance. Its performance is poor and artificial. With generations of optimization and speed gains, performance improves. The next jump occurs when the programmers loosen the rules and allow the machine to figure out more things on its own, and to shape or even ignore the old rules. To become good at anything you have to know how to apply basic principles. To become great at it, you have to know when to violate those principles.

The saying that the victor is the one who makes the next to last mistake is very true.

Lasker was a pioneer in the psychological approach to chess, writing that the best move was the one that made your opponent most uncomfortable. That is, “to play the man, not the board.”

The science writer Malcolm Gladwell famously formulated a “ten thousand hours” theory in his book Outliers that says practice, not innate talent, is what makes the difference in exceptional human achievement.

Gladwell later clarified further in a Q&A on the website Reddit, writing that practice alone wasn’t enough, and that “I could play chess for 100 years and I’ll never be a Grandmaster. The point is simply that natural ability requires a huge investment of time in order to be made manifest.

Before kids are accepted into the program and while they are participants, I carefully review a selection of their games. And while I won’t claim a perfect record of picking winners, it is obvious to me when a young player displays flashes of brilliance. By brilliance I mean the sort of inspiration and creativity that cannot be produced by ten million hours of practice,

Lastly on talent, don’t tell me that hard work can be more important than talent. This is a handy platitude for motivating our kids to study or practice piano, but as I wrote ten years ago in How Life Imitates Chess, hard work is a talent.

In purely chess terms, a human versus machine game is asymmetrical warfare. Computers are very good at sharp tactics in complex positions while that is a human’s greatest weakness. Humans are very good at planning and what we call “positional play,” the strategic and structural considerations and quiet maneuvering.

The great AI pioneer Donald Michie, who worked at Bletchley Park with Alan Turing cracking the Enigma code during World War II, wrote wisely about this in 1989, predicting that there could be a “Grandmaster backlash” against machine participation in tournaments: Chess is a culture shared among colleagues who form a human community, however adversarial the game may be in itself. After play, opponents commonly analyze the fine points together, and many find in the tournament room the mainstream of their social life. Robot intruders contribute only brute force, not interesting chess ideas. … Rather as a tennis professional facing a robot player able to impart spins which could never come from a human-held racket, Grandmasters will find in such opposition only obscurity. What has this to do with the skill to which they have devoted their life?

Michie also compared playing against a computer to a professional opera singer performing a “duet with a synthesizer,” an analogy I appreciate very much.

It was a Cold War showdown, the brash American against the Soviet apparatus, played on the world stage in Reykjavik for hundreds of thousands of dollars, an incredible amount at the time, instead of between two Soviets in a Moscow theater for peanuts, pride, and privileges.

Then the 1973 Mansfield Amendment limited DARPA appropriations to projects with direct military application, a heavy blow to government support of basic research in the sciences and a death blow to relatively unproductive fields like AI was turning out to be, at least in the eyes of the Defense Department. They wanted expert systems for recognizing bomb targets, not machines that could talk.

His first conclusion was not surprising: the growing government bureaucracy stifled communication and innovation. “It got big,” he told me over lunch. “For a while, when we had retreats you would have physicists and computer guys swapping stories and ideas with microbiologists and psychologists. Everybody could fit into one room. As it grew, that became impossible, and the different groups had little contact with each other.” Instead

Great new things come from cross-pollination.

The field was machine learning, which had been around for years without showing very good results. What made the difference in the 1980s was data—lots and lots of data.

No one could imagine that solving such a “human” problem like language could be a matter of scale and speed.

As one Google Translate engineer put it, “When you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.”

The program developed its pieces, launched an attack, and immediately sacrificed its queen! It lost in just a few moves, having given up the queen for next to nothing. Why did it do it? Well, when a Grandmaster sacrifices his queen it’s nearly always a brilliant and decisive blow. To the machine, educated on a diet of GM games, giving up its queen was clearly the key to success!