Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins

by Garry Kasparov

Twelve years later I was in New York City fighting for my chess life against just one machine, a $10 million IBM supercomputer nicknamed “Deep Blue.” This battle, actually a rematch, became the most famous human-machine competition in history.

Despite centuries of science fiction about automatons that look and move like people, and for all the physical labor today done by robots, it’s fair to say that we have advanced further in duplicating human thought than human movement.

Smarter computers are one key to success, but doing a smarter job of humans and machines working together turns out to be far more important.

These investigations led to visits to places like Google, Facebook, and Palantir, companies for whom algorithms are lifeblood.

The technology for automatic elevators had existed since 1900, but people were too uncomfortable to ride in one without an operator. It took the 1945 strike and a huge industry PR push to change people’s minds, a process that is already repeating with driverless cars. The cycle of automation, fear, and eventual acceptance goes on.

What a powerful point. On tech being not ready.

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.

As much as I like to be appreciated for my work in human rights, my lectures and seminars to business and academic audiences, my foundation’s work in education, and my books on decision making and Russia, I recognize that “former world chess champion” is a calling card with few peers. And, as I explained in detail in that 2007 book on decision making, How Life Imitates Chess, my chess career shaped and informed my thinking in every way.

There are actually two separate but related versions of the fallacy. The first is “the only way a machine will ever be able to do X is if it reaches a level of general intelligence close to a human’s.” The second, “if we can make a machine that can do X as well as a human, we will have figured out something very profound about the nature of intelligence.” 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.

The phrase “Sputnik moment” subsequently entered the national lexicon to represent any foreign accomplishment that serves to remind America that it is not without rival.

A more recent Sputnikian wakeup call to rouse the American giant was supposed to be the 2010 revelation that kids in Shanghai scored far better on standardized math, science, and reading tests than their peers in other nations. An October 13, 2016, Washington Post headline warned that “China has now eclipsed us in AI research.”

In the 1970s, superior Japanese cars were bought by American consumers in the millions. Chinese graduates are enthusiastically welcomed into every American university and firm. In today’s globalized world, technological competition has given way to the sense that we all benefit from someone, somewhere, doing things right, or at least doing them better. While this is no doubt better

than no one doing it right anywhere, we cannot abandon the quest for scientific excellence in the United States. America still possesses the unique potential to innovate on a scale that can push the entire world economy forward. A world in which America is content with mediocrity is, literally, a much poorer world.

In 1985, I started discussing the creation of such an app with the German tech writer Frederic Friedel, who was a serious aficionado of computer chess. He and a programmer acquaintance, Matthias Wüllenweber, founded ChessBase in Hamburg and released the ground-breaking program of the same name in January 1987. And with that, an ancient board game was pulled into the information age, at least if you had an Atari ST.

The ability to collect, organize, analyze, compare, and review games with just a few clicks was, as I put it at the time in 1987, as revolutionary for the study of chess as the printing press.

The key factor in producing elite chess talent is finding it early, and thanks to strong computers this is now very easy to do just about anywhere. It’s no coincidence that the current list of elite chess players contains many representatives of countries with little or no old chess traditions. Computers tend to have this impact in many ways, reducing the influence of dogma.

This growth of machines from chess beginners to Grandmasters is also a progression that is being repeated by countless AI projects around the world. AI products tend to evolve from laughably weak to interesting but feeble, then to artificial but useful, and finally to transcendent and superior to human.

While they may have shortened the careers of a few older players, computers also enabled younger players to rise more quickly. Not just the playing engines, but because of how PC database programs allowed elastic young brains to be plugged into the fire hose of information that was suddenly available.

Bell Laboratories

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’m fond of citing Pablo Picasso, who said in an interview, “Computers are useless. They can only give you answers.” An answer means an end, a full stop, and to Picasso there was never an end, only new questions to explore.

This is what different systems are indeed doing today, using techniques like genetic algorithms and neural nets to basically program themselves. Unfortunately, they have not proved to be stronger than the traditional fast-searching programs that rely more on hard-coded human knowledge—at least not yet. But this is the fault of chess, not of the methods. The more complex the subject, the more likely it is to benefit from an open, self-creating algorithm versus fixed human knowledge. Chess just isn’t complex enough and even I can admit that there is more to life than chess.

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.” I cannot disagree with this statement in isolation, being the product of its truth myself.

Later, programming techniques were developed that allowed programs to “fantasize” a little by looking at hypothetical positions away from the search tree, but this came at the cost of slowing the main search. Much more success was had with ways to make the search smarter and deeper with techniques like “quiescence search” and “singular extensions” that tell the algorithm to deeply examine variations that meet special conditions, such as piece captures or the king being in check. It’s a slight wave toward the old Type B programs and the dream of playing chess like a human and prioritizing certain moves early on, but it’s still search, not knowledge. These clever techniques went a long way toward eliminating the horizon effect in practical play, as did ever-faster chips.

Google doesn’t even worry very much about hiring people with language skills. They feed the system examples of correct translations, millions and millions of examples, so the machine can figure out what’s likely to be right when it encounters something new. When Michie and others tried this in the early days, their machines were too slow and their data collection and entry systems were paltry. No one could imagine that solving such a “human” problem like language could be a matter of scale and speed. They were like the early chess programmers looking at Type A brute force programs and despairing they would ever be fast enough to play competently. 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 barrage of privacy notices has become like all the disregarded warnings about the dangers of trans fats and corn syrup. We want to be healthy, but we like doughnuts more. The greatest security problem we have will always be human nature.

Privacy is dying, so transparency must increase.

I took two lessons away from this discovery. The first is that we often do our best thinking under pressure.

competition. I would still rather have fifteen minutes on my clock than fifteen seconds to make a critical move, but the fact remains that our minds can perform remarkable feats under duress.

No invention is innately “disruptive,” to use another overused term; it must be used disruptively.

The benefits of chess for improving concentration and creativity in kids is documented, so it’s not far-fetched to imagine

Nobel Prize winner Richard Feynman wrote extensively about how he thought his eclectic hobbies like playing Brazilian music and lock-picking actually helped him be better at physics instead of distracting him from it.

He did this not only by analyzing and preparing for the play of his opponents, but with an intense regime of self-criticism.

To prepare, Botvinnik focused on training matches and analysis that replicated what he believed were the games and positions he played poorly in the matches he lost. He understood that while he could not control what his opponents might work on to improve themselves, he could target his own deficiencies.

Even a single game against anyone else would mean lowering Deep Blue from its pedestal, exposing it to scrutiny and criticism. It beat the champion and retired, Fischer-like, becoming as much myth as machine.

He wrote that it had “not only given me a central repository of all of the fruits of my labors in the information fields, but it also has increased the volume and quality of the yield.

We haven’t lost free will; we have gained time that we don’t yet know what to do with.

The real risk is substituting superficial knowledge for the type of understanding and insight that is required to create new things.

The prevailing attitude is that education is too important to take risks. My response is that education is too important not to take risks. We need to find out what works and the only way to do that is to experiment. The kids can handle it. They are already doing it on their own. It’s the adults who are afraid.

The fascinating work of researchers like Daniel Kahneman, Amos Tversky, and Dan Ariely has demonstrated how terrible human beings can be at thinking logically. For all the immense power of the human mind, it is very easy to fool.

Just like chess Grandmasters do at the board, we rely on assumptions and heuristics to make sense of the complexity around us.

“The exigencies of war compel us to remember one thing. A man is much more dispensable than a computer

There are many happily contradictory threads in this discussion, and many of them are contained in this book. I would hate to pretend to have all the answers. It is healthy, and it is necessary, to be concerned about the directions our technology is taking us. I am optimistic on most days, worried on others, and mostly afraid only that we may not have the foresight, imagination, and determination we need to do what must be done.

Chess is the perfect example of Larry Tesler’s “AI effect,” which says that “intelligence is whatever machines haven’t done yet.” As soon as we figure out a way to get a computer to do something intelligent, like play world championship chess, we decide it’s not truly intelligent.

Superintelligence goes beyond the usual fearmongering and explains in (still occasionally terrifying) detail the how and why we might create machines that are far more intelligent than we are, and why they might not care to keep humans around anymore.

The more that people believe in a positive future for technology, the greater chance there is of having one.

Another thing that raising a child and writing a book have in common is how much you learn from an experience that ostensibly places you in the position of knowledge giver.

If people find the rapid advance of intelligent machines terrifying instead of wonderful, it won’t stop it, but it could make the outcome worse for us all.