Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins

by Garry Kasparov

A “lounge for the weak” at an airport, a “plate of little stupids” at a restaurant

Gödel, Escher, Bach: An Eternal Golden Braid

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Hofstadter wanted to ask, why conquer a task if there’s no insight to be had from the victory? “Okay,” he says, “Deep Blue plays very good chess—so what? Does that tell you something about how we play chess? No. Does it tell you about how Kasparov envisions, understands a chessboard?” A brand of AI that didn’t try to answer such questions, however impressive it might have been, was, in Hofstadter’s mind, a diversion.

Not to be cynical, but Google’s current market cap of over $500 billion is probably one reason.

“Due to the competitive priorities of most programs, little is revealed about how a program finally selects one move over another. This largely explains why computer chess has appeared to advance primarily as a competitive sport (performance driven) rather than as a science (problem driven).”

Anti-discrimination laws in the United States make it illegal to ask applicants about age, gender, race, and health, but algorithmic social media analysis can identify those in a split second, as well as make very accurate guesses at things like sexual preference, political leanings, and income level. History tells us that eventually the desire for services wins out over a vague desire for privacy. We like sharing personal information on social media. We like to have books and music recommended to us by the algorithms of Netflix and Amazon. We won’t give up GPS maps and directions even though using them means dozens of private companies know where we are practically every minute of the day—information that can also be accessed by governments and courts. When Gmail introduced ads based on scanning the content of people’s email there was a collective shock, but it didn’t last long. It’s only an algorithm and, if you’re going to see an ad, wouldn’t you rather see one you’re interested in than one you aren’t?

I’m glad privacy advocates are on the job, especially regarding the powers of the government. I just think they are fighting a losing battle because the tech will continue to improve and because the people they are trying to protect won’t defend themselves. 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.

Our lives are being converted into data.

This cannot be stopped, so what matters more than ever is watching the watchers. The amount of data we produce will continue to expand, and largely to our benefit, but we must monitor where it goes and how it is used. Privacy is dying, so transparency must increase.

“The Brain’s Last Stand,” “Kasparov Defends Humanity,” “The machines are entering the last human refuge, intelligence.” Even the jokes about the match on shows like Jay Leno and David Letterman had a nervous, slightly apocalyptic feel to them. “Kasparov looks pretty nervous. You may think this is no big deal, but wait until that thing comes for your job!” “He’s playing chess against a supercomputer and I still can’t program my VCR!” “In a related story, earlier today the New York Mets were defeated by a microwave oven.”

The first is that we often do our best thinking under pressure. Our senses are heightened and our intuition is activated in a way that is unique to stress and 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. We often do not realize how powerful our intuitive abilities are until we have no choice but to rely on them.

The second lesson was that everyone loves a good story, even if it flies in the face of objective analysis. We love it when the most annoying character in the movie finally gets what he deserves. We root for underdogs, cringe at a hero’s downfall, and sympathize with the unlucky victim of the Fates. All these tropes are in play in a chess game, just as they are in an election or the rise and fall of a business, and they feed the powerful cognitive fallacy of seeking a narrative where often none exists.

Engines don’t care about story. They expose the reality that the only story in a chess game is each individual move, weak or strong.

The human need to understand things as a story instead of as a series of discrete events can lead to many flawed conclusions.

According to Monty Newborn’s book on the match, IBM’s stock rose an equivalent of $3,310 million in little more than a week, a week that the rest of the Dow Jones went down significantly. I should have demanded stock options instead of a 4–1 prize split!

I was reminded of this particular seminar in the context of chess machines because of a slide I used with a quote from Alan Perlis, a computer science pioneer and the first recipient of the Turing Award, in 1966, awarded by ACM. In a famous list of epigrams about programming that he published in 1982, Perlis wrote, “Optimization hinders evolution.”

There cannot be a better way to capture market share than to capture people’s imaginations.

It also marked the first feeling of betrayal of the experiment I thought I had joined when I played Deep Thought in 1989, the longest-running science experiment in history.

Errrr...

I put my Audemars Piguet back on my wrist, part of my ritual when I knew the game was ending.

:)

As Pynchon’s Proverbs for Paranoids, number 3 says in Gravity’s Rainbow, “If they can get you asking the wrong questions, they don’t have to worry about the answers.”

The phrase “human plus machine” can apply to any use of technology since early man bashed something with a rock. Our progress in demonstrating our superiority over other animals is based not primarily on language, but on our creation and use of tools. The mental capacity to make things that improved survival chances led to the natural selection of better and better tool makers and tool users. It’s true that many animals use objects as tools, from apes to crows to wasps, but there is a giant leap from picking up an object to use as a tool and visualizing the right instrument for a task and creating it.

The entrepreneur and venture capitalist Max Levchin used a good expression for this effect referring to Silicon Valley and tech start-ups, and I like it for just about everything. While we were working on a book project together a few years ago, he called it “innovating at the margins.” That is, looking for small efficiencies instead of taking on more substantial risks in the main area of business. Levchin has been interested in online payment and alternative currencies since cofounding PayPal in 1998,

Despite the affluence and high level of technology in the United States, Western Europe, and in Asia’s traditional economic leaders, the potential for rapid change in education is likely in the developing world. There is little reason for them to try to catch up to the developed world by imitating education methods that are becoming obsolete. Just like the people in many poorer nations have adopted smartphones and virtual currencies while skipping the steps of personal computers or traditional banking, they can adopt dynamic new education paradigms very quickly since there are fewer existing structures to replace.

Wealthy nations approach education in the same way a wealthy aristocratic family approaches investing. The status quo has been good for a long time; why rock the boat? I’ve spoken at many education conferences in the past few years, from Paris to Jerusalem to New York, and I’ve never seen such a conservative mindset in any other sector. Not only the administrators and bureaucrats, but the teachers and parents as well. Everyone except for the kids. 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.

Many studies have shown that depression, or a simple lack of self-confidence, results in decision making that is slower, more conservative, and inferior in quality. Pessimism leads to what the psychologists call “a heightened sense of potential disappointment in the expected outcome” of one’s decisions. This leads to indecisiveness and the desire to avoid or postpone consequential decisions.

Intuition is the product of experience and confidence. And here I mean “product” in the mathematical sense, as the equation intuition = experience x confidence.

Emotional influence is only one of the many ways in which humans act irrationally and unpredictably. Economic theory is predicated on the fact that people are “rational actors,” that we will always decide based on what is in our best interests. This is probably why economics is called the “dismal science” and why there is a saying that economists have as much effect on the economy as weather forecasters have on the weather. Humans often aren’t rational at all, not in groups and not individually.

One of the simplest and most powerful examples of how vulnerable we are to false intuition is the “Monte Carlo fallacy,” also called the “gambler’s fallacy.” Assuming a fair coin and a fair flip, if the coin comes up heads twenty times in row, what are the odds it will come up heads again on the next flip? Surely twenty-one consecutive heads is hugely against the odds. The instinct would be to bet on tails, assuming that some statistical regression will occur in your favor eventually. This is entirely wrong, and the belief that it is true is a big part of why the gambling empires in Las Vegas and Macau don’t have to worry about paying their immense electricity bills. Each flip has the same fifty-fifty odds, no matter how many come up in a row or any other order.

We do not calculate every decision by brute force, checking every possible outcome. It is inefficient and unnecessary to do so, because generally we get by pretty well with our assumptions. But when they are isolated by researchers, or exploited by advertisers, politicians, and other con artists, you can see how we could all use a little objective oversight, which is where our machines can help us.

We fall in love with our plans and refuse to admit new evidence against them. We allow confirmation bias to influence us into thinking what we believe is right, despite what the data may say. We trick ourselves into seeing patterns in randomness and correlations where none exist.

And while using your phone isn’t cheating in real life, you might develop a cognitive limp from an overreliance on a digital crutch. The goal must be to use these powerful and objective tools not only to do better analysis and make better decisions in the moment, but also to make us into better decision makers.

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Checklists and goalposts are vital to disciplined thinking and strategic planning. We often stop doing these things outside of a rigid work environment, but they are very useful and today’s digital tools make them very easy to maintain.

It was a triumph of process. A clever process beat superior knowledge and superior technology. It didn’t render knowledge and technology obsolete, of course, but it illustrated the power of efficiency and coordination to dramatically improve results. I represented my conclusion like this: weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

Many jobs will continue to be lost to intelligent automation, but if you’re looking for a field that will be booming for many years, get into human-machine collaboration and process architecture and design. This isn’t just “UX,” user experience, but entirely new ways of bringing human-machine coordination into diverse fields and creating the new tools we need in order to do so.

Resources are limited, so, as one medical researcher put it, do you work on making better mosquito nets or on a cure for malaria?

In a 1951 lecture, Alan Turing suggested that machines would “outstrip our feeble powers” and eventually “take control.”

Computer scientist and science fiction author Vernor Vinge popularized the concept and coined the modern term for this tipping point, “the singularity,” in a 1983 essay. “We will soon create intelligences greater than our own. When this happens, human history will have reached a kind of singularity, an intellectual transition as impenetrable as the knotted space-time at the center of a black hole, and the world will pass far beyond our understanding.”

And yet there is a glaring absence of coordinated governmental or international thinking on the topic. He says, “The approach seems to be to just sleepwalk along as we did with the Internet.”

“The greatest misconception is the hope that the singularity—or the fear that super-intelligence—is right around the corner.” McAfee’s commonsensical and humane investigations into the impact of technology on society most closely match my own outlook. His pragmatism matches the great line by machine learning expert Andrew Ng, formerly of Google and now with China’s Baidu, who has said that worrying about super-intelligent and evil AI today is like worrying about “the problem of overcrowding on Mars.”

I have argued that our technology can make us more human by freeing us to be more creative, but there is more to being human than creativity. We have other qualities the machines cannot match. They have instructions while we have purpose. Machines cannot dream, not even in sleep mode. Humans can, and we will need our intelligent machines in order to turn our grandest dreams into reality. If we stop dreaming big dreams, if we stop looking for a greater purpose, then we may as well be machines ourselves.