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The Problem Isn’t The Robots, It’s Us

2014 May 4

Richard Feynman was a legend in scientific circles.  One of the preeminent physicists of the 20th century—even other top minds considered him a magician—he is almost as well known for his jokes and pranks as he is for his groundbreaking discoveries.

When Feynman was a young scientist, Eugene Wigner compared him to Paul Dirac, a giant at the time well known for his autistic qualities, saying that “he’s a second Dirac, only human this time.”  The quote is telling, not least because Wigner was Dirac’s brother-in-law.

While Dirac was clearly a genius, Feynman was truly transcendent.  Although Feynman won the Nobel prize in physics, he was also a pioneer in nanotechnology and computing, did important work in virology and became an accomplished painter.  Much like Feynman, as robots replace human jobs, we must learn to do them anew, only human this time.

What Is Intelligence?

Intelligence has always been hard to define.  IQ tests have been around for at least a hundred years and they certainly measure something—scores have been shown to have a 30%-50% correlation with professional success—but fail to capture the whole picture. Feynman himself is said to have had an IQ score of 125.  Good, but not exceptional.

If intelligence is an intangible quality in humans, then it is even harder to denote in computers.  We know computers can do certain tasks, but at what point do can they really be considered to possess human intelligence?  It’s a startlingly complicated question and it’s been debated for decades.

The most accepted answer comes from a 1950 paper by Alan Turing.  He devised a simple test—called the Turing Test—which is devilishly simple.  You just have a human judge converse with both machines and people and see if the judge can reliably tell the difference.

Alas, it turned out that the Turing test was relatively easy to pass.  One program, called ELIZA, was able to fool people as early as 1966.  Another, named PARRY stumped even experienced psychologists.  Yet, those programs actually weren’t very useful for anything but passing the test.  As it turns out imitating intelligence is not that hard.

The Infinite Monkey Theorem

Many believe that the real test of humanity is not logic or computation, but the arts. However it’s long been known that even great works, such as the the collected works of Shakespeare or Tolstoy’s War and Peace could be created mindlessly.

The concept, known as the infinite monkey theorem posits that infinite monkeys tapping away at infinite computers will eventually create not only Tolstoy and Shakespeare, but every other work human civilization has produced.  In effect, with enough computing power, producing great works becomes a problem of curation rather than creation.

This, of course, is no longer theoretical.  Computers already perform creative tasks like writing articles and composing music.  In fact, they do it so well that even expert critics are sometimes fooled.  Yet as Jaron Lanier argues in You Are Not a Gadget, this line of reasoning misses the point in much the same way the Turing test does.

Tolstoy and Shakespeare did not create because they were doing a job, but because they had a specific intention to relate human experience.  Feynman was driven by similar motivations, which he made clear in his second memoir, The Pleasure of Finding Things Out.

We achieve greatness not through our ability to perform tasks, but through specific intent. It is our ability to imagine and dream that makes us special.

How Technology Fails

Computers do what they do not because they are motivated by experience, but because we design them to perform specific tasks in a specific way.  Perhaps not surprisingly, given how incredibly powerful our computers have become, they excel at the jobs we give them.  If fact, they often do them much better than humans do.

Yet they are not perfect and as Tim Harford has pointed out in the Financial Times, they often need humans to correct them.  Computerized techniques like big data analysis are good at figuring out “what?” but not so good at discerning “why?” and the “why” is important.

If we tell a machine to find a series of correlations, it can instantly scour millions of data points and develop a working model.  These models—Google flu trends is a great example—can be incredibly useful, but they do go awry.  Correlation is not causality and at some point, we need to uncover causes to recognize and solve important problems.

Intelligence is vastly more than processing.

Don’t Be A Robot

Dirac became famous by solving a fairly obvious problem.  (The Dirac equation essential reconciles Einsteins relativity with quantum mechanics).  Yet Feynman excelled because he posed questions nobody else thought to ask.  His genius was not only computation, but imagination.

For example, when he created the concept of nanotechnology at a physics conference in 1959, he didn’t use any complicated formulas— his entire speech could be read by an intelligent high schooler—but simply pointed out the possibilities of “room at the bottom”. He was also a great collaborator, which allowed him to explore new horizons.

Our problem today is not that we face a world of increasing automation, but that too many of us have grown accustomed to acting like robots, striving to perform rote tasks with efficiency and accuracy.  We are educated to provide answers, not questions and when we enter professional life we are evaluated the same way.

Today, we carry smartphones with exponentially more processing power than Feynman and Dirac put together.  We can choose robots to do a number of jobs more cheaply and efficiently than a human ever could.  Yet they remain tools, means to an end rather than ends in themselves.  Robots cannot live our lives for us.

That’s the challenge of freedom.  At some point, you have to decide what to do with it or it is wasted.

– Greg

13 Responses leave one →
  1. May 4, 2014

    Hi Greg, I love the topic, Thanks as it forces people who accept conventional thinking to understand the importance of business creativity and purpose where seemingly forever, technology for technology sake seemed to rule. I spent 25 years working with engineers (HP and Agilent) and respecting their gifts, while struggling as a martyr to represent external thinking and emotional selling propositions. The times I won support produced the strongest product launches in either companies history yet neither know how it was done or cared to ask. Many engineers, so it seems, prefer to emulate robots and assume just the facts matter. I love a recent story where a blind man posts a sign saying he is blind and gets little help, then an ad man restates his sign from I am blind to ‘it is spring and I am blind” so context was everything.

    I happen to believe deeply in what marketing can and needs to do to shape a better world and keep players honest. The problem is too few by actions support that premise. Most follow the almighty dollar.

    I admit it is frustrating to see our most human gifts – creative problem solving and value creation – diffused so that what robots can do, threatens what we do at our best. that ‘best’ is a poor example and more CEO’s and creative people need to dedicate time and efforts to illustrating our best potential rather than stupefying it so much any robot can do it.

    I may be alone in this but am convinced business creativity and connected-ness needs to step up and illustrate a more insightful nation.


  2. May 4, 2014

    Thanks Bill. Although I wasn’t thinking about engineers, who can be quite good at creative problem solving. Yet, I do think you’re right. It is the non-routine tasks that are hard to automate.

    – Greg

  3. May 4, 2014

    Thank you Greg. Feynman was surely one of the towering geniuses as much for his brilliance as his humanity as the article points out. It will take that combination of vision to keep us on track. There need be no conflict between science and technology, and humanity but it will take thought and effort.

  4. May 4, 2014

    Hi Greg,
    Engineers are great at product and market characterization, solving problems with known attributes/performance characteristics and incremental improvement. I loved working with them as their definition work set me up to do what I do well which I liken to establishing relevance, credibility, context, emotional and logical arguments, compelling value propositions and what I call market shaping. My intended point was that some knowledge functions have blinders on regarding the higher impact contributions that marketers can make but I am not talking about traditional marketing either. I get deeply involved in reshaping the role marketers can play to create value, purpose, map trends to actionable opportunities. All the work I did in HP and Agilent was a surprise because all they knew was traditional push marketing.

    So what I’d love to see is stronger acceptance by CEO and exec teams especially in tech, science and medical markets to understand how marketing can be the best function to help companies to adapt in challenging times instead of putting down old references of a now untenable marketing model. The robots play was – to me – just another indicator that when human potential or creative thinking is absent, geez why not let a robot do that. To an earlier blog you had, yes some automation robotics will do a better job sensing patterns. So we need to know what we do well and what can be automated with better results.

  5. May 5, 2014

    The reason people perform like robots is that it is easy to measure. We want certainty and robotic thinking provides that. As we get more technologically savvy, we are becoming more like what we are creating—robots. I think this is because we have little tolerance for uncertainty in everything we do.

  6. May 5, 2014

    Very true. The issue today isn’t so much categories of jobs, such as blue collar/white collar, but routine vs. non-routine work.

    – Greg

  7. May 5, 2014

    I think there’s a lot of truth to that. For a long time “you manage what you measure” was a popular corporate mantra.

    – Greg

  8. May 6, 2014

    Loved the article Greg.

    Feynman’s IQ result may have been skewed. “Feynman received the highest score in the country by a large margin on the notoriously difficult Putnam mathematics competition exam, although he joined the MIT team on short notice and did not prepare for the test. He also reportedly had the highest scores on record on the math/physics graduate admission exams at Princeton.”

    “Yet Feynman excelled because he posed questions nobody else thought to ask” I think that is an excellent point and the crux of your argument. I also think that computers find solutions that we can not as we are blinded by the bias of knowledge.

    Humans and computers make a fine match.

  9. May 7, 2014

    The IQ story comes from Feynman himself, but many have questioned it. He was philosophically opposed to IQ scores and there was, as you mentioned, his scores on the Putnam and other tests. He unquestionably had a very strong mathematical acumen.

    Yet, there are also some good reasons to believe it’s true. An IQ of 125 is quite high, just not unusually high and many intelligence researchers believe that once you go above 120, IQ scores are largely irrelevant.

    We also have Feynman’s own accounts of how he solved very complex problems in his head through creative problem solving rather than computational horsepower. A while back, I wrote about some tricks of my own I picked up over the years:

    In any case, it’s amazing to see how his mind worked. He created nanotechnology without any real computation at all. If you haven’t read his speech, “There’s Plenty of Room at the Bottom,” you should take a look. You can find it above under the link “entire speech.”

    – Greg

  10. May 10, 2014

    Greg, some great math cheats in your earlier posts. I hadn’t read that. I think the heading “Lean Back And Tell A Story” was especially applicable to Feynman. I don’t think that he used math shortcuts as much as he had the capacity to understand systems. Math, of course, is only a construct that we use to describe the physical things that happen. Math is required to get to the detail and description to explore the edges of science but I believe that it’s the basic conceptualization that should be taught to kids entering physics, so they know that the math is trying to describe. I believe that Feynman’s mind worked like this.
    Here’s his recounting an early thought experiment with his wagon:
    And a great description of heat:

  11. May 10, 2014

    I think that’s right. He had an intense curiosity about how things work. To be honest, I think he overplayed how much he used tricks. In any case, he was absolutely brilliant!


  12. May 12, 2014

    If you ask a monkey to do a human intelligence test, they won’t do as well. So we conclude they aren’t as intelligent.
    But when we asked humans to do an intelligence test based around things monkeys were familiar with, the humans lost.
    So are we less intelligent than monkeys?

    Now we are judging computers by an intelligence test set for humans.
    But what we are actually testing is wrong routing.

    When a human thinks it tries to follow a logical path. But the neral paths go wrong and take it down other pathways. 99.9999999999999% of those are wrong.
    But our memory only remembers the one which led to a better outcome.
    And we judge what better means too.

    But when we look at computers, we criticise them for not making wrong steps. For being logical. The perfect exampe is Spock. We laugh at logic, because we delight in being illogical. We have sold ourselves the story that it is better.

    But it isn’t. Put a policeman on traffic duty and the traffic will back up – he can’t beat a traffic light. Play chess against a computer and the computer will win – it can analyse more moves and make fewer errors. Build a website based on logic and it will sell more than a salesman and give a happier path for the customer.

    We must stop being so desperate to be King Cnut and hold back the tide of logic.
    You’re second best. To a piece of sand. Get over it.

  13. May 12, 2014

    Interesting points. Thanks Peter.

    – Greg

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