Unlearning 101

A Picture in Unlearning

It has been said that a photo is worth a thousand words. If so, it appears that President Obama has been caught red-handed oogling a young woman.

The truth is not so clear as this ABC video demonstrates. (Hilariously, the same can't be said for French President Nicolas Sarkozy.)

It just goes to show you that the truth isn't always so clear and that we must all remain open to the idea of unlearning what we believe to be the truth. (With the possible exception, of course, of the idea that all French men are sexist pigs!)

Unlearning the Future

Carl Sagan once advised the public to “never underestimate the power of exponential.” It was sound advice. Unfortunately, as with so much other wise counsel, it is easy to dismiss.

You shouldn’t.

Consider just a few recent examples of exponential growth. In 1998, a small Silicon Valley start-up was conducting 25 million web-based searches a day. The company had developed an impressive technology but it hardly seemed transformative. Ten years of exponential growth later, though, and Google has changed everything from how people get their news and entertainment to how politics and health care are practiced.

Wikipedia offers yet another example of exponential growth. Who amongst us could have imagined in 2001 when the first 100 Wikipedia entries were placed on the Internet that within eight years the site--which relies on free labor no less--would become the most widely used information source in the world?

It would be easy to believe that Google and Wikipedia are unique and, as such, represent one-time anomalies. They are not.

Ironically, if you type the word “exponential” into Google, the first search item that comes up is an entry from Wikipedia. If one then clicks on the list of “exponential topics” at the bottom of the definition there is an odd looking word: zenzizenzizenzic.

The term is defined as a number to its eighth power. Thus, the zenzizenzizenzic of 2 is 2(8)--or 256.

Now, zenzizenzizenzic might seem like a rather innocent concept but recall that Google and Wikipedia both experienced zenzizenzizenzic-like growth and, in the process, transformed a number of industries; and, in the coming decade, so too will a number of other emerging technologies.

Consider, for example, that Intel Corporation recently announced it had successfully doubled--to 2 billion--the number of transistors it has crammed onto its next-generation computer chip.

The real world manifestation of Intel’s zenzizenzizenzic is that we can now purchase a computer over 200 times more powerful than the one we bought a 1997--at one-third the cost.

Moreover, because the semiconductor industry will likely continue its little trick of effectively doubling the number of transistors it can place on a computer chip every 18 months this implies that by 2020 the zenzizenzizenzic of today’s 2 billion transistor could morph into a 500 billion transistor computer chip. Try wrapping your mind around what such super-powerful computers might enable us to do and achieve.

Exponential growth is not simply limited to the computer and Internet industries. Data storage, bandwidth, gene sequencing and even the growth of synthetic diamonds are all expected to undergo zenzizenzizenzic progression in the coming decade.

In fact these fields are just the tip of the proverbial iceberg. According to Gartner Research, the field rapid prototype manufacturing (i.e. machines which can physically print objects) will grow more than 100-fold. And in the past year, researchers in Finland have replaced a patient’s lower jaw with synthetic material growth from stem cells; researchers at the University of Minnesota have successfully demonstrated that they could grow a beating rat heart from living cells; and self-driven robotic cars have successfully navigated through an urban environment. Progress in these fields will not stop.

It has been said that the future is here, it is just unevenly distributed. This is true and if one studies the amazing technological progress occurring in information technology, biotechnology, nanotechnology, neurotechnology and robotics and then applies the power of zenzizenzizenzic to these advances, it is easy to understand how it would behoove us all to reconsider Carl Sagan’s sage advice and begin preparing ourselves for a great deal of unlearning. 

Unlearning Danger for Your Child's Sake

"There is a huge disconnect between how dangerous people think things are and how danger the world really is. There's this mass, institutionalized paranoia about child safety."

The above quote is from Lenore Skenazy, the author of Free Range Kids and, more notably, the mother who allowed her 8-year old son to take the New York City subway home with, gasp, no parently supervision.

I have written and spoken before about how people do an awful job of adequately and realisticly assessing risk (we tend to see risk where there is little and ignore situations where risk should be a real concern). As such, I'll admit  that I strongly agree with Skenazy's viewpoint.

In that spirit, I'd like to re-run a piece I posted on my other website, www.jumpthecurve.net, last year. I'm convinced the world would be a better place and our children will actually be safer if we unlearned our irrational fears and, instead, give them the freedom to discover the world -- and its inherent risks -- on their own.

Enjoy.

As a parent, I see preparing my children for the future as my main responsibility. To do this job, I employ a lot of tactics: I encourage them to play; I try to foster their creativity; and I also allow them to fail.

With this in mind, I’d invite you to watch this 9-minute video by Gever Tulley entitled “5 Dangerous Ideas for Kids.” Most of the lessons might seem counter-intuitive—especially in today’s hyper-cautious environment whereby we try to protect our kids from every possible danger—but the logic behind each idea is solid. If you don’t have the time to watch the video, the five ideas (actually there are six) are:

1. Play with Fire
2. Own a Pocket Knife
3. Throw a Spear
4. Deconstruct Appliances
5. Break the Digital Management Copyright Act
6. Drive a Car

The beauty of all of these lessons (with the exception of the sixth) is that they are also applicable to adults who are trying to better prepare themselves for the future. (Note: If you’re an adult and you’re looking for a replacement for the sixth idea, I’d suggest that you allow a robot to drive the car for you.)

Unlearn Your View of Colors ... and People

Take a look at the picture to the right. Do you see the green and the blue spirals? Amazingly, they are both the same color. (For a detailed explanation read this  article in Discover Magazine.)

This optical illusion, which shows how our mind uses neighboring colors to make inferences about other colors, is a wonderful metaphor for how many of us -- myself included -- often mistakenly make inferences about people based on associations rather than people's true characteristics.

For example, in a recent exercise, Joshua Bell, one of the world's greatest classical concert violinists, dressed in blue jeans and played in the tunnel of a Metro (i.e. subway) station in Washington, D.C. Not surprisingly, not a single person stopped to listen to him. That same evening, however, thousands of people were willing to spend hundreds of dollars to hear him play.

My point is that all too often we fail to see or appreciate true beauty -- or truth, if you will -- because we are blinded by our environment. It is something we will all need to unlearn if we wish to see the world not as we see it -- but as it really is.

Related Posts

Unlearning 101: Lesson #10 -- Change Your Worldview

Train Your Mind to See Different Points of View

The Air Force Needs to Unlearn

The Wall Street Journal had an interesting review of the book, Mr. Gatling’s Terrible Marvel, a while back. It is a history of the world’s first machine gun. Interestingly, although the gun was patented during the Civil War and Mr. Gatling urged the Union Army to adopt it—arguing that it would "save lives, wounds and sickness, by lessening the numbers subjected to the perils of war"—nobody listened. It wasn’t until the Spanish-American War in 1898—almost 40 years after its invention—that it was first deployed.

This little lesson in history is directly applicable to a new, modern weapon—unmanned aerial vehicles (UAVs). Last year, Secretary of Defense Robert Gates complained that the Air Force was not adopting the use of UAVs—also known as drones—fast enough. He further argued that the Air Force generals who weren’t adopting the technology were unnecessarily putting airman’s lives at risk.

Gates is right. UAV’s can now fly for hours over enemy territory and, if necessary, deploy an assortment of weapons. Perhaps it is time that a great many Air Force generals who learned the importance of using fighters and bombers to engage and suppress the enemy now “unlearn” that behavior. The sooner they do, the fewer American lives the military will unnecessarily put at risk.

(Note: As a former military officer, I would like to add that I hope society will eventually unlearn war as  a means of conflict resolution altogether, but until such time as an enlightened policy is achieved I think it prudent to work towards policies that prevent, reduce and minimize such conflicts.)

Unlearning "Miles Per Gallon"

According to a study by two professors at Duke University, consumers would make more informed buying decisions about automobiles if fuel efficiency information were conveyed in “gallons per mile” as opposed to “miles per gallon.”

The current “miles per gallon” standard can play tricks on people’s intuitions. The reality is that improving fuel efficiency from 10 to 20 mpg is actually a more significant savings than improving from 25 to 50 mpg—for the same distance of driving. For example, using the first example of switching from a 10 mpg vehicle to a 20 mpg vehicle, if someone were driving 100 miles the switch would result in a savings of 5 gallons. If, however, a family made the other switch (from a 25 to 50 mpg vehicle) they would only realize a savings of two gallons.

The distinction is important because while it is obvious that a 50 mpg car would be the most fuel efficient, the reality is that many people still rely (or prefer) larger SUV’s and minivans. Therefore, when in the market for a vehicle which they will actually use, they should concern themselves more with the “gallons per mile” than “miles per gallon.” (For a better understanding of this argument, I’d recommend this article.)

It will be interesting to see if buyers, the automobile industry, their advertising companies, and even environmentalists can unlearn their old focus on “miles per gallon.”

Unlearning the Need for Answers

Gregory Rodriguez recently had an excellent article in the Los Angeles Times entitled "The danger of not knowing how to inquire." In many ways, it is a positive review Andrea Batista Schlesinger's new book, "The Death of Why?" but, more broadly, the article questions American's penchant for action.

In part, this penchant for action is a product of our "desire for certainty [for] hard facts, quick answers." 

But what if there are no "hard answers"?

Batista- Schlesinger writes, "What we need to acknowledge, now more than ever, is that we do not know everything. We cannot know everything. Knowledge changes ... the future is a moving target, and the ground beneath us will never be still. The only thing we can count on to see us through an uncertain future is our ability to ask questions."

Batista Schlesinger is right. The world is moving beneath our feet; we do need to ask new questions; and we must all have a little more intellectual humility.

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The Iceberg Principle

Have Some Intellectual Humility 

Is It Time to Unlearn Calculus?

Arthur Benjamin, a professor of mathematics, argues that it is time for high school math teachers to spend less time focusing on calculus as the "summit" of a proper math education and more time on teaching people statistics and probability.

Benjamin's point is not that calculus is not important. In fact, he argues that it should still be taught. Instead, he argues that because people use -- or, at least, they should use -- statistics and probabality on a daily basis the subjects should receive a greater emphasis in high school mathematics curriculum.

I agree and we will know society has successfully achieved this goal when no one plays the lottery anymore.

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Unlearning 101: Unlearning Patterns

Unlearning Streaks as Feats of Excellence

"That randomness naturally leads to streaks contradicts people's intuition." Leonard Mlodinow

As luck (or, perhaps, random chance would have it), I was just in the process of finishing Leonard Mlodinow's excellent book, "The Drunkard's Walk: How Randomness Rules Our Lives," when I came upon this article written by him in today's Wall Street Journal. It is entitled "The Triumph of the Random" and I can't recommend it highly enough.

I say this because it will likely challenge your views of whether athletic feats of greatness -- such as Joe DiMaggio's famous 56-game hitting streak or Kobe Bryant's recent 81-point game -- are as much a matter of skill as they are random luck.

The reason this matters -- especially in the business world -- is because all too often people believe that streaks (especially smaller ones) are the result of a broader trend than they really are.

As an example, Mlodinow cites the example of a World Series match-up between a favored team and an underdog. Assuming that the favored team has a 55% chance of winning every game and the underdog only 45%, most people assume that given a seven game series the better team win will the series in an overwhelming majority of cases.

This isn't true. The underdog can actually be expected to be crowned "World Champion" 40%  of the time.  Imagine that -- forty percent of the time the "better" team won't be the winner!

The analogous situation in a business environment is that of the "focus group." All too often marketers use a relatively small sample of people in a focus group to make broader predictions about the population.

For example, the fact that 6 out of 10 people prefer a particular beverage or food product is more likely to be the result of random chance than an accurate reflection of a trend. Or the fact that your stock broker has beaten the market the last three years (or, alternatively, performed worse than the market) may have less to do with his or her financial acumen and more to do with random luck.

As I have said before, however, people are so good at seeing patterns that we often see one where none exist. This is worth keeping in mind next time you see a "hot" streak in baseball or basketball, or pat yourself on the back for having your stock portfolio outperforms the market for a couple of years in a row.

If you don't believe me, read the Mlodinow article.

Is It Time to Unlearn the Scientific Method?

Last summer, Chris Anderson, the editor of Wired, wrote an excellent article entitled "The End of Theory: The Data Deluge Makes Scientific Method Obsolete” in which he convincingly argued that massive amounts of data, in combination with sophisticated algorithms and super powerful computers, offer mankind a whole new way of understanding the world.

Anderson believes that our technological tools have now progressed to the point where the “old way” of doing science—hypothesize, model and test—is becoming obsolete. In its place, a new paradigm is now emerging whereby scientists, researchers and entrepreneurs simply allow statistical algorithms to find patterns where science cannot.

If Anderson is correct—and I am open to the idea that he could be—this will take science in a whole new direction. In short, instead of modeling and waiting to find out if hypotheses are valid, the scientific community can instead rely on intelligent algorithms to do the heavy lifting.

Before this vision can be achieved, however, it will require a great many brilliant scientists to unlearn the idea that their “model-based” method of trying to make sense of today’s increasingly complex world is the only way to search for new meaning.

The implication for a field such as biology which, as Anderson points out is actually becoming more difficult to model as we learn more about it (due to our limited understanding of how genetics, microbes, personal behavior, the environment, and a host of other factors work in partnership to determine a person’s health), could be profound. More specifically, we will be able to analyze data without allowing hypotheses (which are, perhaps, wrong) to cloud our view of what the data is really showing us.