Unlearning 101

The Lesson of the Lily Pad

I was recently asked by a leading nanotechnology consultant, Rocky Rawstern, if I could   say anything on nanotechnology to a wider audience but I had to keep it under 250 words, what would I say. Here was my response:

To those who don’t believe nanotechnology will change the world in the near future just because it hasn’t accomplished much in the last 20 years, consider this little quiz: If a single lily pad began doubling on a pond on the first day of June and doubled each day thereafter until the entire pond was covered by the end of the month, on Day 20 what percentage of the pond would be covered with lily pads?

The answer is one-tenth of one percent. That’s right, .1%! What happens over the next 10 days is a little short of amazing—the entire pond gets covered. Such is the nature of exponential growth.

Now, advances in nanotechnology aren’t quite experiencing exponential growth but they are close and over the course of the next decade nanotechnology’s impact on material sciences, medicine, and energy are going to be—like the lily pads’ spread over pond in the last few days— extraordinary.

Unlearning & the Rise of the Supercomputer

Marshall McCluhan once famously said, “First, we shape our tools and then our tools shape us.” As a professional futurist, I have been trying to explain how some of our tools are getting exponentially more powerful. In turn, these tools are accelerating the advances that will soon shape our future and, by virtue of their findings, necessitate unlearning. Nowhere is this more true than in the field of supercomputers. 

According to this ComputerWorld article, supercomputers with 100 million cores and capable of one quintillion calculations per second—or 1,000,000,000,000,000,000 for those of you counting at home—will arrive around 2018. This growing processing power is important because it will help innovative researchers, scientists and entrepreneurs design next-generation materials, drugs, bio-fuels, batteries and much, much more. 

 One area merits special attention and that is in the area of how supercomputers are yielding new insights about the brain. As scientists learn more about how the brain really operates, I am convinced that we will need to unlearn much of what we now think we "know" about the brain. 

To better understand supercomputers amazing power, I invite you to watch this two-minute video I put together last year. Although be forewarned, one quadrillion is already rather quaint:

Unlearning Prediction

"Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.” Lao Tzu

This famous quote was uttered over 2500 years ago and may strike some people as odd that, as a professional futurist, I have chosen to highlight it.

My rationale is two-fold. First, as I have said many times before, the chief responsibility of a futurist is not to “predict” the future but rather outline a range of future possibilities. Second, and more important, I have highlighted the quote because it is true.

If you have not already read Nassim Taleb's outstanding book, The Black Swan: The Impact of the Highly Improbable, do so as soon as possible. On Page 177 of the book, Taleb has an illustration of the scattering effect which artfully explains why predicting the future—especially for longer time frames—is virtually impossible.

Because I can’t reprint the figure here, I am left with Taleb’s analogy for understanding the scattering effect: the prediction of a billiard ball.

Assuming one has knowledge of the location of every ball and the speed and accuracy of the cue ball, it is relatively easy to predict the movement of the ball after the first shot. A person might even expect that, due to the growing computational power of computers, the second, third and fourth degree movements can be estimated with great accuracy. The problem, however, grows increasingly complex with each subsequent movement and after the ninth movement the gravitational pull of a person standing at the next pool table exerts enough of an effect to alter the trajectory of the ball. And after 56 movements even the smallest particle on the outer edge of the universe will effect the trajectory!

The bottom line is this: There are simply too many factors to consider when contemplating the future 20, 30, 50 or 100 years out. (Even a small effect today can have outsized implications 50 years hence). The situation, of course, becomes even more complicated because of Taleb’s main thesis which is that “Black Swans”—described as “high impact, low probability” events—tend to shape the future more than “expected” trends.

Therefore, whether a future “Black Swan” takes the shape of a pandemic, an asteroid, an E-bomb, a rogue terrorist attack, a North Korean or Iranian nuclear attack, an unexpected breakthrough in quantum computing or synthetic biology, or, more likely, some “unknown unknown;” the future will be difficult too predict because we don’t have—and won’t ever have—enough knowledge about the future.

Unlearn By Dispelling Old and New Ignorance

In his famous speech at Rice University where he declared that it was America’s intention to put a man on the moon by the end of the decade, President Kennedy said “the greater our knowledge increases, the greater our ignorance unfolds,” adding that “the vast stretches of the unknown and the unanswered and the unfinished still far outstrip our collective comprehension.”

 

Kennedy went on to offer a historical perspective for the magnitude of change society had experienced over the short course of human history. He asked his audience to condense the 50,000 years of man’s recorded history into the span of fifty years. Under this scenario, Kennedy noted that not much happened for the first forty years. Ten years ago, man emerged from his cave, and only five years ago did he learned to write. Christianity appeared two years ago, the printing press this year, and just two months ago the steam engine appeared. Last month electric lights, telephones, automobiles, and airplanes became available, and only last week did we develop penicillin, television, and nuclear weapons. To reach “the stars before midnight tonight,” Kennedy then poignantly added that Americans would have to “dispel old [and] new ignorance.”

 

Since achieving Kennedy’s goal in 1969, progress has continued exponentially. (Ironically, perhaps, with the exception of space exploration which, as Monday's 40th aniversary of the moon landing reminds us, has not made much progress.) Taking his historical analogy a little further, however, in the last proverbial "day" computers, biotechnology, the Internet, and the sequencing of the human genome have all appeared on the scene.

 

What Kennedy's analogy reminds us it that will need to continue to “dispel old ignorance” -- or continuously unlearn if you will -- only on a faster scale.

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. 

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.

What If Society Has to Unlearn Death?

In 1899, just a few years before the Wright brothers achieved their historic accomplishment, Lord Kelvin—then one of the world’s brightest men and most accomplished scientists—declared heavier than air machines to be “impossible.”

He was wrong.

To add insult to injury, Lord Kelvin was proved wrong by a pair of bicycle repairmen from Dayton, Ohio.

A few years ago, a relatively unknown computer scientist, Aubrey de Grey, declared that aging should not be viewed as something which will necessarily ultimately result in death. Rather, he theorized that aging is a disease and should instead be treated as such.

The outcry from the scientific community was similar to Lord Kelvin’s reaction to human flight. One group of scientists even declared that de Grey’s idea was “so far from plausible that it commands no respect at all within the informed scientific community.”

Well, according to this article in Wired, the idea is now beginning to gain some acceptance within scientific circles.

To be sure, society is still a long way from de Grey’s goal of ending aging but, as I have written before, I’d encourage people to not dismiss the idea entirely. For if he is right, it will require society to unlearn a great many ideas which it now holds as dear.

In fact, the scale of unlearning our current paradigm of “death as an inevitability” could make other past historic paradigm shifts—such as the idea that the earth is not at the center of the universe (an idea for which Aristarchus was run out of Alexandria and Galileo was forced to recant under edict of the Catholic Church) or Darwin’s theory of evolution—look like child’s play.

Related Post

Unlearning Disease

Why the Healthcare Industry Must Be Prepared to Unlearn

In my many talks, presentations and speeches on the future of health care, one of the things that I really try to impress upon my audiences is the importance of unlearning—that is letting go of old knowledge or old ways of doing business. This is an incredibly hard thing to do, but if one wants to prosper in the Exponential Economy it is a skill that all of us will need to practice with increasing frequency.

To this end, I’d like to share two articles. The first is from NewScientistTech and it discusses how studies have found that surgical residents performed better during simulated surgery after playing on a Wii console. Now, the notion that a video game might help improve a surgeon’s performance might seem a little counter-intuitive, but as ever more surgeries become laproscopic and microscopic in nature, the dexerity of the surgeon is going to become even important than it already is. The challenge that this prsents is that old surgeons (as well as parents who want their children to become surgeon) might have to unlearn their bias against video games. In this case, video-gaming skills might actually make residents better surgeons in the near future.

On a slightly different level, this second report suggests that doctors might be able to detect cancer by the “feel” of the cancer cell. According to a recent study, cancer cells, in some situations, are softer than healthy cells. If true, this might require doctors to unlearn their current and preferred methods for detecting and testing for cancer.

Unlearn the Future, Today

Effective today, June 8, 2009, General Motors and Citigroup are no longer components of the Dow 30. In and of itself this news has no great relevance, but it does highlight how fast today’s economy continues to change. As I mentioned the other day on my other blog (www.jumpthecurve.net), many people think that "tomorrow will be pretty much like yesterday.” This simply isn’t true.

One strategy I have advocated for unlearning the future is to "develop a future bias.” As a thought exercise and to help you unlearn, I’d like to suggest a second exercise: review the members of the current Dow 30 and imagine which company will be the next to be removed from the list and explain why it might go. (For extra credit, I invite you to imagine which company might replace it.) The purpose of this exercise is to force you what of your comfort zone and come face-to-face with the reality that “the only constant is change” and unlearning is a critical skill for the future. (Also see "The Future Requires Unlearning.)

To help you get started, here are some possibilities. Advances in nanotechnology could cause many of the materials Alcoa makes to become either irrelevant or obsolete, and the company will be replaced by a nanomaterial company. Or perhaps Caterpillar will not remain competitive due to advances in robotics and it will be replaced by iRobot. A third alternative is that intense competition from Nokia will wreak havoc on both AT&T and Verizon and they will be replaced on the Dow 30 by Google, which may continue to make strides in the communication field with its new Android platform. A fourth possibility is that new advances in biotechnology will render many existing drugs obsolete and Pfizer will be replaced on the Dow 30 with a biotech company such as Genentech.

Really, there is no shortage of possibilities but one of the best ways to prepare for the future is to simply acknowledge that it is going to change. And, as Darwin said: “It is not the strongest of a species that survives, nor is it the fittest; it is those that can adapt the fastest.” Are you and your organization prepared to adapt -- and unlearn? Quickly?

If not, you could go the way of General Motors.

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Unlearning "Land Bridges": A Lesson for Scientists

In relative terms, the field of plate tectonics is still fairly new -- becoming widely accepted only in the mid-1960’s. The idea that massive continents could have drifted apart over millions of years was, however, first expounded on by an amateur American geologist, Frank Bursley, in 1908. Bursley was struck by how the shape between the west coast of Africa and the east coast of South America looked as though they could fit together like two pieces of a jigsaw puzzle, and suggested the movement of the continents might also explain the formation of mountain ranges. Bursley provided little evidence and his idea was soon – and easily – dismissed by the scientific establishment who had little time for the “strange” theories from amateurs.

 

A few years later Bursley’s idea was picked up by a German meteorologist, Alfred Wegener, who was disturbed by the many animal and fossil anomalies which didn’t fit conveniently into the day’s existing theory of how the earth was formed. Again, it was dismissed. This time because the idea came from an outsider – a meteorologist.

 

And what precisely was the leading theory used by geologists to explain how the exact same fossils of animals and plants could have existed on the opposite sides of different oceans? The answer was “land bridges” -- mysterious strips of land, for which no evidence existed, but miraculously allowed animals to peacefully and successfully meander thousands of miles across vast expanses of the world’s oceans. For example, when an ancient horse named Hipparion was found to have lived in both Florida and France, geologists drew a land bridge across the Atlantic Ocean to explain away this disturbing discrepancy. Soon, a variety of other land bridges begun to spring up and populate the world of geological science in order to explain everything from how tapirs existed in both Southeast Asia and South America at the same time to how snails could turn up in both Scandinavia and New England.

 

Surprisingly, and in the face of a complete and utter lack of evidence, land bridges remained the geological orthodoxy for the next 50 years. In 1944, a British geologist, Arthur Holmes, wrote a book entitled Principles of Physical Geology elaborating on Wegner's theory but it was dismissed by one reviewer who even had the temerity to warn that Holmes presented his arguments so coherently that they might actually be believed by students! In 1955, no less a figure than the esteemed Albert Einstein wrote a ringing endorsement for a book which all but ridiculed the ideas of Wegener and Holmes.

 

And in 1964, in the face of mounting evidence, a Canadian geologist by the name of Lawrence Morley wrote a paper explaining how magnetic studies of the floor of the Atlantic Ocean were spreading in the exact motion prescribed by the theory of “continental drift.” Morley’s paper was abruptly and rudely dismissed by the editor of the prestigious Journal of Geophysical Research with this now infamous note: “Such speculations make interesting talk at cocktail, but it is not the sort of thing that ought to be published under serious scientific aegis.”

 

Later that year at a conference of the Royal Society the tide finally began to turn but it wasn’t until 1968 when the same publication, which had rejected Morley’s paper only four years earlier, published the article which gave the science of plate tectonics its name.

 

The story serves as a wonderful reminder to those scientists who are quick to dismiss ideas from amateurs, outsiders, and unconventional thinkers. To this end, I invite you to watch this 60 Minutes segment on “Cold Fusion” and pay special attention to the outright dismissal of the idea by some of today’s leading experts. Could they be wrong? I don’t purport to know the answer but history suggests that these scientific experts should at least entertain the notion that they might be wrong.

 

The same is true of the theories of Aubrey de Grey, an outsider from the field of computer science, who is strongly challenging today’s conventional wisdom on the “science of aging.”