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The Final Frontier July 6, 2017

Posted by Peter Varhol in Education, Technology and Culture.
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Yes, these are the voyages of the Starship Enterprise.  Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.

To someone of my age, this defined the possibilities of space, perhaps even more so than the Apollo 11 landing on the moon.

We failed at this, in my lifetime, to my dying (hopefully not soon) regret.  We failed, not because of a lack of technology, but because of a lack of will.  Since the 1980s, America has been looking inward, rather than reaching for the next brass ring in the universe.

Today, we have no ability to launch astronauts into orbit.  No, we don’t.  Our astronauts go into orbit courtesy of the ESA or the Russians (not sure that ESA is doing all that much any more).  I am sure many of you are pleased at this, but you miss the larger picture.

May I quote Robert Browning: “Ah, but a man’s reach should exceed his grasp, Or what’s a heaven for?”

Seriously.  Life is bigger, much bigger, than our individual petty concerns.  We may think our concerns are larger than life, but until we reach beyond them, we are petty, we are small.  Until we give ourselves to larger and more grandiose goals, we are achieving nothing as human beings.

Look at the people, throughout history, who have given their lives, willingly, in favor of a larger goal.  Not just the astronauts, but soldiers, sailors, explorers, yes, even a few politicians.

Today, my only hope is with the private companies, SpaceX, Blue Origin, Virgin Galactic, and their ilk.  They are our future.  Not NASA, or the government in any way, shape or form.  I hope with all of my heart and soul they can reach where the collective citizenry has declined to.

Set controls for the heart of the sun.

About Uber, Friction, and Life June 28, 2017

Posted by Peter Varhol in Technology and Culture.
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No matter where you are in most major or even minor cities around the world (yes, there are significant exceptions), you can pull out your smartphone, press a couple of buttons, and have an Uber taxi meet you at your location in a few minutes.  You compare the driver with the photo you received, and you have a measure of security.  The driver already knows your destination, and you know that you don’t have to pass him (or her) some cash at the end of the process.

And that’s the way it should be, in this day and age.  The technology has been there, and Uber, Lyft, and their ilk are bringing it together.

But let’s take an honest look about what we are trading off, because there are always tradeoffs.  In this case, we are trading off friction.  By friction, I mean the hassle of hailing a commercial taxi, finding the phone number and calling a taxi company, or getting to a location where taxis tend to congregate.

(And as I was told in Stockholm last month, all taxis are not created equal.  “Don’t take that one,” the bell captain at a hotel said.  “They will gouge you.”)

All of this sounds like a good thing.  But it turns out it is part of the life learning process as a person.  For the first twenty-three years of my life, I never saw a taxi, or a train, or a subway.  I grew up in rural America.  Today I am comfortable finding and navigating all of the above, in any city in the US or Europe.  Why?  Because I had to.

(And incidentally, no matter the payment method, I always tip in cash.  These folks work for a living, and deserve the discretion of how and where to report their tips.)

I have grown as a person.  That’s difficult to quantify, and certainly given a more frictionless path in the past I might well have chosen it.  But the learning process has built my confidence and yes, my worldliness.  I am more comfortable navigating cities I have never been to before.  I don’t stay in a bubble.

If you are using Uber (and Lyft) as an excuse for not interacting with others, especially others who are different from you, then you are not learning about the world, and how to interact with it.  And as your life winds down, you may come to regret that.

The Future is Now June 23, 2017

Posted by Peter Varhol in Algorithms, Technology and Culture.
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And it is messy.  This article notes that it has been 15 years since the release of Minority Report, and today we are using predictive analytics to determine who might commit a crime, and where.

Perhaps it is the sign of the times.  Despite being safer than ever, we are also more afraid than ever.  We may not let our electronics onto commercial planes (though they are presumably okay in cargo).  We want to flag and restrict contact with people deemed high-risk.  We want to stay home.  We want the police to have more powers.

In a way it’s understandable.  This is a bias described aptly by Daniel Kahneman.  We can extrapolate from the general to the particular, but not from the particular to the general.  And there is also the primacy bias.  When we see a mass attack, was are likely to instinctively interpret that as an increase in attacks in general, rather than looking at the trends over time.

I’m reminded of the Buffalo Springfield song: “Paranoia strikes deep, into your lives it will creep.”

But there is a problem using predictive analytics in this fashion, as Tom Cruise discovered.  And this gets back to Nicholas Carr’s point – we can’t effectively automate what we can’t do ourselves.  If a human cannot draw the same or more accurate conclusions, we have no right to rely blindly on analytics.

I suspect that we are going to see increased misuses of analytics in the future, and that is regrettable.  We have to have data scientists, economists, and computer professionals step up and say that a particular application is inappropriate.

I will do so when I can.  I hope others will, too.

Learning How to Learn June 21, 2017

Posted by Peter Varhol in Education, Technology and Culture.
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One of the significant values I got out of my college experiences was a foundation whereby I could build on with lifetime learning.  I’m not quite sure how it happened, but my life outlook seems to have combined a love of learning with the ability to build upon that initial foundation.  A part of it, I’m sure, is that I read a lot and forget little, but something more happened to enable me to readily integrate new knowledge in both the social and natural sciences into a growing world view.

Yes, I know, that is gobblety gook, but I learned that studying social science for my BA.  Gobblety gook was the primary language of communication when I was taking social science.

Nonetheless, it serves to draw a distinction between singing Kumbaya and preparing yourself for a lifetime in the real world.  Kumbaya may help us connect with others in the moment, but does little to prepare us for the future.

It goes beyond how do we learn.  It asks the question “How do we learn to learn?”  I did poorly in college in my freshman year (no, I was not a particular partier).  Rather, I tried valiantly to understand concepts, as my professors insisted.  When I finally realized they really wanted me to memorize facts, I did so voraciously, and averaged superior grades for the rest of my college career.

Somewhere along the way to memorizing facts, I would like to think that I learned how to learn, over the course of a lifetime (38 years after college and counting).  But I can’t apply my own individual circumstances to any proven curriculum.

But I have to think there is a way, perhaps this way.  Old fashioned, perhaps, but really, how often do our intellectual peers think about how to think?  Can we learn how to think by focusing deeply on a relatively few classic volumes?

I don’t know.  But to be fair, almost anything has to be better than what the vast majority of our higher education curricula are doing today.

Analytics Don’t Apply in the Clutch June 21, 2017

Posted by Peter Varhol in Architectures, Strategy, Technology and Culture.
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I was 13 years old, at Forbes Field, and rose with the crowd as Roberto Clemente hit a walk-off home run in the ninth inning to win an important game in the 1971 World Series hunt.  Clemente was a very good hitter for average, but had relatively few home runs.  He delivered in the clutch, as we say.

Moneyball ultimately works in baseball because of the importance of individual achievement in the outcome of games, and the length of the season.  162 games enables carefully thought out probabilities to win out over the long haul.

But teams practicing Moneyball learned that analytics weren’t enough once you got into the postseason.  Here’s the problem.  Probabilities are just that; they indicate a tendency or a trend over time, but don’t effectively predict the result of an individual event in that time series.  Teams such as the Boston Red Sox were World Series winners because they both practiced Moneyball and had high-priced stars proven to deliver results when the game was on the line.

Machine learning and advanced analytics have characteristics in common with Moneyball.  They provide you with the best answer, based on the application of the algorithms and the data used to train them.  Most of the time, that answer is correct within acceptable limits.  Occasionally, it is not.  That failure may simply be an annoyance, or it may have catastrophic consequences.

I have disparaged Nicholas Carr in these pages in the past.  My opinion of him changed radically as I watched his keynote address at the Conference for the Association of Software Testing in 2016 (this talk is similar).  In a nutshell, Carr says that we can’t automate, and trust that automation, without first having experience with the activity itself.  Simply, we can’t automate something that we can’t do ourselves.

All events are not created equal.  Many are routine, but a few might have significant consequences.  But analytics and AI treat all events within their problem domain as the same.  The human knows the difference, and can rise to the occasion with a higher probability than any learning system.

Learning systems are great.  On average, they will produce better results than a human over time.  However, the human is more likely to deliver when it counts.

On Technology, Discovery, and the Modern World June 20, 2017

Posted by Peter Varhol in Technology and Culture.
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I have a book that I bought at a used bookstore in Meadville, PA, circa 1976.  It’s titled The History of 19th Century Science, and is full of stories of scientists in the 1800s and their discoveries in fields such as biology, geology, and chemistry.  That century really was the Golden Age of science and discovery in the modern era.  The advances in science during the latter part of the 1800’s was really amazing.

(I paid the $1 written on the flyleaf, although the proprietor groused that it was mis-marked and probably worth more.  One of these days I’ll have to find out if it’s worth anything.)

But original science today is usually a very different beast.  Much of science, especially the physical sciences, are funded at millions of dollars, with large teams pursuing, quite frankly, is often incremental knowledge.

And that’s what many scientists have come to expect of the fruit of their labors.  In an era where scientists seem to be satisfied with very modest advancements over the course of decades of research and millions of dollars, there remains the opportunity to do significant and important work.

The real problem is that taking a mental leap is not the safe way to do science.  And if you are trying to establish a life career as a research scientist, you won’t take chances, so you won’t look for a breakthrough.

If you are a scientist, maybe you should look for breakthroughs more often.

Uber Must Die June 6, 2017

Posted by Peter Varhol in Technology and Culture.
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I dislike saying that, and I will get there in an indirect way.  I have a friend whose father recently passed, and as the executor she is required to bring his financial affairs to a conclusion.  She is dealing with utilities, banks, and a variety of other contact points, increasingly frustrated at the difficulty of closing, cancelling, or transferring accounts.  Her own company actually shut off the electricity to his condo, where she was now living.

The only exception is Fidelity, for retirement accounts.  She called Fidelity, expecting more of the same.  She got actionable advice, and the names and direct phone numbers of people who were willing to help her still further.  As a result, she wants to move not only his but all of her retirement assets into Fidelity accounts.

I would like to think that we tend to gravitate to companies that have good reputations, and provide good customer service.  Uber seems to be different, in that its users are able to divorce their use from the service from their understanding of the underlying company.

This is very wrong, of course.  I simply lack the fundamental understanding of why people frequent a company whose founder and CEO talked of starting an Uber-based dating service called “Boober.”

If you stand for anything at all, you must rail against this.  Yet people who use Uber don’t, oddly.

In fairness, Uber has done some things right.  It has provided a way to hail a taxi (even though that is still not what they claim to be) without knowing the local number of a taxi company.  People, oddly, don’t want to actually contact someone to get their ride.  But if you are a Uber user, you must know that your ride is subsidized, at around 60 percent, and someday they will have to significantly increase their prices.  Are you still onboard?

The problem is that the VCs are so financially invested that they can’t let it die, and will do anything to take it to an exit strategy.  So the VCs can’t get out.  Guess what?  You can.  Delete the app.  Take the additional friction of finding a cab.  You will grow as a human being.

But Uber is poison.  Beyond an initial goal to upend the taxi industry, it has no redeeming value.  Most of us care about the companies we do business with.  If you don’t care about Uber, you are very wrong.

We Are Seeing the World Change in Our Lifetimes May 19, 2017

Posted by Peter Varhol in Technology and Culture.
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I know that sounds like hyperbole, but that doesn’t make it any less true.

Let me give an example. When I was growing up, we had a milkman, who delivered milk to our doorstep in a dilapidated old truck early in the morning, twice a week (the milkman was my uncle).

Today, your smart refrigerator can monitor your milk consumption and automatically order milk to be delivered with your dinner that evening. By drone.  You have milk!

But the pace of change has accelerated immensely recently, to the point where we are talking seriously about driverless cars, autonomous vehicles in general, and robots or other AI performing both labor and professional tasks.  No one beyond a few specialists were having these discussions five years ago.

I am in awe at living in this era. The world has never seen anything like this, and we are at the dawn of, well, something.  I know it will be different; I hope it’s good.

Tens of millions of traditional lifetime jobs will disappear in the next decade (sorry, Mr. Trump), and we will never see their likes again.  I am confident that others will arise, in time, but it will be a messy at least several years.

Work in general is changing. There will still be coal miners, but they will be in office cubicles in Des Moines, manhandling joysticks to control the robots a thousand miles away and a thousand feet underground.  I especially liked this one, where London City Airport is basing its air traffic controllers 50 miles away and letting them see and respond to traffic by TV, GPS, and ground systems.

The problem is that we are lousy at predicting the future. We don’t know how it’s going to turn out.  We can’t know with any certainty what will last, for the moment, and what will fall ignobly.

Many will survive and even thrive. Many will not.  The revolution has started, and I am excited to be a part of it; I simply hope that I am not the first up against the wall.