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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.

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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.

Has Moneyball Killed Baseball? June 20, 2017

Posted by Peter Varhol in Education, Publishing, Strategy.
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Moneyball was a revelation to me.  It taught me that the experts could not effectively evaluate talent, and opened my own mind to the biases found in software development, testing, and team building.  Some of my best conference presentations and articles have been in this area.

But while Moneyball helped the Oakland Athletics, and eventually some other teams, it seems to be well on its way to killing the sport.  I’ve never been a big sports fan, but there were few other activities that could command the attention of a 12-year old in the late 1960s.

I grew up in the Pittsburgh area, and while I was too young to see the dramatic Bill Mazeroski home run in the 1960 World Series, I did see the heroics of Roberto Clemente and Willie Stargell in the 1971 World Series (my sister was administrative assistant at the church in Wilmington NC where Stargell had his funeral).  I lived in Baltimore where the Pirates won a Game 7 in dramatic fashion in 1979 (Steve Blass at the helm for his third game of the series, with Dave Guisti in relief).

But baseball has changed, and not in a good way.  Today, Moneyball has produced teams that focus on dramatic encounters like strikeouts, walks, and home runs.  I doubt this was what Billy Beane wanted to happen.  That makes baseball boring.  It is currently lacking in any of the strategy that it was best at.

As we move toward a world where we are increasingly using analytics to evaluate data and make decisions, we may be leaving the interesting parts of our problem domain behind.  I would like to think that machine learning and analytics are generally good for us, but perhaps they provide a crutch that ultimately makes our world less than it could be.  I hope we find a way to have the best of both.

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.

Higher Education Ignores Results It Doesn’t Like June 7, 2017

Posted by Peter Varhol in Education.
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I am a strong believer in higher education.  At the same time, I recognize that what passes for higher education in some classes and even entire universities is a farce that suffers from lack of, well, caring.

In particular, some universities claim that they provide a high quality education by fiat alone, and discount or ignore evidence to the contrary.  And no one is willing or able to hold them accountable.  This WSJ article (paywall) notes that those schools whose students don’t show improvement in critical thinking discount the value of the test and stop administering it.  And no one calls them to task.

But the cry from my own experiences in academia still rings in my ears – “We don’t have customers, we have students!”

This kind of close-minded thinking is all too common in our higher education.  We like to think that educated people are by nature intelligent and thoughtful human beings.  Too often, they are just the opposite.

Further, parents lack the information or ability to critically evaluate the education alternatives available to their children, and defer to what those children feel comfortable with.  That’s not necessarily a bad thing, but it does mean that parents may be shelling out tens or hundreds of thousands of dollars for a subpar education, or even no education at all.

To be fair, your experience in college is largely what you make of it.  You can go to Harvard, and party your way through with little impact on your ability to receive a degree.  You can go to Plymouth State, apply yourself, and obtain the foundation for a successful lifetime of learning and critical thinking.  In that sense, it doesn’t matter where you go.

But the American family is largely a poor consumer of higher education.  We spend more time and effort buying a car than we do buying an education, yet the implications of a poor choice are far more significant with the latter.  I wish we would find a way to penalize universities that make obviously unsupported claims on their quality, but regrettably, we genuflect to higher education so instinctively that we as consumers just don’t imagine going there.

Can We Level Set? June 6, 2017

Posted by Peter Varhol in Uncategorized.
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I am reading some incredible things about self-driving cars, machine learning (which I know something about), and a broad variety of other fanciful technologies.  To be fair, self-driving cars are a ways away, for a variety of reasons, Uber self-flying aircraft are a pipe dream, and intelligent machines taking over jobs are largely still off in an indeterminate future.

So, just where is the world on technology?  I am not entirely sure, but I would like to offer some opinions.  First, I am fairly technology savvy, although I am rarely a first adopter.  I am certainly not a last adopter.

My Subaru is going on 19 years old, and still starts reliably.  It will be replaced this year, I promise.  But I will not have a self-driving car in my lifetime.  And if you are middle age or beyond, neither will you, despite what we are fed for what passes for news these days.

My new Dell, all four cores and 8GB of RAM, still hangs on web browsing.  If you think your car is going to seamlessly communicate with the Internet and other cars in real time (do you even know what real time means?), you are very wrong.  Your NetFlix movie streaming probably doesn’t even give you a high-def video reliably.  Be honest here.  I am told I get 30Mbps, and it is slow because of what we are sending.

Speaking of which, I have a TV that is about 20 years old.  It has a tube; does anyone remember what that is?  But I get the stuff I need from Xfinity.

We are constantly fed a farce of new and even better.  A few of us buy the latest and greatest, and think that is where the world is.  If you are a constant first adopter, more power to you.  You spend money on things that you don’t need, and probably don’t even use to their potential.  But hey, it’s your money.

My point is that what you spend on being a first adopter today is pretty much wasted.  In 30 years, we may see a completely controlled highway system filled with self-driving cars.  It won’t happen tomorrow in a way that will alleviate the pain of driving or the traffic issues of today.  Personal aircraft won’t happen in anyone’s lifetime, despite Uber’s big conference on the topic.  Facebook will not dominate our lives.

If we think otherwise, we are deluding ourselves.  I am a big believer in technology.  I think we are making the world better.  And I think that many of the things that are going on are really great.

But they are not right around the corner.