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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.