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What Brought About our AI Revolution? July 22, 2017

Posted by Peter Varhol in Software development, Software platforms, Algorithms.
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Circa 1990, I was a computer science graduate student, writing forward-chaining rules in Lisp for AI applications.  We had Symbolics Lisp workstations, but I did most of my coding on my Mac, using ExperList or the wonderful XLisp written by friend and colleague David Betz.

Lisp was convoluted to work with, and in general rules-based systems required that there was an expert available to develop the rules.  It turns out that it’s very difficult for any human expert to described in rules how they got a particular answer.  And those rules generally couldn’t take into account any data that might help it learn and refine over time.

As a result, most rules-based systems fell by the wayside.  While they could work for discrete problems where the steps to a conclusion were clearly defined, they weren’t very useful when the problem domain was ambiguous or there was no clear yes or no answer.

A couple of years later I moved on to working with neural networks.  Neural networks require data for training purposes.  These systems are made up of layered networks of equations (I used mostly fairly simple polynomial expressions, but sometimes the algorithms can get pretty sophisticated) that adapt based on known inputs and outputs.

Neural networks have the advantage of obtaining their expertise through the application of actual data.  However, due to the multiple layers of algorithms, it is usually impossible to determine how the system arrives at the answers it does.

Recently I presented on machine learning at the QUEST Conference in Chicago and at Expo:QA in Spain.  In interacting with the attendees, I realized something.  While some data scientists tend to use more complex algorithms today, the techniques involved in neural networks for machine learning are pretty much the same as they were when I was doing it, now 25 years ago.

So why are we having the explosion in machine learning, AI, and intelligent systems today?  When I was asked that question recently, I realized that there was only one possible answer.

Computing processing speeds continue to follow Moore’s Law (more or less), especially when we’re talking floating point SIMD/parallel processing operations.  Moore’s Law doesn’t directly relate to speed or performance, but there is a strong correlation.  And processors today are now fast enough to execute complex algorithms with data applied in parallel.  Some, like Nvidia, have wonderful GPUs that turn out to work very well with this type of problem.  Others, like Intel, have released an entire processor line dedicated to AI algorithms.

In other words, what has happened is that the hardware caught up to the software.  The software (and mathematical) techniques are fundamentally the same, but now the machine learning systems can run fast enough to actually be useful.

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.

Why I Have to Keep Task Manager Running in Windows July 2, 2017

Posted by Peter Varhol in Software platforms.
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Over the last year or so, my daily personal laptop has been running slower and slower.  For a variety of reasons, Windows performance and reliability tends to degrade over time.  Memory especially, but also disk and CPU have been pegging at 100 percent all too frequently.  I suppose I could wipe the system and start again from scratch, but that’s also a good indication that it’s time to get a new laptop.

I’ve upgraded to a new laptop, a midrange Core i5 quad-core system with 8 GB of RAM, running Windows 10.  That will fix my memory, CPU and disk problems, I thought.

Wrong.  My system still hung regularly.  So I started investigating in more detail.

Chrome, for one very big reason.  I will typically keep four or five tabs open, and it doesn’t take long for one or two of them to take up well over 2GB of memory.  And I’m talking about very commonly used sites, like weather.com, fitbit.com, or cnn.com.

While I’ve read several reasons (some of which are contradictory or don’t reflect my situation) why Chrome consumes memory like a drunken sailor, there doesn’t seem to be a whole lot to do about it.  Some talk about disabling add-ins; the only add-in I have running is Flash, and that is still required by many commercial websites (and crashes just as frequently).

Chrome has also been known to consume huge amounts of CPU and disk bandwidth.  I haven’t really read anything actionable about what to do here.

So I keep Task Manager open.  When a Chrome tab starts to misbehave, there is no alternative but to kill the process.

But wait!  It’s not just Chrome!  In Windows 10, there’s also this process called Microsoft Telemetry Service (and yes, it is a Windows Service).  I found this service using 99 percent of my CPU on more than one occasion.  What does Microsoft Telemetry Service do?  It sends use information from your computer to Microsoft.  Not just error information; use information.

It is enabled by default.  If you disable it, some of the Windows updates will re-enable it without telling you.

My very strong recommendation is to disable it and the horse it rode in on.  I guess this is what we deserve in the Facebook era, where we have no privacy.

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.

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.