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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.

Please Explain to Me Why Uber Isn’t Toast May 5, 2017

Posted by Peter Varhol in Software development, Technology and Culture.
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Uber is in the process of transforming the taxi industry. In general, that’s a good thing.  Mostly (more on that in a later post).

Everything else about Uber is a very bad thing.

First, it is about professional drivers, not ride sharing. Anyone who hasn’t figured that out by now needs to go directly to jail, and not pass Go.  When was the last time you personally shared your car with strangers, seriously?  This is absolutely not about ride sharing, and you know that, despite the drivel coming out of the company.

Second, it is about treating their drivers as poorly as possible, but keeping them onboard until they can get to driverless vehicles. Its CEO has already told us all how he treats his drivers.  All drivers will be jettisoned at the moment driverless cars become viable.  And, yes again, this is about professional drivers, not ride sharing.

Third, it is about a scofflaw culture that operates illegally, then claims it is misunderstood, or that laws are simply things that get in its way, or something that makes it superior.

Last, it is an employer that celebrates the bro culture, that takes everything that it can from its employees and delivers nothing in return, especially those who are not white, male, and affluent. Especially affluent.  Except for its drivers.  Let’s keep them poor and hungry.  Until the time we cut them all loose.

I realize that none of this is a damnable business problem (sometimes business itself is damned). But it should be, and it will be, in the not-to-distant future.

For you VCs out there, I fully appreciate how Uber is upending an industry. But it is poison as a company and an investment.  Get out if you can; no, get out at any cost.

About Licenses, Certifications, and Tech Jobs April 14, 2017

Posted by Peter Varhol in Education, Software development, Technology and Culture.
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As an academic, 25 years ago, I postulated to my students that software developers would require certifications and licenses at some point in time to pursue their craft. I was widely ridiculed at the time, so I would like to revisit that position today.

First, I want people to understand that I have no particular qualifications to write on this topic (that is ironic, based on the sentiment of this post).

We are facing two forces here. One is that innovation comes from at least partly those who have breakthrough ideas, from any field, without necessarily having formal training in that field.  While certainly true in software, I would imagine it true in other professional fields as well.

The second is that we as a society are increasingly depending upon software, and in particular software working correctly. This means we are vitally interested in having people who are working in that field are in some way qualified to do what they do.

And what does that mean? As in other professional fields, it means that we have studied formally, taken tests, and achieved a level of competence that is quantitatively identifiable and measureable.  In other words, we have a degree in the field, and we have passed one or more tests.

In the late 1980s, I worked for a defense contractor who was required to assure the DOD that its employees all had technical degrees. At that time, my MS in applied math qualified in that regard, so I passed muster.  Other long-time employees did not.  Did that make me better than them?  I don’t think so, but it made me more credentialed.

It has gotten worse since then. As we have self-driving cars, high-speed financial trading systems, fly-by-wire aircraft, and a myriad of other essential and safety-critical systems, we feel the need to have a level of confidence in the professionals behind them.  That confidence may be misplaced, but it is backed by a degree and/or certification.

In The Complacent Class, economist Tyler Cowen notes that in the 1950s, five percent of workers required a government-issued license in order to do their jobs, but by 2008, 29 percent did.  At many of the software conferences that I participate in, smart and serious professionals compare professional qualifications and job requirements.  It seems increasingly difficult to obtain employment without these certifications; in fact, I met many mid-career people who feel they need to become certified to continue their careers at a high level.

I don’t know the answer to this. I would like to think that some mixture of educated, certified professionals and unqualified-on-paper but passionate and self-educated people are essential in software.

But. Employers are increasingly looking for people who have credentials, usually those provided by a professional society (at least in software), that say they have studied and passed a test.  The problem is that such a thing may or may not have anything to do with their competence, knowledge, dedication, or ability to deliver on a project or task.

Increasingly, we as a society are not allowing for the mixture of qualified-on-paper and passionate-by-nature. I do believe that is wrong, but we are not willing to take the time and effort to identify those who can seriously contribute from those who have passed a test.

Decisions, Decisions – There’s an Algorithm for That March 20, 2017

Posted by Peter Varhol in Software development, Strategy, Technology and Culture.
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I remember shoveling sh** against the tide. Yes, I taught statistics and decision analysis to university business majors for about 15 years.  It wasn’t so much that they didn’t care as they didn’t want to know.

I had more than one student tell me that it was the job of a manager to make decisions, and numbers didn’t make any difference. Others said, “I make decisions the way they are supposed to be made, by my experience and intuition.  That’s what I’m paid for.”

Well, maybe not too much longer. After a couple of decades of robots performing “pick-and-place” and other manufacturing processes, now machine learning is in the early stages of transforming management.  It will help select job candidates, determine which employees are performing at a high level, and allocate resources between projects, among many other things.

So what’s a manager to do? Well, first, embrace the technology.  Simply, you are not going to win if you fight it.  It is inevitable.

Second, make a real effort to understand it. While computers and calculators were available, I always made my students “do it by hand” the first time around, so they could follow what the calculations were telling them.  You need to know what you are turning your decisions over to.

Third, integrate it into your work processes. By using machine learning to complement your own abilities.  Don’t ignore it, but don’t treat it as gospel either.

There are many philosophical questions at work here. Which is better, your experience or the numbers?  Kahneman says they are about the same, which does not bode well for human decision-making.  And the analysis of the numbers will only get better; can we say the same thing about human decision-making?

Of course, this has implications to the future of management. I’ll explore my thoughts there in a future post.

AI: Neural Nets Win, Functional Programming Loses October 4, 2016

Posted by Peter Varhol in Software development, Software platforms, Uncategorized.
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Today, we might be considered to be in the heady early days of AI commercialization. We have pretty decent speech recognition, and pattern recognition in general.  We have engines that analyze big data and produce conclusions in real time.  We have recommendations engines; while not perfect, they seem to be to be profitable for ecommerce companies.  And we continue to hear the steady drumbeat of self-driving cars, if not today, then tomorrow.

I did graduate work in AI, in the late 1980s and early 1990s. In most universities at the time, this meant that you spent a lot of time writing Lisp code, that amazing language where everything is a function, and you could manipulate functions in strange and wonderful ways.  You might also play around a bit with Prolog, a streamlined logic language that made logic statements easy, and everything else hard.

Later, toward the end of my aborted pursuit of a doctorate, I discovered neural networks. These were not taught in most universities at the time.  If I were to hazard a guess as to why, I would say that they were both poorly understood and not worthy of serious research.  I used a commercial neural network package to build an algorithm for an electronic wind sensor, and it was actually not nearly as difficult as writing a program from scratch in Lisp.

I am long out of academia, so I can’t say what is happening there today. But in industry, it is clear that neural networks have become the AI approach of choice.  There are tradeoffs of course.  You will never understand the underlying logic of a neural network; ultimately, all you really know is that it works.

As for Lisp, although it is a beautiful language in many ways, I don’t know of anyone using it for commercial applications. Most neural network packages are in C/C++, or they generate C code.

I have a certain distrust of academia. I think it came into full bloom during my doctoral work, in the early 1990s, when a professor stated flatly to the class, “OSI will replace Ethernet in a few years, and when that happens, many of our network problems will be solved.”

Never happened, of course, and the problems were solved anyway, but this tells you what kind of bubble academics live in. We have a specification built by a committee of smart people, almost all academics, and of course it’s going to take over the world.  They failed to see the practical roadblocks involved.

And in AI, neural networks have clearly won the day, and while we can’t necessarily follow the exact chain of logic, they generally do a good job.

Update:  Rather than functional programming, I should have called the latter (traditional) AI technique rules-based.  We used Lisp to create rules that spelled up what to do with combinations of discrete rules.

Another Old Line Conglomerate Gets It Wrong August 4, 2016

Posted by Peter Varhol in Software development, Technology and Culture.
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I seem to be taking my curmudgeon role seriously. Today I read that Jeff Immelt, longtime CEO of industrial conglomerate GE, says that every new (young) person hired has to learn how to code.

So many things to say here. First, I have never been a proponent of the “everyone can code” school.  No, let me amend that; everyone can probably learn to code, but is that the best and most productive use of their time?  I would guess not.

Second, I’m sure that in saying that Immelt has put his money where his mouth is, and has gotten his own coding skills together. No?  Well, he’s the boss, so he should be setting the example.

This is just stupid, and I am willing to bet a dollar that GE won’t follow through on this idle boast. Not even the most Millennial-driven, Silicon Valley-based, we’re so full of ourselves startup tech company would demand that every employee know how to code.

And no company needs all of their employees to be spending time on a single shared skill that only a few will actually use. GE needs to focus on hiring the best people possible for hundreds of different types of professional jobs.  It may be an advantage for all of them to have some level of aptitude for understanding how software works, but not coding shouldn’t be a deal-breaker.

I have worked at larger companies where grandiose strategies have been announced and promoted, but rarely if ever followed through. This pronouncement is almost certainly for PR purposes only, and will quietly get shelved sooner rather than later.  And making such a statement does no credit whatsoever to Immelt, who should know better.

The Tyranny of Open Source July 28, 2016

Posted by Peter Varhol in Software development, Software platforms, Software tools.
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If that title sounds strident, it quite possibly is. But hear me out.  I’ve been around the block once or twice.  I was a functioning adult when Richard Stallman wrote The GNU Manifesto, and have followed the Free Software Foundation, open source software licenses, and open source communities for perhaps longer than you have been alive (yes, I’m an older guy).

I like open source. I think it has radically changed the software industry, mostly for the better.

But. Yes, there is always a “but”.  I subscribe to many (too many) community forums, and almost daily I see someone with a query that begins “What is the best open source tool that will let me do <insert just about any technical task here>.”

When I see someone who asks such a question on a forum, I see someone who is flailing about, with no knowledge of the tools of their field, or even how to do a particular activity. That’s okay; we’ve all been in that position.  They are trying to get better.

We all have a job to do, and we want to do it as efficiently as possible. For any class of activity in the software development life cycle, there are a plethora of tools that make that task easier/manageable/possible.

If you tell me that it has to be an open source tool, you are telling me one of two things. First, your employer, who is presumably paying you a competitive (in other words, fairly substantial) salary, is unwilling to support you in getting your job done.  Second, you are afraid to ask if there is the prospect of paying for a commercial product.

And you need to know the reason before you ask the question in a forum.

There is a lot of great open source software out there that can help you do your job more efficiently. There is also a lot of really good commercial software out there that can help you do your job more efficiently.  If you are not casting a broad net across both, you are cheating both yourself and your employer.  If you cannot cast that broad net, then your employer is cheating you.

So for those of you who get onto community forums to ask about the best open source tool for a particular activity, I have a question in return. Are you afraid to ask for a budget, or have you been told in no uncertain terms that there is none?  You know, you might discover that you need help using your open source software, and have to buy support.  If you need help and can’t pay for it, then you have made an extremely poor decision.

So what am I trying to say? You should be looking for the best tool for your purpose.  If it is open source, you may have to be prepared to subscribe to support.  If it is commercial, you likely have to pay a fee up front.  If your sole purpose in asking for an open source product is to avoid payment, you need to run away from your work situation as quickly as possible.

Artificial Intelligence and the Real Kind July 11, 2016

Posted by Peter Varhol in Software development, Software platforms, Uncategorized.
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Over the last couple of months, I’ve been giving a lot of thought to robots, artificial intelligence, and the potential for replacing human thought and action. A part of that comes from the announcement by the European Union that it had drafted a “bill of rights” for robots as potential cyber-citizens of a more egalitarian era.  A second part comes from my recent article on TechBeacon, which I titled “Testing a Moving Target”.

The computer scientist in me wants to say “bullshit disapproved”. Computer programs do what we instruct them to do, no more or no less.  We can’t instruct them to think, because we can’t algorithmically (or in any other way) define thinking.  There is no objective or intuitive explanation for human thought.

The distinction is both real and important. Machines aren’t able to look for anything that their programmers don’t tell them to (I wanted to say “will never be able” there, but I have given up the word “never” in informed conversation).

There is, of course, the Turing Test, which generally purports a way to determine whether you are interacting with a real person or computer program.  In limited ways, a program (Eliza was the first, but it was an easy trick) can fool a person.

Here is how I think human thought is different than computer programming. I can look at something seemingly unstructured, and build a structure out of it.  A computer can’t, unless I as a programmer tell it what to look for.  Sure, I can program generic learning algorithms, and have a computer run data through those algorithms to try to match it up as closely as possible.  I can run an almost infinite number of training sequences, as long as I have enough data on how the system is supposed to behave.

Of course, as a human I need the imagination and experience to see patterns that may be hidden, and that others can’t see. Is that really any different than algorithm training (yes, I’m attempting to undercut my own argument)?

I would argue yes. Our intelligence is not derived from thousands of interactions with training data.  Rather, well, we don’t really know where it comes from.  I’ll offer a guess that it comes from a period of time in which we observe and make connections between very disparate bits of information.  Sure, the neurons and synapses in our brain may bear a surface resemblance to the algorithms of a neural network, and some talent accrues through repetition, but I don’t think intelligence necessarily works that way.

All that said, I am very hesitant to declare that machine intelligence may not one day equal the human kind. Machines have certain advantages over us, such as incredible and accessible data storage capabilities, as well as almost infinite computing power that doesn’t have to be used on consciousness (or will it?).  But at least today and for the foreseeable future, machine intelligence is likely to be distinguishable from the organic kind.