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My Boss is a Computer August 11, 2018

Posted by Peter Varhol in Machine Learning, Technology and Culture.
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Well, not really, but if you can be fired by a computer, it must be your boss.  Not my story, but one that foretells the future nonetheless.  An apparently uncorrectable software defect led to a contract employee being locked out of his computer and his building, and labeled inactive in the payroll.

It was almost comically funny that his manager and other senior managers and executives at the company, none of whom fired him, could not get this fiat reversed.  A full three weeks passed, in which he received no pay and no explanation, before they were able to determine that his employment status had never been updated in their new HR management software.  Even after he was reinstated, his colleagues treated him as someone not entitled to work there, and he eventually left.

It seems that intelligent (or otherwise) software is encroaching into the ultimate and unabashed people-oriented field – human resources.  And there’s not a darned thing we can do about it.  Software is not only conducting full interviews, but also performing the entire hiring process.  While we might hope that we aren’t actually selected (or rejected) by computer algorithms, that is the goal of these software systems.

So here’s the problem.  Or several problems.  First, software isn’t perfect, and while most software bugs in released software are no more than annoying, bugs in this kind of software can have drastic consequences on people.  Those consequences will likely spill over to the hiring company itself.

Second, these applications are usually machine learning systems that have had their algorithms trained through the application of large amounts of data.  The most immediate problem is that the use of biased data will simply perpetuate existing practices.  That’s a problem because everything about the interview and selection process is subjective and highly prone to bias.

Last, if the software doesn’t allow for human oversight and the ability to override, then in effect a company has ceded its hiring decisions to software that it most likely doesn’t understand.  That’s a recipe for disaster, as management has lost control over the reasons why management exists in the first place.

Now, there may be some that will say that’s actually a good thing.  Human management is, well, human, with human failings, and sometimes they manifest themselves in negative ways.  Bosses are dictatorial, or racist, or some combination of negative qualities, and are often capricious in dealing with others.  Computer software is at least consistent, if not necessarily fair as we might define it.

But no matter how poor the decisions that might come from human managers, we own them.  If it’s software, no one owns them.  When we are locked in to following the dictates of software, without any understanding as to who programmed it to do what, then we give up on our fellow citizens and colleagues.  Worse, we give up the control that we are paid to maintain.

Lest we face a dystopian future where computer software rules our working lives, and we are powerless to act as the humans we are, then we must control the software that is presumably helping us.

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Here’s Looking At You June 18, 2018

Posted by Peter Varhol in Algorithms, Machine Learning, Software tools, Technology and Culture.
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I studied a rudimentary form of image recognition when I was a grad student.  While I could (sometimes) identify simple images based on obviously distinguishing characteristics, the limitations of rule-based systems, the computing power of Lisp Machines and early Macs, facial recognition was well beyond the capabilities of the day.

Today, facial recognition has benefitted greatly from better algorithms and faster processing, and is available commercially by several different companies.  There is some question as to the reliability, but at this point it’s probably better than any manual approach to comparing photos.  And that seems to be a problem for some.

Recently the ACLU and nearly 70 groups sent a letter to Amazon CEO Jeff Bezos, alongside the one from 20 shareholder groups, arguing Amazon should not provide surveillance systems such as facial recognition technology to the government.  Amazon has a facial recognition system called Rekognition (why would you use a spelling that is more reminiscent of evil times in our history?)

Once again, despite the Hitleresque product name, I don’t get the outrage.  We give the likes of Facebook our life history in detail, in pictures and video, and let them sell it on the open market, but the police can’t automate the search of photos?  That makes no sense.  Facebook continues to get our explicit approval for the crass but grossly profitable commercialization of our most intimate details, while our government cannot use commercial and legal software tools?

Make no mistake; I am troubled by our surveillance state, probably more than most people, but we cannot deny tools to our government that the Bad Guys can buy and use legally.  We may not like the result, but we seem happy to go along like sheep when it’s Facebook as the shepherd.

I tried for the life of me to curse our government for its intrusion in our lives, but we don’t seem to mind it when it’s Facebook, so I just can’t get excited about the whole thing.  I cannot imagine Zuckerberg running for President.  Why should he give up the most powerful position in the world to face the checks and balances of our government?

I am far more concerned about individuals using commercial facial recognition technology to identify and harass total strangers.  Imagine an attractive young lady (I am a heterosexual male, but it’s also applicable to other combinations) walking down the street.  I take her photo with my phone, and within seconds have her name, address, and life history (quite possibly from her Facebook account).  Were I that type of person (I hope I’m not), I could use that information to make her life difficult.  While I don’t think I would, there are people who would think nothing of doing so.

So my take is that if you don’t want the government to use commercial facial recognition software, demonstrate your honesty and integrity by getting the heck off of Facebook first.

Update:  Apple will automatically share your location when you call 911.  I think I’m okay with this, too.  When you call 911 for an emergency, presumably you want to be found.

Cognitive Bias in Machine Learning June 8, 2018

Posted by Peter Varhol in Algorithms, Machine Learning.
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I’ve danced around this topic over the last eight months or so, and now think I’ve learned enough to say something definitive.

So here is the problem.  Neural networks are sets of layered algorithms.  It might have three layers, or it might have over a hundred.  These algorithms, which can be as simple as polynomials, or as complex as partial derivatives, process incoming data and pass it up to the next level for further processing.

Where do these layers of algorithms come from?  Well, that’s a much longer story.  For the time being, let’s just say they are the secret sauce of the data scientists.

The entire goal is to produce an output that accurately models the real-life outcome.  So we run our independent variables through the layers of algorithms and compare the output to the reality.

There is a problem with this.  Given a complex enough neural network, it is entirely possible that any data set can be trained to provide an acceptable output, even if it’s not related to the problem domain.

And that’s the problem.  If any random data set will work for training, then choosing a truly representative data set can be a real challenge.  Of course, will would never use a random data set for training; we would use something that was related to the problem domain.  And here is where the potential for bias creeps in.

Bias is disproportionate weight in favor of or against one thing, person, or group compared with another.  It’s when we make one choice over another for emotional rather than logical reasons.  Of course, computers can’t show emotion, but they can reflect the biases of their data, and the biases of their designers.  So we have data scientists either working with data sets that don’t completely represent the problem domain, or making incorrect assumptions between relationships between data and results.

In fact, depending on the data, the bias can be drastic.  MIT researchers have recently demonstrated Norman, the psychopathic AI.  Norman was trained with written captions describing graphic images about death from the darkest corners of Reddit.  Norman sees only violent imagery in Rorschach inkblot cards.  And of course there was Tay, the artificial intelligence chatter bot that was originally released by Microsoft Corporation on Twitter.  After less than a day, Twitter users discovered that Tay could be trained with tweets, and trained it to be obnoxious and racist.

So the data we use to train our neural networks can make a big difference in the results.  We might pick out terrorists based on their appearance or religious affiliation, rather than any behavior or criminal record.  Or we might deny loans to people based on where they live, rather than their ability to pay.

On the one hand, biases may make machine learning systems seem more, well, human.  On the other, we want outcomes from our machine learning systems that accurately reflect the problem domain, and not biased.  We don’t want our human biases to become inherited by our computers.

Can Machines Learn Cause and Effect? June 6, 2018

Posted by Peter Varhol in Algorithms, Machine Learning.
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Judea Pearl is one of the giants of what started as an offshoot of classical statistics, but has evolved into the machine learning area of study.  His actual contributions deal with Bayesian statistics, along with prior and conditional probabilities.

If it sounds like a mouthful, it is.  Bayes Theorem and its accompanying statistical models are at the same time surprisingly intuitive and mind-blowingly obtuse (at least to me, of course).  Bayes Theorem describes the probability of a particular outcome, based on prior knowledge of conditions that might be related to the outcome.  Further, we update that probability when we have new information, so it is dynamic.

So when Judea Pearl talks, I listen carefully.  In this interview, he is pointing out that machine learning and AI as practiced today is limited by the techniques we are using.  In particular, he claims that neural networks simply “do curve fitting,” rather than understand about relationships.  His goal is for machines to discern cause and effect between variables, that is “A causes B to happen, B causes C to happen, but C does not cause A or B”.  He thinks that Bayesian inference is ultimately a way to do this.

It’s a provocative statement to say that we can teach machines about cause and effect.  Cause and effect is a very situational concept.  Even most humans stumble over it.  For example, does more education cause people to have a higher income?  Well maybe.  Or it may be that more intelligence causes a higher income, but more intelligent people also tend to have more education.  I’m simply not sure about how we would go about training a machine, using only quantitative data, about cause and effect.

As for neural networks being mere curve-fitting, well, okay, in a way.  He is correct to point out that what we are doing with these algorithms is not finding Truth, or cause and effect, but rather looking at the best way of expressing a relationship between our data and the outcome produced (or desired, in the case of unsupervised learning).

All that says is that there is a relationship between the data and the outcome.  Is it causal?  It’s entirely possible that not even a human knows.

And it’s not at all clear to me that this is what Bayesian inference is saying.  And in fact I don’t see anything in any statistical technique that allows us to assume cause and effect.  Right now, the closest we come to this in simple correlation is R-squared, which allows us to say how much of a statistical correlation is “explained” by the data.  But “explained” doesn’t mean what you think it means.

As for teaching machines cause and effect, I don’t discount it eventually.  Human intelligence and free will is an existence proof; we exhibit those characteristics, at least some of the time, so it is not unreasonable to think that machines might someday also do so.  That said, it certainly won’t happen in my lifetime.

And about data.  We fool ourselves here too.  More on this in the next post.

More on AI and the Turing Test May 20, 2018

Posted by Peter Varhol in Architectures, Machine Learning, Strategy, Uncategorized.
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It turns out that most people who care to comment are, to use the common phrase, creeped out at the thought of not knowing whether they are talking to an AI or a human being.  I get that, although I don’t think I’m myself bothered by such a notion.  After all, what do we know about people during a casual phone conversation?  Many of them probably sound like robots to us anyway.

And this article in the New York Times notes that Google was only able to accomplish this feat by severely limiting the domain in which the AI could interact with – in this case, making dinner reservations or a hair appointment.  The demonstration was still significant, but isn’t a truly practical application, even within a limited domain space.

Well, that’s true.  The era of an AI program interacting like a human across multiple domains is far away, even with the advances we’ve seen over the last few years.  And this is why I even doubt the viability of self-driving cars anytime soon.  The problem domains encountered by cars are enormously complex, far more so than any current tests have attempted.  From road surface to traffic situation to weather to individual preferences, today’s self-driving cars can’t deal with being in the wild.

You may retort that all of these conditions are objective and highly quantifiable, making it possible to anticipate and program for.  But we come across driving situations almost daily that have new elements that must be instinctively integrated into our body of knowledge and acted upon.  Computers certainly have the speed to do so, but they lack a good learning framework to identify critical data and integrate that data into their neural network to respond in real time.

Author Gary Marcus notes that what this means is that the deep learning approach to AI has failed.  I laughed when I came to the solution proposed by Dr. Marcus – that we return to the backward-chaining rules-based approach of two decades ago.  This was what I learned during much of my graduate studies, and was largely given up on in the 1990s as unworkable.  Building layer upon layer of interacting rules was tedious and error-prone, and it required an exacting understanding of just how backward chaining worked.

Ultimately, I think that the next generation of AI will incorporate both types of approaches.  The neural network to process data and come to a decision, and a rules-based system to provide the learning foundation and structure.

Google AI and the Turing Test May 12, 2018

Posted by Peter Varhol in Algorithms, Machine Learning, Software development, Technology and Culture, Uncategorized.
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Alan Turing was a renowned mathematician in Britain, and during WW 2 worked at Bletchley Park in cryptography.  He was an early computer pioneer, and today is probably best known for the Turing Test, a way of distinguishing between computers and humans (hypothetical at the time).

More specifically, the Turing Test was designed to see if a computer could pass for a human being, and was based on having a conversation with the computer.  If the human could not distinguish between talking to a human and talking to a computer, the computer was said to have passed the Turing Test.  No computer has ever done so, although Joseph Weizenbaum’s Eliza psychology therapist in the 1960s was pretty clever (think Alfred Adler).

The Google AI passes the Turing Test.  https://www.youtube.com/watch?v=D5VN56jQMWM&feature=youtu.be.

I’m of two minds about this.  First, it is a great technical and scientific achievement.  This is a problem that for decades was thought to be intractable.  Syntax has definite structure and is relatively easy to parse.  While humans seem to understand language semantics instinctively, there are ambiguities that can only be learned through training.  That’s where deep learning through neural networks comes in.  And to respond in real time is a testament to today’s computing power.

Second, and we need this because we don’t want to have phone conversations?  Of course, the potential applications go far beyond calling to make a hair appointment.  For a computer to understand human speech and respond intelligently to the semantics of human words, it requires some significant training in human conversation.  That certainly implies deep learning, along with highly sophisticated algorithms.  It can apply to many different types of human interaction.

But no computing technology is without tradeoffs, and intelligent AI conversation is no exception.  I’m reminded of Sherry Turkle’s book Reclaiming Conversation.  It posits that people are increasingly afraid of having spontaneous conversations with one another, mostly because we cede control of the situation.  We prefer communications where we can script our responses ahead of time to conform to our expectations of ourselves.

Having our “AI assistant” conduct many of those conversations for us seems like simply one more step in our abdication as human beings, unwilling to face other human beings in unscripted communications.  Also, it is a way of reducing friction in our daily lives, something I have written about several times in the past.

Reducing friction is also a tradeoff.  It seems worthwhile to make day to day activities easier, but as we do, we also fail to grow as human beings.  I’m not sure where the balance lies here, but we should not strive single-mindedly to eliminate friction from our lives.

5/14 Update:  “Google Assistant making calls pretending to be human not only without disclosing that it’s a bot, but adding “ummm” and “aaah” to deceive the human on the other end with the room cheering it… horrifying. Silicon Valley is ethically lost, rudderless and has not learned a thing…As digital technologies become better at doing human things, the focus has to be on how to protect humans, how to delineate humans and machines, and how to create reliable signals of each—see 2016. This is straight up, deliberate deception. Not okay.” – Zeynep Tufekci, Professor & Writer 

Let’s Have a Frank Discussion About Complexity December 7, 2017

Posted by Peter Varhol in Algorithms, Machine Learning, Strategy, Uncategorized.
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And let’s start with the human memory.  “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information” is one of the most highly cited papers in psychology.  The title is rhetorical, of course; there is nothing magical about the number seven.  But the paper and associated psychological studies explicitly define the ability of the human mind to process increasingly complex information.

The short answer is that the human mind is a wonderful mechanism for some types of processing.  We can very rapidly process a large amount of sensory inputs, and draw some very quick but not terribly accurate conclusions (Kahneman’s Type 1 thinking), we can’t handle an overwhelming amount of quantitative data and expect to make any sense out of it.

In discussing machine learning systems, I often say that we as humans have too much data to reliably process ourselves.  So we set (mostly artificial) boundaries that let us ignore a large amount of data, so that we can pay attention when the data clearly signify a change in the status quo.

The point is that I don’t think there is a way for humans to deal directly with a lot of complexity.  And if we employ systems to evaluate that complexity and present it in human-understandable concepts, we are necessarily losing information in the process.

This, I think, is a corollary of Joel Spolsky’s Law of Leaky Abstractions, which says that anytime you abstract away from what is really happening with hardware and software, you lose information.  In many cases, that information is fairly trivial, but in some cases, it is critically valuable.  If we miss it, it can cause a serious problem.

While Joel was describing abstraction in a technical sense, I think that his law applies beyond that.  Any time that you add layers in order to better understand a scenario, you out of necessity lose information.  We look at the Dow Jones Industrial Average as a measure of the stock market, for example, rather than minutely examine every stock traded on the New York Stock Exchange.

That’s not a bad thing.  Abstraction makes it possible for us to better comprehend the world around us.

But it also means that we are losing information.  Most times, that’s not a disaster.  Sometimes it can lead us to disastrously bad decisions.

So what is the answer?  Well, abstract, but doubt.  And verify.

Who Will Thrive in an AI World? November 26, 2017

Posted by Peter Varhol in Machine Learning, Technology and Culture.
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Software engineers, of course, who understand both relevant programming languages and the math behind the algorithms.  That is significantly less than the universe of software engineers in general, but I don’t see even those math-deprived programmers having a big problem, at least in the short term.

Beyond that?  Are we all toast?

Well, no.  Today someone asked me how machine learning would affect health care jobs.  I thought to where health care was going with machine learning, and to my own experiences with health care.  “The survivors will be those who can understand what the algorithms tell them, but also talk with the patients those results affect.”

I have dealt with doctors (such as my current PCP, who could be a much better doctor if she simply trusted herself) who simply look at test results and parrot them back to you.  I had a doctor who I liked and trusted, who could not find cancer but insisted it was there, based on photographs (it was not).

These are not health care professionals who will thrive in an era of AI-assisted medical evaluation and diagnosis.  They simply parrot test results, without adding value or effectively communicating with the patient.

To be fair, our system has created this kind of doctor, who is afraid of using their expertise to express an independent opinion.  I had one who did employ his expertise, during my cancer scare.  He came into my room, and said, “Where is your nose drain?  How come you’re not choking?”  Then “I looked at your MRI from six years ago, and you had indications then.  Whatever this is, it probably isn’t cancer.”  It wasn’t.

Doctors have become afraid to use their expertise, because of the fear of lawsuits and other recriminations.  That is unfortunate, and of course not entirely their fault.  But this is just the kind of doctor who will not survive the machine learning revolution.

I think that general conclusion can be extended to other fields.  Those that become overly reliant on machine results, and decline to employ their own expertise, will ultimately be left behind.  Those who are willing to use those results, yet supplement them with their own expertise, and effectively explain it to their patients, will succeed.  We are still people, after all, and need to communicate with one another.