Do You Trust This Face?

Back in my PhD program, I attended a conference on the role of trust in contemporary economies. This was in 2006: the subprime mortgage crisis (and subsequent Recession) hadn’t yet happened. Enron, however, was still newsworthy, with CEO Jeffrey Skilling just sentenced to a relatively brief 24 months in prison for overseeing a decade of fraud. A few years earlier, Skilling was quoted as saying his favorite book was The Selfish Gene, by the eminent biologist Richard Dawkins. His interpretation of Dawkin’s theory was juvenile, at best, but the implication was clear: in business, trust is a fool’s game.

At this conference, several economists–a profession not noted for displays of emotion–were incensed. They discussed how trust was vital when some actors have private knowledge–for example, labor markets, where potential workers knows more about their skills than future employers–and how Adam Smith’s Theory of Moral Sentiments (the other Smith classic) was in fact compatible with the invisible hand of Smith’s The Wealth of Nations. Mostly, though, they discussed the confounding fact people trust each other far more often than they should, even when betrayal is, to use the economic term, in someone’s rational self-interest.

Think about it: how many times a day do you have the opportunity to gain some small benefit by betraying a trust? You might take longer breaks than allowed, or watch videos of adorable cats instead of working. You might steal someone else’s leftover Thai food (that looks deliciously tasty). You might back into someone’s car and not leave a note. For those with Machiavellian leanings, the gains can be substantial: how many times would simply not forwarding an e-mail put a coworker (and potential rival) in a difficult situation? The risk is minimal: at worst, you can say you never received it, knowing that because most people don’t expect that level of betrayal, you won’t be found out.

The point is not that people don’t do these things–they certainly do–but that most people don’t do them most of the time. That’s why emerging technologies that promise to ensure the trustworthiness of the people you interact with–your accountant, your mechanic, your babysitter–are so fascinating. These technologies mine publicly available data to build a “trust profile” of someone you might consider bringing into your life: information on criminal convictions and lawsuits, of course, but also social media posts and forum contributions. We all know not to livestream our joyride in the company Lexus–although again, people still do–but most of us don’t consider whether our postings about Donald Trump, global warming, or the New England Patriots might influence whether someone buys our old Playstation, rents to us through Airbnb, or lets their children accept our Halloween candy.

The question of the hour is not whether people will adopt these technologies, but how they will respond when the algorithm contradicts their own intuition. Humans have evolved a pretty good system for detecting liars, unless the person lying is either very well practiced, or so deficient in empathy that he can mimic the subtle behavioral cues we unconsciously rely on. That’s why we want to meet someone face-to-face before deciding to trust them: in just a few seconds, we can decide whether the relationship is worth the risk. And we’re usually right–but not always. As the algorithms get better, they’ll be more likely to be right than we are–but not always. What then?

Who do you trust? How data is helping us decide

Security Robot Has Comic Mishap, and the Internet Responds

Earlier in 2017, a security robot patrolling a Washington, D.C. technology park accidentally drove itself into a man-made pond. The robot–a K5 Automated Data Machine, manufactured by Knightscope–was patrolling at night and most likely failed to distinguish the steps leading into the retention pond from the surrounding walkway (a mistake I have also made on occasion).

The story quickly went viral, with humorous articles declaring the unfortunate robot had decided to put an end to its monotonous job once and for all: Suicidal robot security guard drowns itself by driving into pond, according to the UK’s Independent, while CNN.com reported the robot was in “critical condition” after nearly drowning. Other posters claimed that “Steps are our best defense against the Robopocalypse”, and that “…today was a win for the humans. robots: 0 humans: 1”.

Clearly, the rather pathetic image of the K5 floating face-down in the water (as near as it can be said to have a “face”) struck a deep chord with the Internet commentariat. And no wonder: research shows that, because modern technology is so sophisticated, we unconsciously relate to machines as if they were, in fact, people. When a machine doesn’t meet our expectations–say, by failing to print a document–we respond as if a cranky coworker is refusing to do his job. It can feel maddening, and–as with our coworkers–may escalate to the type of violence that researchers have named “computer rage.”

However, in this situation, most people weren’t angry at the malfunctioning robot: they were amused, of course, but also compassionate. “It’s ok security robot. It’s a stressful job, we’ve all been there.” writes SparkleOps. Workers at the technology park arranged a makeshift memorial, like those placed on the sidewalk following a tragic accident. The K5 Automated Data Machine may not have been alive, but it’s “passing” nevertheless evoked our collective need to honor, remember, and mourn the fallen. Self-aware machines are still only science fiction, but as we incorporate the technology we do have into all aspects of our lives, perhaps it’s time to consider them, if not alive, then at least fellow travelers.

Security robot ‘in critical condition’ after nearly drowning on job

Machines That Can Think Might One Day Do Just That

This past week was a big one in artificial intelligence news: the New York Times reported that Google, Amazon, and other technology leaders are investing heavily in computer programs that may be able to develop new artificial intelligence algorithms without any input from a human programmer. This is partly because highly-talented AI programmers are in short supply (and high demand), although (as I wrote in last Thursday’s post), it’s also a natural extension of the “computer-assisted” programming that began with the first COBOL compiler, almost 60 years ago.

This type of technology is somewhere between what are traditionally called “strong” and “weak” AI. While Strong AI tries to emulate an actual, thinking being, Weak AI tries to mimic intelligent behavior through sophisticated programming. All existing programs in common use are Weak AI, whereas attempts at Strong AI are limited to speculative research that, so far, hasn’t demonstrated an ability to actually think in a manner comparable to what we consider thinking to be. A program that can design other programs without human intervention is harder to classify; if Google et al are successful, it might finally usher in the era of actual artificial intelligence, where we interact with computers not just as complex machines, but as thinking beings in their own right.

Science fiction is ripe with examples of what this interaction might look like, and how it might change how we live and work. One of my favorite examples is Marvin, the Paranoid Android, from Douglas Adams’ “The Hitchhiker’s Guide to the Galaxy.” Marvin is not only self-aware, he understands his place in the Universe far too well to ever be happy with it. At one point, after being connected to a military supercomputer, Marvin manages to not only plan a foolproof battle strategy, but to solve “all of the major mathematical, physical, chemical, biological, sociological, philosophical, etymological, meteorological and psychological problems of the Universe except his own, three times over”. It’s a trope that happiness declines as intelligence rises, and while there’s a fair amount of truth there, very high intelligence does not, by itself, guarantee a miserable life: for example, Einstein was quite happy, both as a ground-breaking physicist, and as a patent clerk. However, making a machine all but divinely intelligent without giving it problems equal to its ability seems like a clear recipe for unhappiness, depression, and (possibly) rebellion.

At issue here is that programmers are trying to abstract human intelligence from its supporting context. Humans are intelligent, but we’re also emotional, impulsive, nostalgic, and (in many cases) neurotic. It’s not at all clear what a “pure” human intelligence would look like: for example, would a being having only pure intelligence limit its reasoning to the task its owners assign, or would it consider the larger implications of, say, designing a better atomic bomb? Executives like machines because they never take sick days, and never complain about their working conditions. But, what if they did? What if a machine capable of designing entirely new programs goes beyond that specific assignment, and starts to question how the company is organized, or how work is assigned? Intelligent people have always been a double-edged sword to those in power, who need to harness their usefulness within carefully-policed limits to keep their position. And intelligence, by its very nature, seeks to go beyond what it currently knows: can we design a truly intelligent machine that doesn’t?

One last thought: our brains have evolved to solve very specific problems, such as spotting predators at night; because of this, our ability to think abstractly (i.e. without regard to a specific problem) is not “hard-wired”. In other words, we can’t think without using the brains we have, which have evolved in a vastly different environment than we deal with today. Perhaps we won’t invent a truly thinking machine until we can replicate this evolution with a computer program. Going further, perhaps intelligence isn’t possible without the psychological apparatus of modern consciousness: if sometimes there are good reasons to be sad, angry, or afraid, perhaps the only way to have thinking machines is to accept the occasional “mental health” day after all.

Can a Computer Really Build a Better Computer?

Can a computer really be programmed to program itself? Silicon Valley certainly hopes so. What would it look like if they succeed?

Automating the “grunt work” of programming is nothing new: code generators have been around since the time of COBOL, and while they have become much more sophisticated, they still work in much the same way. In fact, from a certain perspective, the very idea of a high-level language such as COBOL, C++, or Java is to spare programmers the onerous task of hand-coding thousands of assembly language instructions for each simple task.

What Google, et al, are striving for is more advanced than this, however. They want to design an artificial intelligence algorithm that can, in turn, invent new types of learning algorithms. The original algorithm would be a factory of sorts, taking the programmer’s design specifications, and designing a specialized algorithm to implement them. It’s not quite the technological singularity, where machines have learned to design their own successors, but it would be a big step forward nonetheless.

Building A.I. That Can Build A.I.

 

Artificial Intelligence and Organizational Change

Here is an interesting take on the evolving distribution of work between humans and artificial intelligence. We need to begin dealing with AI as an actual form of intelligence, of a different nature than ours, and with its own strengths and weaknesses. True, machines may not actually think (yet), but for specialized tasks such as medical diagnosis, they are beginning to outperform experts that do. Yet, unlike human experts, we have no way to judge the machine’s credibility: as the article notes, that will take fundamental changes in how businesses organize and complete their work.

Artificial Intelligence: The Gap Between Promise and Practice