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The Glass Cage: Automation and US

The Glass Cage

The impact of technology is all around us. Maybe we’re at another Gutenberg moment and maybe we’re not.

Marshall McLuhan said it best.

When any new form comes into the foreground of things, we naturally look at it through the old stereos. We can’t help that. This is normal, and we’re still trying to see how will our previous forms of political and educational patterns persist under television. We’re just trying to fit the old things into the new form, instead of asking what is the new form going to do to all the assumptions we had before.

He also wrote that “a new medium is never an addition to an old one, nor does it leave the old one in peace.”

In The Glass Cage: Automation and US, Nick Carr, one of my favorite writers, enters the debate about the impact automation has on us, “examining the personal as well as the economic consequences of our growing dependence on computers.”

We know that the nature of jobs is going to change in the future thanks to technology. Tyler Cowen argues “If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch.”

Carr’s book shows another side to the argument – the broader human consequences to living in a world where computers and software do the things we used to do.

Computer automation makes our lives easier, our chores less burdensome. We’re often able to accomplish more in less time—or to do things we simply couldn’t do before. But automation also has deeper, hidden effects. As aviators have learned, not all of them are beneficial. Automation can take a toll on our work, our talents, and our lives. It can narrow our perspectives and limit our choices. It can open us to surveillance and manipulation. As computers become our constant companions, our familiar, obliging helpmates, it seems wise to take a closer look at exactly how they’re changing what we do and who we are.

On the autonomous automobile, for example, Carr agues that while they have a ways to go before they start chauffeuring us around, there are broader questions that need to be answered first.

Although Google has said it expects commercial versions of its car to be on sale by the end of the decade, that’s probably wishful thinking. The vehicle’s sensor systems remain prohibitively expensive, with the roof-mounted laser apparatus alone going for eighty thousand dollars. Many technical challenges remain to be met, such as navigating snowy or leaf-covered roads, dealing with unexpected detours, and interpreting the hand signals of traffic cops and road workers. Even the most powerful computers still have a hard time distinguishing a bit of harmless road debris (a flattened cardboard box, say) from a dangerous obstacle (a nail-studded chunk of plywood). Most daunting of all are the many legal, cultural, and ethical hurdles a driverless car faces-Where, for instance, will culpability and liability reside should a computer-driven automobile cause an accident that kills or injures someone? With the car’s owner? With the manufacturer that installed the self-driving system? With the programmers who wrote the software? Until such thorny questions get sorted out, fully automated cars are unlikely to grace dealer showrooms.

Tacit and Explicit Knowledge

Self-driving cars are just one example of a technology that forces us “to change our thinking about what computers and robots can and can’t do.”

Up until that fateful October day, it was taken for granted that many important skills lay beyond the reach of automation. Computers could do a lot of things, but they couldn’t do everything. In an influential 2004 book, The New Division of Labor: How Computers Are Creating the Next Job Market, economists Frank Levy and Richard Murnane argued, convincingly, that there were practical limits to the ability of software programmers to replicate human talents, particularly those involving sensory perception, pattern recognition, and conceptual knowledge. They pointed specifically to the example of driving a car on the open road, a talent that requires the instantaneous interpretation of a welter of visual signals and an ability to adapt seamlessly to shifting and often unanticipated situations. We hardly know how we pull off such a feat ourselves, so the idea that programmers could reduce all of driving’s intricacies, intangibilities, and contingencies to a set of instructions, to lines of software code, seemed ludicrous. “Executing a left turn across oncoming traffic,” Levy and Murnane wrote, “involves so many factors that it is hard to imagine the set of rules that can replicate a drivers behavior.” It seemed a sure bet, to them and to pretty much everyone else, that steering wheels would remain firmly in the grip of human hands.

In assessing computers’ capabilities, economists and psychologists have long drawn on a basic distinction between two kinds of knowledge: tacit and explicit. Tacit knowledge, which is also sometimes called procedural knowledge, refers to all the stuff we do without actively thinking about it: riding a bike, snagging a fly ball, reading a book, driving a car. These aren’t innate skills—we have to learn them, and some people are better at them than others—but they can’t be expressed as a simple recipe, a sequence of precisely defined steps. When you make a turn through a busy intersection in your car, neurological studies have shown, many areas of your brain are hard at work, processing sensory stimuli, making estimates of time and distance, and coordinating your arms and legs. But if someone asked you to document everything involved in making that turn, you wouldn’t be able to, at least not without resorting to generalizations and abstractions.The ability resides deep in your nervous system outside the ambit of your conscious mind. The mental processing goes on without your awareness.

Much of our ability to size up situations and make quick judgments about them stems from the fuzzy realm of tacit knowledge. Most of our creative and artistic skills reside there too. Explicit knowledge, which is also known as declarative knowledge, is the stuff you can actually write down: how to change a flat tire, how to fold an origami crane, how to solve a quadratic equation. These are processes that can be broken down into well-defined steps. One person can explain them to another person through written or oral instructions: do this, then this, then this.

Because a software program is essentially a set of precise, written instructions—do this, then this, then this—we’ve assumed that while computers can replicate skills that depend on explicit knowledge, they’re not so good when it comes to skills that flow from tacit knowledge. How do you translate the ineffable into lines of code, into the rigid, step-by-step instructions of an algorithm? The boundary between the explicit and the tacit has always been a rough one—a lot of our talents straddle the line—but it seemed to offer a good way to define the limits of automation and, in turn, to mark out the exclusive precincts of the human. The sophisticated jobs Levy and Murnane identified as lying beyond the reach of computers—in addition to driving, they pointed to teaching and medical diagnosis—were a mix of the mental and the manual, but they all drew on tacit knowledge.

Google’s car resets the boundary between human and computer, and it does so more dramatically, more decisively, than have earlier breakthroughs in programming. It tells us that our idea of the limits of automation has always been something of a fiction. Were not as special as we think we are. While the distinction between tacit and explicit knowledge remains a useful one in the realm of human psychology, it has lost much of its relevance to discussions of automation.

Tomorrowland

That doesn’t mean that computers now have tacit knowledge, or that they’ve started to think the way we think, or that they’ll soon be able to do everything people can do. They don’t, they haven’t, and they won’t. Artificial intelligence is not human intelligence. People are mindful; computers are mindless. But when it comes to performing demanding tasks, whether with the brain or the body, computers are able to replicate our ends without replicating our means. When a driverless car makes a left turn in traffic, it’s not tapping into a well of intuition and skill; it’s following a program. But while the strategies are different, the outcomes, for practical purposes, are the same. The superhuman speed with which computers can follow instructions, calculate probabilities, and receive and send data means that they can use explicit knowledge to perform many of the complicated tasks that we do with tacit knowledge. In some cases, the unique strengths of computers allow them to perform what we consider to be tacit skills better than we can perform them ourselves. In a world of computer-controlled cars, you wouldn’t need traffic lights or stop signs. Through the continuous, high-speed exchange of data, vehicles would seamlessly coordinate their passage through even the busiest of intersections—just as computers today regulate the flow of inconceivable numbers of data packets along the highways and byways of the internet. What’s ineffable in our own minds becomes altogether effable in the circuits of a microchip.

Many of the cognitive talents we’ve considered uniquely human, it turns out, are anything but. Once computers get quick enough, they can begin to replicate our ability to spot patterns, make judgments, and learn from experience.

It’s not only vocations that are increasingly being computerized, avocations are too.

Thanks to the proliferation of smartphones, tablets, and other small, affordable, and even wearable computers, we now depend on software to carry out many of our daily chores and pastimes. We launch apps to aid us in shopping, cooking, exercising, even finding a mate and raising a child. We follow turn-by-turn GPS instructions to get from one place to the next. We use social networks to maintain friendships and express our feelings. We seek advice from recommendation engines on what to watch, read, and listen to. We look to Google, or to Apple’s Siri, to answer our questions and solve our problems. The computer is becoming our all-purpose tool for navigating, manipulating, and understanding the world, in both its physical and its social manifestations. Just think what happens these days when people misplace their smartphones or lose their connections to the net. Without their digital assistants, they feel helpless.

As Katherine Hayles, a literature professor at Duke University, observed in her 2012 book How We Think, “When my computer goes down or my Internet connection fails, I feel lost, disoriented, unable to work—in fact, I feel as if my hands have been amputated.”

While our dependency on computers is “disconcerting at times,” we welcome it.

We’re eager to celebrate and show off our whizzy new gadgets and apps—and not only because they’re so useful and so stylish. There’s something magical about computer automation. To watch an iPhone identify an obscure song playing over the sound system in a bar is to experience something that would have been inconceivable to any previous generation.

Miswanting

The trouble with automation is “that it often gives us what we don’t need at the cost of what we do.”

To understand why that’s so, and why we’re eager to accept the bargain, we need to take a look at how certain cognitive biases—flaws in the way we think—can distort our perceptions. When it comes to assessing the value of labor and leisure, the mind’s eye can’t see straight.

Mihaly Csikszentmihalyi, a psychology professor and author of the popular 1990 book Flow, has described a phenomenon that he calls “the paradox of work.” He first observed it in a study conducted in the 1980s with his University of Chicago colleague Judith LeFevre. They recruited a hundred workers, blue-collar and white-collar, skilled and unskilled, from five businesses around Chicago. They gave each an electronic pager (this was when cell phones were still luxury goods) that they had programmed to beep at seven random moments a day over the course of a week. At each beep, the subjects would fill out a short questionnaire. They’d describe the activity they were engaged in at that moment, the challenges they were facing, the skills they were deploying, and the psychological state they were in, as indicated by their sense of motivation, satisfaction, engagement, creativity, and so forth. The intent of this “experience sampling,” as Csikszentmihalyi termed the technique, was to see how people spend their time, on the job and off, and how their activities influence their “quality of experience.”

The results were surprising. People were happier, felt more fulfilled by what they were doing, while they were at work than during their leisure hours. In their free time, they tended to feel bored and anxious. And yet they didn’t like to be at work. When they were on the job, they expressed a strong desire to be off the job, and when they were off the job, the last thing they wanted was to go back to work. “We have,” reported Csikszentmihalyi and LeFevre, “the paradoxical situation of people having many more positive feelings at work than in leisure, yet saying that they wish to be doing something else when they are at work, not when they are in leisure.” We’re terrible, the experiment revealed, at anticipating which activities will satisfy us and which will leave us discontented. Even when we’re in the midst of doing something, we don’t seem able to judge its psychic consequences accurately.

Those are symptoms of a more general affliction, on which psychologists have bestowed the poetic name miswanting. We’re inclined to desire things we don’t like and to like things we don’t desire. “When the things we want to happen do not improve our happiness, and when the things we want not to happen do,” the cognitive psychologists Daniel Gilbert and Timothy Wilson have observed, “it seems fair to say we have wanted badly.” And as slews of gloomy studies show, we’re forever wanting badly. There’s also a social angle to our tendency to misjudge work and leisure. As Csikszentmihalyi and LeFevre discovered in their experiments, and as most of us know from our own experience, people allow themselves to be guided by social conventions—in this case, the deep-seated idea that being “at leisure” is more desirable, and carries more status, than being “at work”—rather than by their true feelings. “Needless to say,” the researchers concluded, “such a blindness to the real state of affairs is likely to have unfortunate consequences for both individual wellbeing and the health of society.” As people act on their skewed perceptions, they will “try to do more of those activities that provide the least positive experiences and avoid the activities that are the source of their most positive and intense feelings.” That’s hardly a recipe for the good life.

It’s not that the work we do for pay is intrinsically superior to the activities we engage in for diversion or entertainment. Far from it. Plenty of jobs are dull and even demeaning, and plenty of hobbies and pastimes are stimulating and fulfilling. But a job imposes a structure on our time that we lose when we’re left to our own devices. At work, were pushed to engage in the kinds of activities that human beings find most satisfying. We’re happiest when we’re absorbed in a difficult task, a task that has clear goals and that challenges us not only to exercise our talents but to stretch them. We become so immersed in the flow of our work, to use Csikszentmihalyi s term, that we tune out distractions and transcend the anxieties and worries that plague our everyday lives. Our usually wayward attention becomes fixed on what we’re doing. “Every action, movement, and thought follows inevitably from the previous one,” explains Csikszentmihalyi. “Your whole being is involved, and you’re using your skills to the utmost.” Such states of deep absorption can be produced by all manner of effort, from laying tile to singing in a choir to racing a dirt bike. You don’t have to be earning a wage to enjoy the transports of flow.

More often than not, though, our discipline flags and our mind wanders when we’re not on the job. We may yearn for the workday to be over so we can start spending our pay and having some fun, but most of us fritter away our leisure hours. We shun hard work and only rarely engage in challenging hobbies. Instead, we watch TV or go to the mall or log on to Facebook. We get lazy. And then we get bored and fretful. Disengaged from any outward focus, our attention turns inward, and we end up locked in what Emerson called the jail of self-consciousness. Jobs, even crummy ones, are “actually easier to enjoy than free time,” says Csikszentmihalyi, because they have the “built-in” goals and challenges that “encourage one to become involved in one’s work, to concentrate and lose oneself in it.” But that’s not what our deceiving minds want us to believe. Given the opportunity, we’ll eagerly relieve ourselves of the rigors of labor. We’ll sentence ourselves to idleness.

Automation offers us innumerable promises. Our lives, we think, will be greater if more things are automated. Yet as Carr explores in The Glass Cage, automation extracts a cost. Removing “complexity from jobs, diminishing the challenge they present and hence the level of engagement they promote.” This doesn’t mean that Carr is anti-automation. He’s not. He just wants us to see another side.

“All too often,” Carr warns, “automation frees us from that which makes us feel free.”