The Naked Future (17 page)

Facebook, it turns out, is particularly useful for researchers looking to separate fake friends from real ones. In sociology these two groups sometimes go by names that were originated in 1973 by sociologist Mark Granovetter: weak ties and strong ties. Our strong ties are the people with whom we interact often. These are family, friends, people we have a lot in common with, and with whom there is much homophily. Our weak ties are the people we add to our network without quite knowing why. Off-line, that category includes people you've had sparse interactions with, the girl from that cocktail party whom you remember as interesting, even if you can't remember her name. Before social networks these weak ties would orbit for a while and then be lost to oblivion. On Facebook your weak ties remain visible through the News Feed even if you interact with them very rarely.

The experiment showed that weak ties in
sum
exercise more
effect on your sharing than even your friends and family. They serve an important role introducing us to news and stimuli outside our normal circle. “These people share information from sources we might not frequent. As a result of seeing content from these users, you are many times more likely to share it. Surprisingly, while strong ties are individually more influential, weak ties are collectively more influential,” Bakshy told the crowd. Despite the reputation of Facebook as a place where people post mostly personal pictures of their kids and cats, in fact most of the links, articles, and other content that people share come from weak ties. It's not personal, but news, petitions, meme photos of a grouchy cat, stuff from outside. But it has allowed Facebook to better anticipate what sort of content, what memes, provocative blog posts, and other material its individual users will respond to, Bakshy says. The evidence suggests that the approach is working. For all the recent talk of Facebook being the passé social network, it continues to dominate in a number of key metrics, perhaps the most important of which is time on the Web site. The average Facebook user spends almost an hour a day on Facebook, far more than users spend on any other social network. No, our relationship with Facebook is not as exciting as it was. But it is stable, even marital.

It's also allowed Facebook to better predict which of the people in your network, whether real friends or weak ties, can move you closer to buying something, and by how much.

Skip ahead to a second experiment that Bakshy spearheaded in 2011. This one involved a far more modest subject pool of just 23 million Facebook users. It worked like this: the subjects saw a story in their News Feed, perhaps for a History Channel broadcast or the Tough Mudder decathlon sporting event. Unlike a provocative
New York Times
piece, a funny George Takei photo, or some bit of information that you may share naturally, the purpose of this story was clearly mercantile. It probably didn't look like a commercial but it was still a product endorsement, or, as Facebook calls it, a sponsored story
.

The subjects of the experiment were placed into three groups.
The first group saw the story and the identity of one friend who liked the associated product. The second two groups saw the story and the identities of more friends who liked the product. The stated goal was to measure the role of “social influence in social advertising.”

In advertising, well-paid celebrities have long been a proxy for the familiar. Psychologists Carl Senior and Baldeesh Gakhal have shown that we're more likely to buy something from a famous person than from someone who is merely beautiful, in part because we trust familiarity over physical attractiveness. No one is more familiar to you than your friends. That's why they're better pitchmen than virtually any celebrity.
²⁷

What Bakshy and his team found is that even the
slightest hint
of weak-tie friend affiliation with a product or brand can increase the probability that a Facebook user will “like” a product story by 10 percent. The effect is much more robust when users are informed by Facebook that one of their
strong ties
likes something. Now here's where contagion comes in: once you click “Like,” a seemingly innocuous action that the entire Facebook platform and
all
of its affiliates prompt you to do all the time, you become an advertisement to your friends. They see the sponsored story about the product you “endorsed.” A number of them see the link and also like it; next thing you know everyone is taking Nike+ challenges and has no idea why.

Bakshy and his team can also measure the dose-response function of each sponsored story to which you're exposed, in effect predicting your tolerance rate for this sort of marketing or even, one day in the not so distant future, how quickly certain friends exhaust their influence on you. Bakshy has indicated that this is a possible future direction for the research. After all, there are thousands of different ways people can be tied to one another and, within the context of a social network where every interaction can be seen and scored, many different ways to measure those interactions—including, in the words of Bakshy, “trust and intimacy.”

Facebook has already begun making use of these insights. A program called Facebook Offers lets businesses extend coupons and
deals directly to fans through the News Feed. When Facebook first announced offers, users couldn't control whether their friends saw if they accepted the offer; your acceptance was a tacit endorsement that was broadcast to your friends.
²⁸
In the summer of 2013, Facebook second-guessed this approach. Turns out when users agree to share the fact that they accepted an offer, they have a much bigger influence on their friends to accept that same offer.
²⁹

If you find yourself in a strange city, a program called Facebook Local Search, currently part of the company's mobile app, can give you a list of local places to visit. Today, it works a bit like a less fun Foursquare. But Facebook collects a lot more personal and connection data than its competitors. In the future Facebook Local Search and Facebook Home suggestions will be based on your habits, your likes and dislikes, and which friend recommendations are going to be most influential for you. You, too, will be making recommendations, perhaps without realizing it.
³⁰

Facebook assumes that you'll come to appreciate the additional personally relevant context in the ads to which you're exposed, the additional convenience of knowing that someone in your network liked a product that you're considering. They may be right. We may not yet feel at ease with the direction that advertising is taking but few of us have a strong attachment to its current manifestation in which we're constantly bombarded by images and sounds from people we do not know, selling us items we do not want, and incapable of hearing us when we voice our refusal.

The big-box retailers are trying to shrink themselves down to like size. In 2011 Walmart purchased Silicon Valley data-mining company Kosmix to help Walmart Labs get a handle on social network data from Facebook and Twitter. They deepened their investment in 2013 with the acquisition of predictive analytics start-up Inkiru. They're competing with dozens of other outlets and corporations including Target and Amazon to make the most out of whatever data about you they can get. In the hyper-personalized retail environment, your likes, dislikes, ZIP code, income, habits, gullibility, and friendships will one day affect not just your impulse buys and online shopping, but every
purchase you make at the gas station, the coffee shop, and the grocery store, even including the price you pay for tuna.

This is one of the areas of predictive analytics where Gryc sees opportunity and change. “Right now, the corporations can afford these analytics. They can afford the data. But what's going to happen in the next few years is that it will become a lot easier for consumers to calculate a lot of these metrics.”

The naked future envisioned by Gryc is one in which consumers and retailers are locked in an information arms race and he's the arms dealer. Today, one side has a clear and seemingly insurmountable advantage. But consumers have more information at their disposal than many realize. Any bank or wallet app can monitor in real time the amount of money we spend on things we don't really need and may not even want. A number of other apps can help you discover the consequences that a given purchase will have on your waistline, bank balance, or your goals. An app called Oroeco can even help you track and predict the effects of your purchases on the environment. Technology helps companies better predict where we'll be, what we'll buy, what we'll want, but it also helps consumers consume smarter, and sometimes even consume less. The big data present is one where companies use our data against us, to trick, coerce, and make inferences that benefit them at our expense. That behavior won't change in the future, but with a better awareness of what's going on and a willingness to experiment with the right tools we can make the fight a bit fairer. With enough personal record keeping, it's possible to turn the tables on the ever more coercive advertisers. For instance, using a QS system such as the earlier mentioned Tictrac, you can see how the media you expose yourself to affects your purchasing behavior, your ability to meet your own savings goals. Indeed, you can see how your exposure to Facebook changes your happiness and your financial security.

You have all the information that you need to help you resist ever more coercive mobile messaging; you give it away to your phone all the time. The next step is to start using it, to become smarter about
you. Imagine answering a push notification on your mobile device and seeing the following message:

There is an 80 percent probability you will regret this purchase.

The answer to highly customized, context-aware advertisements is the strategic, personal use of personal information. The war is just beginning. Both sides will experience victories and perhaps moments of true partnership.

But not every party will emerge from this war as a winner.

Back at Saatchi & Saatchi, there's a clamber of electric saws outside. Down the hall, long slabs of drywall sit against bare beams. Carpenter stations are set up. I ask Becky Wang if the company is expanding. She tells me that the offices are shrinking. “We lost some business,” she explains. One of the workmen stops by her office. “Hi!” she says. “I've been looking for you. I've been trying to use the computer in the hallway, but someone keeps stealing the keyboard. Can we move it?”

As we walk to the elevator, Becky gives me the names of people in New York and Silicon Valley whom she considers to be leaders in retail analytics. They work out of tiny start-ups that I've never heard of. The conversation feels a bit melancholy. The company where she works, with its carefully constructed facade of cool invincibility, is vanishing piece by piece. Before the elevator opens, I ask where I can follow up.

“Use my Gmail,” she says and then, quietly, as though not to alarm the people around her that the vessel they are on is flooding and she is stepping into the last lifeboat, “I'll be leaving here, too.”

Relearning How to Learn

THE
year is 2020. You're at a parent-teacher conference on the eve of the first day of a new school year. Your daughter is going into freshmen algebra tomorrow and you're at this conference to meet her new teacher. You show up armed with every math quiz, every math
problem
that your child has attempted throughout elementary and middle school, as well as a breakdown of how long she took on each and at what time during the day—after breakfast, before dinner—she performed best. The profile may even reveal whom your daughter talks to online, whom she studies with, and how those supposed friends influence her homework performance. This is a lot of information to carry around. If you were to print all this material, you would be dragging boxes along behind you. But this information is already stored on the cloud. All you have to do is give your child's teacher a link.

You have a request: “Would you mind taking all this data and creating an individual learning program for my daughter to make
positively
sure she finishes this year with an understanding of algebra? By the way, she's very shy, won't ask any questions in class,
and probably can't devote more than an hour to algebra a night. Thank you.”

Eight years of quiz scores footnoted and time-stamped? Facebook friends? Television-watching habits? What teacher in 2014 has the time to figure out the relevance of all that information? Not when lesson plans need writing, parents need to be called, and quizzes need grading. Thankfully, this isn't 2014.

Your daughter's teacher opens the link on her phone and downloads the relevant files. They're automatically run through a modeling app that sends her a notification. She suddenly knows exactly how well your kid will do on the first four quizzes, right down to which errors she's going to make. “It seems your daughter keeps reversing second- and third-order operations. We'll start drilling on those tomorrow. I'll schedule a half-hour online tutoring session for the evening, right after
Teen Mom
?”

“That would be great,” you answer, thankful that you aren't being asked to come between your daughter and your daughter's violent devotion to her favorite show on TV.

“There's one more thing,” says the teacher. “The profile shows that when Becky is confronted by a particularly hard problem she'll switch over to Drawsomewords 8 for five minutes or so. She seems to have great spatial-representation skills. I have a friend that designs drafting freeware at a studio downtown. I think that if we can get your daughter an internship, it might help her make the connection between math and drawing and then she'll exhibit a bit less resistance to second- and third-order operations.”

This offer seems generous, perhaps too much so. “Isn't she a bit young for an internship? I mean, it's her first year of high school.”

The teacher nods politely. “She's a bit late, actually. The average student her age has already started a company. But I think I can pull some strings.”

•   •   •

HOW
does the above scenario become reality (and do we want it to)? For starters, the feedback loop between a teacher
administering a lesson and a student taking a test needs to collapse to the second or two it takes a student to click a mouse. More important, the time and convenience costs of keeping records on individual student performance would need to fall to virtually zero. Finally, teachers, state education secretaries, administrators, parents, and employers would have to be willing to accept new performance metrics in place of what we today call grades. Every item on that list, except for the last one, exists in 2014.

But the most important step is philosophical. We need to acknowledge what education is today: essential, expensive, and in terrible shape. The United States spends more than $10,000 a year per elementary and secondary student; that's $2,000 above Japan and $4,000 above South Korea, two countries where students are outperforming us in science and math.

Even if we don't know how to invest in school, we understand its importance. We've absorbed the fact that high school should prepare students for college because a college degree has never been a more essential credential to join the middle class. People with different education levels experience the same national economy in dramatically different ways. Unemployment among people with a high school degree was 8 percent in December 2012. Among people with a bachelor's degree it was 4 percent. Statistically, people with a master's degree or higher saw no employment collapse during the Great Recession. While it's true that nearly half of all 2012 college grads in the United States were either unemployed or, far more likely, underemployed in low-wage jobs (and carrying an average of $27,000 in school debt), they were still faring better than their peers who did not have a degree.

This speaks to a national skills gap that's growing along the lines of economic class. Low-skilled jobs are partly being replaced by a smaller number of high-skilled jobs. Even as GM parts factories were shuttering in Michigan, kids in Silicon Valley were seeing their start-ups bought out in a matter of months. In many cases it wasn't because of the products or services those fledgling companies were building but because of the talent contained therein, a phenomenon sometimes called acqui-hiring.

Our nation's response to our education challenge (both locally and nationally) embodies the worst aspects of an obsolete mind-set. A slavish devotion to lecturing has been compounded by a nascent obsession with testing. Whether it's the Adequate Yearly Progress (AYP) reports mandated by No Child Left Behind, SAT scores, or just finals, the effect is the same: at the end of a designated interval—a week, a semester—teachers ask students to take a test. Too often we accept whatever result comes back as an objective and useful indication of the students' command of the material (administered via lecture). We do this despite the fact that history is full of intelligent people who didn't perform well on standardized tests and we know people forget information they've been successfully tested on. A lot of this testing is purely for the sake of identifying failing schools and teachers. Increasingly little of it has to do with helping students learn. Lectures make testing necessary. Testing makes lectures important. Testing is the big data present.

The naked future looks very different.

The year is 2007 and Stanford professor Andrew Ng is in front of four hundred students, giving his famous and highly rated lecture on machine learning. He asks a question of the undergrads assembled before him and observes three distinct behaviors in response.

Ten percent of the class is slumped back; these students are texting, checking Facebook, or recovering from hangovers. They're what you might call “zoned out.” About half the students are still madly typing the last thing said, displaying the sort of dedicated academic seriousness that propelled them through AP courses to get to Stanford. But they aren't raising their hands. Thirty percent or so sit quietly, waiting for someone else to answer. Only a few kids near the front, less than 1 percent by Ng's estimation, ask to be called on. If one of them gets the question right, Ng can breathe a sigh of relief and move on to the rest of the material.

The predictable dreariness of this lecture hall exchange began
to depress Ng. It's a scene you could find in virtually any lecture hall today. Indeed, the
lecture
has changed relatively little from the time of Socrates, as evinced by the fact that Plato spends most of
The Republic
following Socrates around taking notes. It's a method of teaching that has endured because it's functional, which is not exactly a compliment.

When Ng looked out over that horde of four hundred students, he recognized himself among them, one of the quiet kids, neither waving his hand nor asleep, simply sitting, passive and indecipherable.

“I was a shy kid back in school. So raising your hand and asking a question, or answering a question, I did that sometimes, but not always,” he tells me in his office on the Stanford campus.

Andrew Ng, it turns out, was fortunate to be a quiet student. If not for this quality of bashfulness, he would never have started his company, Coursera, which is remaking education for the twenty-first century.

Today, anyone in the world can familiarize themselves with the fundamentals of machine learning through Andrew Ng's massively open online course (MOOC). It boasted more than one hundred thousand alumni by July 2012. In his interactive instructional videos, Ng comes across very much as he does in real life. He is polite, serious, attentive, constrained in his movements, but friendly. He is not as shy as he was as an undergraduate at Carnegie Mellon but he remains an
exceptionally
soft-spoken man. Though he lectures quite successfully to auditoriums, he is clearly an instructor who thrives on one-on-one exchanges. His online course affords him the opportunity for this type of interaction with tens of thousands of people.

Coursera offers a huge departure in the way student performance is measured and understood. Instead of tests at the end of the week or semester, short, interactive quizzes are interspersed throughout the lesson, in keeping with the human attention span as science actually understands it (not how headmasters want it to be). Every student must interact with the material as they're studying it, not
afterward. This allows Ng's online platform to be not only an information distribution system but a telemetric data collection system.

“We can log every mouse click, every time you speed up or slow down the video, every time you replay a particular five-second piece of the video. Every quiz submission, be it right or wrong, we know exactly how many seconds you took to do every quiz, and every post you read or posted. We're starting to look at this data, which is giving us, I think, a new window into human learning,” Ng told me.

He admits that the subject matter in his machine-learning course is not easy. In fact, without a good understanding of linear algebra and at least some familiarity with statistics, the course is impossible. Chris Wilson from the online magazine
Slate
attempted the course and noted despairingly, “Avert your eyes, Mom, because I have a confession to make: I'm not entirely certain I'm going to pass.”

Writing code for learning algorithms doesn't become intuitive just because we want it to, or because the White House has a renewed interest in science, technology, engineering, and mathematics (STEM) education, or because someone designed a video game to teach it. Computer science will remain a difficult, multistep, and rule-filled domain because such is science. Though we are prone in the Internet era to lionize technology wizards the way we used to venerate rock stars, science and music aren't interchangeable. Science will never feel natural because it is not natural. For all his genius, Andrew Ng can't change this.

What telemetric education offers is the chance for all students to raise their hands and be heard. That opportunity doesn't come easily in a crowded classroom and especially not for women or minority students, many of whom feel that if they ask the wrong question or display ignorance, they'll confirm some unflattering, broadly held perception about their social group. We now understand this to be a real phenomenon, one that plays out in classrooms around the world every day, called stereotype threat.

It turns out other people's bad expectations are holding
you
back.

Here's how we know this is true. In 2006 Smith psychology researcher Maryjane Wraga and a few colleagues gathered together fifty-four female students and paid them $20 apiece to perform a series of spatial tests. Wraga divided them into three groups and told the first group that these were the sorts of tests women were expected to do well on and then told the second group that they were
not
expected to do well. In essence, two-thirds of the participants were told that they were going to confirm or refute either a positive or negative stereotype about all women. She didn't tell the third group (the control group) anything.

Each subject took the test under functional magnetic resonance imaging (fMRI). The women who were told they were being examined to confirm a negative stereotype showed activity in the part of the brain associated with processing anger and sadness (the rostral-ventral anterior cingulate) and the part of the brain charged with learning about social and interpersonal relationships (the right orbital gyrus). In other words, the subjects themselves encoded the stated premise of the experiment—that women were more likely to perform negatively on the test—as fact.

Conversely, the second group of women, the ones who were told that the test was intended to validate a positive stereotype about their sex, displayed activity in the portion of the brain associated with working memory (right anterior prefrontal cortex) as well as the portion of the brain associated with egocentric encoding (middle temporal gyrus), which is how we perceive objects in relation to us.

We've known that confidence can affect test performance but until Wraga's study, science didn't know exactly how large a role social stigma and stereotyping play in education. Wraga and her colleagues found that the women with the positive stereotype stimulus did
14 percent better
on the challenge than the women with negative stereotype stimulus.
¹

Stereotype threat could be a contributing factor in the fact that just 30 percent of African Americans and fewer than 20 percent of Latinos have an associate's degree (among those currently in their
twenties). It may also be one of the reasons why the United States now has a higher college dropout rate than any highly developed country.
²

Consider the implications of Wraga's findings, particularly that 14 percent performance differential between the two subject groups. Bad performance doesn't result in stereotyping; rather, the situation is reversed. When a person is continuously exposed to negative predictions about how she'll perform on a test as a factor of group affiliation it's the
prediction
that has a deleterious effect on performance. Coursera creates an environment where students are shielded from the effects of these predictions.