Wear Plaid and Hang Out at Coffee Shops? In the Future, Facebook May Know You’re a Hipster.

Artificial intelligence is coming for your social media.

National Journal
Brian Resnick
Dec. 16, 2013, midnight

So you grow some scruff, start wear­ing tight jeans, oc­ca­sion­ally throw on sus­pend­ers, and reg­u­larly don plaid. You start hanging out at that ar­tis­an­al kom­bucha spot down the street — you know, the one with all the wick­er? And then one morn­ing — be­fore you wax that 360-de­gree handle­bar — when you log onto Face­book, you no­tice something dif­fer­ent.

There’s one ad for sock sus­pend­ers, and an­oth­er one for Amish-made beard con­di­tion­er*.

At parties and on the streets, it’s ob­vi­ous — you’re a total hip­ster. And now Face­book knows it too, just by the pho­tos you’ve up­loaded.

While this dysto­pi­an scen­ario is still set in an un­deter­mined fu­ture, the idea that so­cial me­dia can scan your pho­tos for con­tex­tu­al clues about life­style has already been proven. In his lab at Uni­versity of Cali­for­nia San Diego, Serge J. Be­longie and his stu­dents have cre­ated a com­puter al­gorithm that can guess what “urb­an tribe” (i.e., sub­cul­ture) you be­long to.

Hu­mans can do this al­most without think­ing. We hold ste­reo­types of what the word “hip­ster” im­plies, but we know that not one of these vari­ables com­pletely defines the term. A “hip­ster” is the sum of many parts, like a plaid shirt or tight jeans.

This concept — the abil­ity to use con­tex­tu­al clues to define an ob­ject — is what artists and psy­cho­lo­gists call gestalt. But that type of cog­ni­tion is not easy for a com­puter, which op­er­ates on hard rules.

“That’s the theme in my re­search,” Be­longie says. “Whenev­er I stumble across a pat­tern that people can see but is not im­me­di­ately evid­ent from a quant­it­at­ive per­spect­ive, I’d like to probe that to fig­ure out what’s caus­ing that.”

Be­longie and his team com­pile ref­er­ence pho­tos for each urb­an tribe and then “teach” the com­puter to re­cog­nize the pat­terns in col­ors, tex­tures, and geo­met­ric ar­range­ments. The com­puter then “combs through the data­base and looks for ex­cep­tion­al ex­amples of each of those cat­egor­ies.” In oth­er words, Be­longie is try­ing to teach the com­puter the gestalt it needs to nav­ig­ate a high school cafet­er­ia.

Once the com­puter gets a feel for these pat­terns, it can start guess­ing. Be­longie’s pro­gram isn’t per­fect. The ma­chine op­er­ates at just 39 per­cent bet­ter than chance, Be­longie says. “This is something that we’re just crack­ing open a new re­search area.”

A Seis­mic Shift

Be­longie’s work goes bey­ond hip­sters and goths. This was just an off­shoot of his more sober work — teach­ing com­puters to re­cog­nize can­cer in a biopsy slide or dead cor­al from satel­lite pho­tos.

And yet, he says his re­search can’t ig­nore the pull of so­cial me­dia. “In the old days,” he says, “It was NSF and NIH and DARPA grants — that’s what was kept this field rolling.” But now com­pan­ies like Face­book and Google are in­vest­ing heav­ily in ar­ti­fi­cial in­tel­li­gence, and pick­ing off his stu­dents when they gradu­ate.

The de­fin­ing story of the dec­ade con­tin­ues to be the ap­plic­a­tions of big data. Yes, the Snowden leaks were the biggest story of the year, but they speak to the lar­ger trend of what huge in­sti­tu­tions, such as the U.S. gov­ern­ment, can do when they can pro­cess tera­bytes of data quickly in­to us­able in­form­a­tion. But here’s where that story is start­ing to shift. Big data is go­ing to gain (great­er) con­scious­ness. Or at least that’s where the re­search money is head­ing.

Just re­cently, Face­book an­nounced it is open­ing a gi­ant ar­ti­fi­cial in­tel­li­gence lab to be pi­loted by Yann LeCun, a re­search­er in the field from New York Uni­versity. “The set of tech­no­lo­gies that we’ll be work­ing on is es­sen­tially any­thing that can make ma­chines more in­tel­li­gent,” LeCun tells WIRED about the ini­ti­at­ive.

Google, too, re­cently hired Ray Kur­z­weil, a fu­tur­ist who has pre­dicted that com­puter in­tel­li­gence will ex­ceed hu­man in­tel­li­gence by 2045, to work on ma­chine learn­ing pro­jects. “I en­vi­sion, some years from now, that the ma­jor­ity of search quer­ies will be answered without you ac­tu­ally ask­ing,” he told Sin­gu­lar­ity Hub after he was hired. “It’ll just know this is something that you’re go­ing to want to see.”

Which is kind of creepy, right?

Let’s put it in­to this con­text: Cur­rently Face­book does a lot with its user data, such as run­ning mass so­cial ex­per­i­ments on Elec­tion Day, and re­search­ing at which rates wo­men take the names of their hus­bands when they get mar­ried (the so­cial net­work makes all these stud­ies pub­lic). But if that’s creepy, what’s to come is still creepi­er.

“There is a huge, seis­mic shift that is hap­pen­ing, that every com­pany wants ar­ti­fi­cial in­tel­li­gence in some way,” Be­longie ex­plains. “They are just drown­ing in big data.”

And an enorm­ous amount of that data is in im­age form, which up to now, hasn’t fed back mean­ing­ful in­form­a­tion about users. “These im­ages are just sit­ting in these serv­ers like dark mat­ter,” he says.

Be­longie says it’s part of the “nat­ur­al pro­gres­sion,” that these com­pan­ies will use pro­grams like his own “for good or for ill — but most likely just to serve up more rel­ev­ant ads.”

I asked Be­longie if he thought his own work was “creepy.”

“I’ve lost my ob­jectiv­ity,” he laughs, “be­cause I find these prob­lems so in­ter­est­ing from a sci­entif­ic per­spect­ive. I don’t find it creepy, but I think people need to be aware of what they are shar­ing.”

*Ed­it­or’s Note: A won­der­ful product.

COR­REC­TION: This art­icle orig­nally un­der­stated the ac­cur­acy of the Urb­an Tribe pro­gram. The sys­tem guesses cor­rectly 48 per­cent of the time.

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