by Jeff Stibel
Humans know that the context of the question matters. If you ask a friend where to go to see jaguars, there is very little chance that, knowing you love cars, he’ll direct you to the zoo. Unless, of course, you both happen to be on safari in South America, in which case he would be crazy to send you to a car dealership. He also won’t send you to a dealership in Seattle, knowing you live in Los Angeles. Your friend intuitively understands the context because he knows where you are and who you are—your job, your hobbies, your family, your location, your other friends. It’s just easier to communicate with, and more fun to be around, people who know us. This is what humans do, and this is why we make and maintain friendships. Our brains are prediction engines, wired to connect disparate facts into coherent thoughts. Context allows us to make that happen.
Facebook is working to personalize a user’s search experience in just this way. It should come as no surprise that the effort is being led by a gaggle of Googlers. Roughly 10 percent of Facebook’s staff once worked at Google. Why? Because many of the brains at Google have already realized that the world of search has changed. If context matters, then search needs to evolve to incorporate one’s place in space and time. These days, there is nowhere better to do that than on Facebook.
Facebook organically creates search-like results by allowing users to ask natural questions of friends and receive conversational results. This happens through their feeds, profiles, status, and timeline. At one point, Facebook tried to link all of this contextual search to an advertising product called Beacon. It was a huge disaster precisely because it worked so well. Imagine the mixed emotions of a woman finding out about an imminent marriage proposal by Beacon suggesting that she consider looking for a wedding dress given that her boyfriend just bought an engagement ring!
If your best friend tells you on Facebook that you absolutely must try Wolfgang Puck’s new restaurant, come Saturday night, you probably won’t be Yelping Puck’s restaurant reviews, let alone searching for a different restaurant. You’ll just head out to see what Wolfgang is cooking up. This is the concept behind Chacha and Mahalo, “human-guided” search engines. We should do what we have always done and ask our friends or proxies for friends. Luckily, our friends proficiently tweet, follow, and like, and will soon pin, check-in, and stamp as well. The rise of social sharing and recommending threatens to undermine the very concept of search. If the internet can take in all this social information and combine it with personalized context, perhaps it will have an answer for you even before your friends do.
Remember, Google now has Google+, its own social network, which allows it to use conversations between friends to create context in its search engine. But that is not a solution in itself any more than were previous social networks. Just linking people together doesn’t necessarily add context. Facebook pushes recommendations, alerts, and messages through natural conversation, and as of 2013 started developing its own search product, called Facebook Graph Search, to help users find information based on their networks of friends without using a traditional search engine. Google is inevitably tied to its search box for the foreseeable future. While it could conceivably incorporate a friend’s feedback into search results, that method leads to an awkward conversation at best.
Many other companies are busy trying to make the machine appear to each user as if it’s an old friend. If you are searching for jaguars from the San Diego Zoo with FourSquare, Yelp, or Dartmouth’s Hapori, the engines will send you to the Wikipedia page of the big cat or even a description on the zoo’s website. Other upstarts, such as Quora, are going back to basics and creating a Q&A expert to hone in on context. Want to know more about a jaguar? Ask the person who built the car (or bred the cat) . . . a Jerry’s Guide for every question. Google, on the other hand, solves this problem by presenting a list of clarifications on the results page and asking you to choose “jaguar the cat” or “jaguar the car.”
We’re moving toward search becoming a kind of personal assistant that knows an awful lot about you. As a side note, some of you may be feeling quite uncomfortable at this point with this new virtual friend. My advice: get used to it. The benefits will be worth it. As Kevin Kelly has said: “Total personalization in this new world will require total transparency. That is going to be the price. If you want to have total personalization, you have to be totally transparent.”
IV
It’s clear that we are moving into a new era, one in which, if we allow the search engines to get to know us, the answers to our questions will be personalized and contextual. But what about the way we ask the question in the first place? When you strip away the layers of the search problem, you find at its core a communication problem. Humans speaking to machines, and so much ending up lost in translation.
Internet search has always been about text, always about a box in which one could enter words, sentences, and sometimes even questions. But this has never been an ideal interface; it leaves too much between the internet and the individual. Search should be about context, not text.
What if the box could become unbounded? How can we help a person, with all her linguistic nuances, turn questions into computer speak? How can we help a computer, with its cold-hearted calculations, understand the nuances of an emotional machine?
Google finds itself in a tough position. Search is, after all, what Google is all about. Their simple iconic page, with nothing more than a search box, is an integral part of Google’s very fabric. As all mammals know, it is hard to chew on your own flesh, even if doing so is needed to survive. But many others are taking advantage of Google’s blind spot and going after the meat of the search interface.
And that might be precisely why Mayer left Google. Remember that Yahoo! started without a search box. It was just a list of favorite sites. That worked in the Precambrian days of the web, but by the time the web got big enough, the only technical solution was a search box. So Yahoo! adopted one, quietly giving up all of Jerry’s lists. But timing is everything, and there is now an opportunity, once again, to move beyond the status quo. We are already seeing companies big and small enter the fray with a vengeance.
Five years ago, Mayer envisioned this exact scenario and outlined the ideal search engine: “It would be a machine that could answer that question, really. It would be one that could understand speech, questions, phrases, what entities you’re talking about, concepts. It would be able to search all of the world’s information, different ideas and concepts, and bring them back to you in a presentation that was really informative and coherent.” In short, it wouldn’t be Google, and it wouldn’t look anything like today’s search engines.
That was a year before Apple purchased a startup called Siri and two years before it introduced the natural language interface to the world by including it as the central feature in the iPhone 4S. Siri takes Mayer’s simple search box to the next logical step by removing the box. Siri is a search engine interface, and she’s one of the best we’ve come up with so far. She allows us to bypass the search box entirely on our phones and instead exchange questions and answers with our devices. We can ask “What’s the weather today?” or “How many calories are in a donut?” or “Did the Lakers win?” Depending on the clarity of our speech, we may even receive sensible answers to those questions.
Siri is truly remarkable as one of the first of her species, and she’s only going to get better. As anyone who has spent time with her knows, Siri is far from perfect. Despite her intelligence and pleasant demeanor, I have yet to see anyone I know walking around in constant conversation with her (outside of Morgan Freeman, and that is only during a commercial). In fact, sometimes it seems that she’s more entertainer than assistant—people love pulling Siri out to share some of her more amusing answers, like “I have no particular insight into the motivations of chickens” when asked “Why did the chicken cross the road?”
Siri isn’t the only one who can speak our language. Evi is Siri�
��s half-sister who only speaks to Android users. Evi is similar to Siri but also allows users to agree or disagree with her answers, a crowdsourcing feature that counts on the inputs of thousands of users to help her grow exponentially smarter by the day. In addition to Evi and Siri, there are even a couple of non-mobile–based search engines using natural language, such as Lexxe and Swingly.
The ultimate triumph for these voice-prompted search engines is to understand who they are talking to. My five-year-old daughter knows it is me on the phone, even from 3,000 miles away. One day Siri, Evi, and the others will too, and that will ultimately merge this new natural interface with the revolutions happening in contextual search. Nuance, perhaps the largest brain science company in the world, has a new search interface they call Nina that promises to recognize the speaker with pinpoint accuracy. Perhaps one day Nina will be able to give my daughter a run for her money.
The new generation of search will allow us to ask questions in our own natural language, and will provide personalized answers based on context and our unique preferences. We are in the early stages, to be sure, but change is afoot.
The natural consequence of this change is the death of search engines and search altogether. First, the engines. The trend has already turned on the engines. With new devices bursting onto the scene and the applications that come with them, people are using other means to find what they want. For the definition of a word, we use dictionary.com; for an explanation of a topic, we head straight to Wikipedia. If we’re curious about what people are saying about Justin Bieber’s new album, we head to Twitter. If we’re looking for a great gumbo recipe, we ask our Facebook friends. To find a restaurant, we use Yelp, and to browse new houses, we use Trulia or Redfin.
We are finding these things on our smartphones and tablets using apps, bypassing the World Wide Web altogether. The reality is that search was needed only when people didn’t know where to start. The web is now better defined, and it’s even being truncated into clusters or mini-webs. It’s just easier to go directly to appropriate content and bypass the search engine entirely. Not surprisingly, these clusters mimic the modularity of the brain.
Search is an old brain system: it is used more for primitive functions than for cognitive ones. In the brain, we find information through a process of spreading activation, which is a lot like osmosis. We think of something and then other related things come to mind. “Where are my keys?” leads to thoughts of my day, my path, a mental map of my house—a detour through the breakfast I forgot to clean up—then back to the couch in the living room, and voilà, my keys appear in between the cushions. One neuron fires, and that sets off a chain reaction of other nearby neurons. There is no search box, no list of results to choose from.
And then there’s this idea: perhaps the internet will feed you an answer before you ask the question. Imagine that the internet can read your thoughts. Your personal computer, now a personal assistant, knows you skipped breakfast, just as your brain knows you skipped breakfast. She also knows you have been in back-to-back meetings but that your schedule just cleared. So she offers a suggestion: “It’s 11:00 a.m. and you should really eat before your next meeting. D’Amore’s Pizza Express can deliver to you within 25 minutes. Shall I order your favorite, a large thin crust pizza, light on the cheese with extra red pepper flakes on the side?”
This is exciting and life-changing stuff. Next time you enter a keyword into a search bar, think about a world with no more search. Consider how, if the internet knew you a little better, it/he/she could have already provided you the answer before you knew you had the question.
The world of search is rapidly evolving, and the first step is to imagine and then understand what that might mean. Creating businesses, experiences, and new worlds around these changes is where the real fun begins. If we can imagine a world where we are having conversations with our machines, we can understand how to adapt to these natural conversations. With it will emerge new worlds of interaction between mind and machine. If we can imagine a world where the machine can anticipate our needs, we begin to understand how predictions are transformed into actions, with endless possibilities and alternatives. This world is barreling toward us, faster than any of us could have imagined.
Seven
Crowds | Poets | Shakespeare
That a bastard strumpet
Should get ahead in the court
That in love or in wine
Louis should seek easy glory,
Ah! There he is, ah! Here he is,
He who doesn’t have a care.
Thus begins one of six illicit poems circulating Paris in 1749. Sometimes recited, sometimes sung to the tunes of popular songs, the poems criticized and mocked King Louis XV and his new mistress. Not known for his sense of humor, the king demanded that the author of the poetry be tracked down and brought to justice for his blasphemy.
The finest detectives in France were assigned to the case, and soon police arrested medical student François Bonis, who had recited one of the poems in his parlor only a week before. However, after hours of questioning, it became clear that Mr. Bonis was not the author. He had copied one of the poems from a visiting priest, Jean Édouard. The police promptly arrested Mr. Édouard and brought him in for questioning. But he claimed that he had heard one of the poems recited by another priest, Inguimbert de Montange. De Montange too turned out not to be the author. The detectives redoubled their efforts. Surely if they followed the trail long enough, they would reach the true insurrectionist.
In the end, fourteen citizens were arrested, imprisoned in the Bastille for months, and ultimately exiled to the French countryside, far away from King Louis. This lot included priests, law clerks, students, and professors—all loyal subjects of the king who fiercely maintained their innocence. And by all accounts, the fourteen were guilty of nothing more than sharing a poem or singing a song in the wrong place at the wrong time.
The fourteen weren’t the only ones spreading poetic gossip; the streets of eighteenth-century Paris buzzed with chatter about public affairs. In a society that was only semiliterate, news traveled most effectively by word of mouth. Without the written word, songs containing stories of the day were common because putting news to the tune of a popular song made it easier to remember and share.
The police never found the author of the poems because there was no author. Or to look at it another way, everyone was an author. Someone would start a poem about the day’s events, then share it with someone who would recite it but also add their bit, and in turn that person would share it with someone else who would do the same. In a process not unlike that of evolution itself, the most memorable and intriguing pieces of the poem would survive, and the others would be forgotten. This would continue until a memorable song would come of the discord. The citizens of France, as only the French could do, had created the first news network, full of pomp and poetics.
The poems were written by and for the crowd. What started as a couple of cheeky lines quickly became full-scale literary works. The poems spread and grew at a rate that risked a riot or worse. The police had a true dilemma on their hands: they couldn’t simply arrest the author because there was no author. But stopping the network that was spreading the poems wasn’t an option either—it’s almost impossible to slow down a growing network. In either case, obtaining true justice for Louis XV would have meant the arrest and punishment of thousands, which surely would not have been a great public relations move for the unpopular king. The baffled police detectives settled for the next best thing: making examples out of the loudest fourteen in hopes of dissuading others from participating in the crowdsourced network.
I
Crowdsourcing isn’t new. From poems to pyramids, collaborative works by groups of people are as old as work itself. But with each new technological innovation, we’ve brought the crowds closer together. With the proliferation of email, blogs, instant mes
sages, tweets, tags, and pokes, we’re now able to communicate with large numbers of people quickly and seamlessly.
The oldest examples of crowdsourcing come from biology. Ants, of course, have a type of swarm intelligence, as do bees, wasps, and other networked insects. If there is a consciousness, a “light in the attic,” it is the colony as a whole. The crowd of insects creates swarm intelligence, just as the crowd of individuals created the poetry of 1749 Paris. But just like the Parisian police, we don’t really know how they are doing it.
One example of crowdsourcing that we know significantly more about is the human brain. But even there, science has taught us little about exactly how our crowd of neurons creates intelligence, consciousness, and creativity. There are many theories about consciousness but no real answers; the same is true of the other faculties that make humans unique. We believe that the brain is a prediction engine, a pattern recognition machine. We have experiences, we use those experiences to make predictions, and then we guess our way through the world. With each passing guess, we grow wiser. We are prone to fail, fumble, and guess, and that is what makes us intelligent. Brains are unique in their ability to learn through failure. We are more sophisticated versions of the chimps you see on the National Geographic Channel awkwardly groping at tools.
Bumbling baboons is truly what the greatest minds think about our greatest minds. Not surprisingly, even that description is just a guess. The field of neuroscience is too new, too nascent, to give us real insight. What we do know is that our network of neurons acts as a crowd. Each neuron performs a minor task that collectively forms a pattern. We see a snake and certain neurons fire. The snake bites us and other neurons fire. The next time we see a snake, both sets of neurons fire—we see the pattern—and then something unexpected happens: an entirely new group of neurons fire, the ones that make us jump. The scale at which this happens is truly epic: this minor interchange requires tens of millions of neurons firing in a linked chain of events. Just as the crowd of subjects passed King Louis’s poem from poet to poet, each neuron fires in response to other neurons.
