The Formula_How Algorithms Solve All Our Problems... and Create More

by Luke Dormehl

  Using his access to the University of California’s supercomputers, Smarr is currently working on creating a distributed planetary computer composed of a billion processors that—he claims within ten years—will allow scientists to create working algorithmic models of the human body. What, after all, is the body if not a complex system capable of tweaks and modifications?

  As journalist Mark Bowden observed in a 2012 article for the Atlantic, “If past thinkers leaned heavily on the steam engine as an all-purpose analogy—e.g., contents under pressure will explode (think Marx’s ideas on revolution or Freud’s about repressed desire)—today we prefer our metaphors to be electronic.”

  And when it comes to symbolic representations, many people (Smarr included) prefer formulas to metaphors.3

  Self-Knowledge Through Numbers

  Fitting an “n = 1” study in which just one person is the subject, Larry Smarr’s story is exceptional. Not everyone is an expert in supercomputing and not everyone has the ability, nor the resources (his regimen costs between $5,000 and $10,000 each year) to capture huge amounts of personal data, or to make sense of it in the event that they do.

  But Smarr is not alone. As a data junkie, he is a valued member of the so-called Quantified Self movement: an ever-expanding group of similar individuals who enthusiastically take part in a form of self-tracking, somatic surveillance. Founded by Wired magazine editors Gary Wolf and Kevin Kelly in the mid-2000s, the Quantified Self movement casts its aspirations in bold philosophical terms, promising devotees “self-knowledge through numbers.”4 Taking the Positivist view of verification and empiricism, and combining this with a liberal dose of technological determinism, the Quantified Self movement begs the existential question of what kind of self can possibly exist that is unable to be number-crunched using the right algorithms?

  If Socrates concluded that the unexamined life was not worth living, then a 21st-century update might suggest the same of the unquantified life. As with René Descartes’ famous statement, cogito ergo sum (“I think, therefore I am”)—I measure, therefore I exist.

  In a sense, “Selfers” take Descartes’ ideas regarding the self to an even more granular level. Descartes imagined that consciousness could not be divided into pieces in the way that the body can, since it was not corporeal in form. Selfers believe that a person can be summarized effectively so long as the correct technology is used and the right data gathered. Inputs might be food consumed or the quality of surrounding air, while states can be measured through mood, arousal and blood oxygen levels, and performance in terms of mental and physical well-being.

  “I like the idea that someone, somewhere is collecting all of this data,” says Kevin Conboy, the creator of a quantified sex app, Bedpost, which I will return to later on in this book. “I have this sort of philosophical hope that these numbers exist somewhere, and that maybe when I die I’ll get to see them. The idea that computer code can give you an insight into your real life is a very powerful one.”

  A typical Quantified Self devotee (if there is such a thing) is “Michael.” Every night, Michael goes to bed wearing a headband sensor. He does this early, because this is when the sensor informs him that his sleep cycles are likely to be at their deepest and most restorative. When Michael wakes up he looks at the data for evidence of how well he slept. Then he gets up, does some push-ups and meditates for a while, before turning on his computer and loading a writing exercise called “750 Words” that asks him to write down the first 750 words that come to mind.5 When he has done this, text-analysis algorithms scour through the entry and pull up revealing stats about Michael’s mood, mind-set and current preoccupations—some of which he may not even be consciously aware he is worrying about. After this, he is finally ready to get moving (using a FitBit to monitor his steps, of course). If he doesn’t carry out these steps, he says, “I’m off for the rest of the day.”6

  Robo-cize the World

  While QS’s reliance on cutting-edge technology, social networking and freedom-through-surveillance might seem quintessentially modern—very much a creation of post-9/11 America—the roots of what can be described as “body-hacking” go back a number of years. The 1980s brought about the rise of the “robo-cized” athletes who used Nautilus, Stairmaster and other pieces of high-tech gym equipment to sculpt and hone their bodies to physical perfection. That same decade saw the advent of the portable technology known as the Sony Walkman (a nascent vision of Google Glass to come), which transformed public spaces into a controllable private experience.7 Building on this paradigm, the 1990s was home to MIT’s Wearable Computing Group, who took issue with what they considered to be the premature usage of the term “personal computer” and insisted that:

  A person’s computer should be worn, much as eyeglasses or clothing are worn, and interact with the user based on the context of the situation. With heads-up displays, unobtrusive input devices, personal wireless local area networks, and a host of other context sensing and communication tools, the wearable computer can act as an intelligent assistant, whether it be through a Remembrance Agent, augmented reality, or intellectual collectives.8

  There appear to be few limits to what today’s Quantified Selfers can measure. The beauty of the movement (if one can refer to it in such aesthetic terms) is the mass customization that it makes possible. By quantifying the self, a person can find apparently rigorous answers to questions as broad or specific as how many minutes of sleep are lost each night per unit of alcohol consumed, how consistent their golf swing is, or whether or not they should stay in their current job. Consider, for example, the story of a young female member of the Quantified Self movement, referred to only as “Angela.”

  Angela was working in what she considered to be her dream job, when she downloaded an app that “pinged” her multiple times each day, asking her to rate her mood each time. As patterns started to emerge in the data, Angela realized that her “mood score” showed that she wasn’t very happy at work, after all. When she discovered this, she handed in her notice and quit.

  “The one commonality that I see among people in the Quantified Self movement is that they have questions only the data can answer,” says 43-year-old Selfer Vincent Dean Boyce. “These questions may be very simplistic at first, but they very quickly become more complex. A person might be interested in knowing how many miles they’ve run. Technology makes that very easy to do. A more advanced question, however, would be not only how many miles a person has run, but how many other people have run the same amount? That’s where the data and algorithms come in. It’s about a quest for knowledge, a quest for a deeper understanding not only of ourselves, but also of the world we live in.”

  Boyce has always been interested in quantification. As a New York University student enrolled in the Interactive Telecommunications Program, he once attached some sensors, micro-controllers and an accelerometer to a model rocket and launched it off the ground. “What was interesting,” he says, “is that I was able to make a self-contained component that could be sent somewhere, that could gather information, and that I could then retrieve and learn something about.” After analyzing the rocket’s data, Boyce had his “Eureka!” moment. A lifelong skateboarder and surfer, he decided to attach similar sensors to his trusty skateboard and surfboard to measure the mechanical movements made by each. He also broke the surrounding environment down into hundreds of quantifiable variables, ranging from weather and time of day to (in the case of surfing) tidal changes and wave height. “From a Quantified Self standpoint,” Boyce notes, “I can . . . think about where it was that I surfed from a geospatial type of framework, or what equipment I was using, what the conditions were like . . . [It] represents me doing something in space and time.”

  In this way, Selfers return to the romantic image of the rugged individualists of the American frontier: an image regularly drawn upon by Silicon Valley industrialists and their followers. The man who tracks his data is no different fro
m the one who carves out his own area of land to live on, who draws his own water, generates his own power, and grows his own food. In a world in which user data and personal information is gathered and shared in unprecedented quantities, self-tracking represents an attempt to take back some measure of control. Like Google Maps, it puts the individual back at the center of his or her universe. “My belief is that this will one day become the norm,” Boyce says of the Quantified Self. “It will become a commodity, with its own sense of social currency.”

  Shopping Is Creating

  One of the chapters in Douglas Coupland’s debut novel Generation X, written at the birth of the networked computer age, is titled “Shopping Is Not Creating.”9 It is a wonderfully pithy observation about 1960s activism sold off as 1990s commercialism, from an author whose fiction books Microserfs and JPod perfectly lampoon techno-optimism at the turn of the millennium. It is also no longer true. Every time a person shops online (or in a supermarket using a loyalty card) their identity is slightly altered, being created and curated in such a way that is almost imperceptible.

  This isn’t just limited to shopping, of course. The same thing happens whenever you open a new web-browsing window and surf the Internet. Somewhere on a database far away, your movements have been identified and logged. Your IP address is recorded and “cookies” are installed on your machine, enabling you to be targeted more effectively with personalized advertisements and offers. Search regularly for news on a particular sport and you will begin to spot adverts related to it wherever you look—like the murderer played by Robert Walker in Hitchcock’s Strangers on a Train, who sees constant reminders of the woman he killed. Mention the words “Cape Town” in an e-mail, for instance, and watch the flood of “Cheap flights to South Africa” messages flood in.

  It was the American philosopher and psychologist William James who observed, in volume one of his 1890 text The Principles of Psychology, that “a man’s self is the sum total of all that he [can] call his, not only his body and his psychic powers, but his clothes and his house, his wife and children, his ancestors and friends, his reputation and works, his lands, and yacht and bank account.”10 This counts for double in the age of algorithms and The Formula. Based on a person’s location, the sites that they visit and spend time on, and the keywords that they use to search, statistical inferences are made about gender, race, social class, interests and disposable income on a constant basis. Visiting Perez Hilton suggests a very different thing from Gizmodo, while buying airline tickets says something different from buying video games. To all intents and purposes, when combined, these become the algorithmic self: identity and identification shifted to an entirely digital (and therefore measurable) plane.

  Your Pleasure Is Our Business

  Identity is big business in the age of The Formula. The ability to track user movements across different websites and servers has led to the rise of a massive industry of web analytics firms. These companies make it their mission not only to amass large amounts of information about individuals, but also to use proprietary algorithms to make sense of that data.

  One of the largest companies working in this area is called Quantcast. Headquartered in downtown San Francisco—but with additional offices in New York, Dublin, London, Detroit, Atlanta, Chicago and Los Angeles—Quantcast ranks among the top five companies in the world in terms of measuring audiences, having raised in excess of $53.2 million in venture capital funding since it was founded in 2006. Its business revolves around finding a formula that best describes specific users and then advising companies on how to best capitalize on this. “You move away from the human hypothesis of advertising,” explains cofounder Konrad Feldman, “where someone theorizes what the ideal audience for a product would be and where you might be able to find these people—to actually measuring an advertiser’s campaign, looking at what’s actually working, and then reverse-engineering the characteristics of an audience by analyzing massive quantities of data.”

  Before starting Quantcast, English-born University College London graduate Feldman founded another business in which he used algorithms to detect money laundering for some of the world’s leading banks. “We looked through the billions of transactions these banks deal with every month to find suspicious activity,” he says. It was looking at fraud that made Feldman aware of the power of algorithms’ ability to sort through masses of data for patterns that could be acted upon. “It could represent anything that people were interested in,” he says excitedly. “Finances were interesting data, but it only related to what people spend money on. The Internet, on the other hand, has information about interests and changes in trends on the macro and micro level, all in a single data format.” He was hooked.

  “Historically, measurement was done in retrospect, at the aggregate level,” Feldman says of the advertising industry. “That’s what people understood: the aggregate characteristics of an audience.” When Feldman first moved to the United States, he was baffled by the amount of advertising on television, which often represented 20 minutes out of every hour. It was a scattergun approach, rather like spraying machine-gun bullets into a river and hoping to hit individual fish. Whatever was caught was more or less done so by luck. Of course, a television channel can’t change it for every viewer, Feldman explains. The Internet, however, was different. Much like the customized user recommendations on Amazon, Quantcast’s algorithmically generated insights meant that online shopkeepers could redecorate the shop front for each new customer. In this way, audiences are able to be divided into demographics, psychographics, interests, lifestyles and other granular categories. “Yep, we’re almost psychic when it comes to reading behavior patterns and interpreting data,” brag Quantcast’s promotional materials. “We know before they do. We know before you do. We can tell you not only where your customers are going, but how they’re going to get there, so we can actually influence their paths.”

  Quantcast’s way of thinking is rapidly becoming the norm, both online and off. A Nashville-based start-up called Facedeals promises shops the opportunity to equip themselves with facial recognition-enabled cameras. Once installed, these cameras allow retailers to scan customers and link them to their Facebook profiles, then target them with personalized offers and services based upon the “likes” they have expressed online. In late 2013, UK supermarket giant Tesco announced similar plans to install video screens at its checkouts around the country, using inbuilt cameras equipped with custom algorithms to work out the age and gender of individual shoppers. Like loyalty cards on steroids, these would then allow customers to be shown tailored advertisements, which can be altered over time, depending on both the date and time of day, along with any extra insights gained from monitoring purchases. “It is time for a step-change in advertising,” said Simon Sugar, chief executive of Amscreen, who developed the OptimEyes technology behind the screens. “Brands deserve to know not just an estimation of how many eyeballs are viewing their adverts, but who they are, too.”11

  The Wave Theory

  This notion of appealing to users based on their individual surfing habits taps—ironically enough—into the so-called wave theory of futurist Alvin Toffler.12 In his 1980 book The Third Wave, Toffler described the way in which technology develops in waves, with each successive wave sweeping aside older societies and cultures.13 There have been three such waves to date, Toffler claimed. The first was agricultural in nature, replacing the hunter-gatherer cultures and centering on human labor. The second arrived with the Industrial Revolution, was built around large-scale machinery, and brought with it the various “masses” that proliferated in the years since: mass production, mass distribution, mass consumption, mass education, mass media, mass recreation, mass entertainment and weapons of mass destruction. The Third Wave, then, was the Information Age, ushering in a glorious era of “demassification” under which individual freedoms could finally be exercised outside the heaving constraints of mass society. Demassification would, Toffler
argued, be “the deepest social upheaval and creative restructuring of all time,” responsible for the “building [of] a remarkable new civilization from the ground up.” And it was all built on personalization.

  Please Hold to Be Connected to Our Algorithm

  It is well known that not every call-center agent is equipped to handle every type of call that comes in. The larger the company, the less likely it is that any one person will be able to deal with every single inquiry, which is the reason customers are typically routed to different departments in which agents are trained to have different skills and knowledge bases. A straightforward example might be the global company whose call centers regularly receive calls in several different languages. Both callers and agents may speak one or more of several possible languages, but not necessarily all of them. When the French-speaking customer phones up, they may be advised to press “1” on their keypad, while the English-speaking customer might be instructed to press “2.” They are then routed through to the person best suited to deal with their call.

  But what if—instead of simply redirecting customers to different call-center agents based upon language or specialist knowledge—an algorithm could be used to determine particular qualities of the person calling in: based upon speech patterns, the particular words they used, and even details as seemingly trivial as whether they said “um” or “err”—and then utilize these insights to put them through to the agent best suited for dealing with their emotional needs?