The Naked Future by Patrick Tucker Read Free Book Online Page B

map of human mobility the way zoologists and biologists track the movement of bears or birds or lions; but because Microsoft was so flush with cash at the time, Krumm paid several hundred test subjects to carry GPS trackers around with them wherever they went, which broadcasted the wearers’ physical location every couple of seconds. Some people carried the trackers in their pockets and some had the tracker installed on the dashboard of their cars. Microsoft was considering a lot of potential uses for this data, from helping cities better understand traffic patterns to developing a new line of smart thermostats that could predict when customers were on their way home and accordingly turn on the heat. Another potential use was an intelligent calendar to be used in conjunction with Outlook (the default e-mail provider that comes with Windows), which could forecast your potential availability for appointments into the future. Krumm watched the trackers and the people to which they were connected sail through life for more than six years. Altogether, his seven hundred–plus subjects provided more than ninety years’ worth of data on human mobility.
    He presented the data set to Sadilek and they applied an algebraic technique called eigendecomposition to it. Decomposition in this sense simply means reducing a lot of numbers to a single value that’s in some way characteristic of the whole. Eigen is derived from the German word for “self.” Through eigendecomposition Sadilek and Krumm were able to create a model that could predict a subject’s location with higher than 80 percent accuracy up to eighty weeks in advance. 19
    Put another way, based on information stored in your phone, Sadilek and Krumm’s model can predict where you will be—down to the hour and within a square block—
one year and a half from right now.
    Granted, Krumm and Sadilek’s data set isn’t a typical one. Most of us don’t share geo-location information as frequently as did the folks Krumm put on the payroll. At least not yet. And most of us bounce between home, work, or school and back pretty regularly. In fact, if you know where someone usually is on a Monday at 10 A.M . you can infer their location on any given Monday at 10 A.M . fairly well, but it’s still just a guess based on two data points. The magic of Sadilek and Krumm’s Far Out model, as they named it, is that it factors in the occasional random detour—the flat tire, the unexpected work junket, or the sick day—without making those outlier events more significant than they are, without overfitting.
    A flat tire on a Monday at 10 A.M . isn’t actually random, according to the strict definition of the word. We just don’t yet know how to model it. A certain type of person, someone who wears his tires thin without replacing them, someone who drives through an area with lots of hazards, et cetera, is more likely to suffer a flat every few months than is someone who doesn’t take her car out as often, or to the same places, or who replaces her tires religiously. Sadilek’s system doesn’t explain why some people have more flats, but it does find some people are more prone to these anomalies than others. When you have a data set with enough points, even outliers can reveal a pattern.
    I asked Sadilek about how people respond to his work on the Far Out model when he tells them about it. Researchers, by and large, are intrigued and appreciate it. Folks outside the field, many of whom carry a GPS tracker in their pocket without ever realizing it, have a different reaction.
    â€œA small amount of people always worry about the privacy implications of this,” he answered the way a doctor may discuss the unfortunate symptoms of a chronic but medically interesting condition, as in,
Of course you will experience night terrors and eyebleeding; these are now just a part of your life.
The prescription: take this insight