TR10: Modeling Surprise
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The resulting model works remarkably well, Horvitz says. When its parameters are set so that its false-positive rate shrinks to 5 percent, it still predicts about half of the surprises in Seattle’s traffic system. If that doesn’t sound impressive, consider that it tips drivers off to 50 percent more surprises than they would otherwise know about. Today, more than 5,000 Microsoft employees have this “surprise machine” loaded on their smart phones, and many have customized it to reflect their own preferences.
Horvitz’s group is working with Microsoft’s traffic and routing team on the possibility of commercializing aspects of SmartPhlow. And in 2005 Microsoft announced that it had licensed the core technology to Inrix of Kirkland, WA, which launched the Inrix Traffic application for Windows Mobile devices last March. The service offers traffic predictions, several minutes to five days in advance, for markets across the United States and England.
Although none of the technologies involved in SmartPhlow is entirely new, notes Daphne Koller, a probabilistic-modeling and machine-learning expert at Stanford University, their combination and application are unusual. “There has been a fair amount of work on anomaly detection in large data sets to detect things like credit card fraud or bioterrorism,” she says. But that work emphasizes the detection of present anomalies, she says, not the prediction of events that may occur in the near future. Additionally, most predictive models disregard statistical outliers; Horvitz’s specifically tracks them. The thing that makes his approach unique, though, is his focus on the human factor, Koller says: “He’s explicitly trying to model the human cognitive process.”
The question is how wide a range of human activities can be modeled this way. While the algorithms used in SmartPhlow are, of necessity, domain specific, Horvitz is convinced that the overall approach could be generalized to many other areas. He has already talked with political scientists about using surprise modeling to predict, say, unexpected conflicts. He is also optimistic that it could predict, for example, when an expert would be surprised by changes in housing prices in certain markets, in the Dow Jones Industrial Average, or in the exchange rate of a currency. It could even predict business trends. “Over the past few decades, companies have died because they didn’t foresee the rise of technologies that would lead to a major shift in the competitive landscape,” he says.
Most such applications are a long way off, Horvitz concedes. “This is a longer-term vision. But it’s very important, because it’s at the foundation of what we call wisdom: understanding what we don’t know.”