| Description:
| Eric Horvitz, head of the Adaptive Systems and Interaction group at Microsoft Research, talks about surprise modeling.
- “Definition: Surprise modeling combines data mining and machine learning to help people do a better job of anticipating and coping with unusual events.
- Impact: Although research in the field is preliminary, surprise modeling could aid decision makers in a wide range of domains, such as traffic management, preventive medicine, military planning, politics, business, and finance.
- Context: A prototype that alerts users to surprises in Seattle traffic patterns has proved effective in field tests involving thousands of Microsoft employees. Studies investigating broader applications are now under way.”
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Accompanying article:
TR10: Modeling Surprise - Combining massive quantities of data, insights into human psychology, and machine learning can help humans manage surprising events, says Eric Horvitz. By M. Mitchell Waldrop. Technology Review, published by MIT (March/April 2008). "Much of modern life depends on forecasts: where the next hurricane will make landfall, how the stock market will react to falling home prices, who will win the next primary. While existing computer models predict many things fairly accurately, surprises still crop up, and we probably can\'t eliminate them. But Eric Horvitz, head of the Adaptive Systems and Interaction group at Microsoft Research, thinks we can at least minimize them, using a technique he calls 'surprise modeling.' Horvitz stresses that surprise modeling is not about building a technological crystal ball to predict what the stock market will do tomorrow, or what al-Qaeda might do next month. But, he says, 'We think we can apply these methodologies to look at the kinds of things that have surprised us in the past and then model the kinds of things that may surprise us in the future.' ... Granted, says Horvitz, it's a far-out vision. But it's given rise to a real-world application: SmartPhlow, a traffic-forecasting°© service that Horvitz's group has been developing and testing at Microsoft since 2003. ... To monitor surprises effectively, says Horvitz, the machine has to have both knowledge--a good cognitive model of what humans find surprising--and foresight: some way to predict a surprising event in time for the user to do something about it.”
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