Today, a startup called TrapIt launched a beta website that recommends content after learning your tastes via an artificial-intelligence engine spun out of research originally funded by the Defense Advanced Research Projects Agency (DARPA). The company hopes this technological pedigree will set its method apart from other ways of finding information, such as searching or receiving recommendations from social-media sources.
TrapIt’s technology has its roots in the CALO project at the independent nonprofit research institution SRI. CALO is an ambitious attempt to help computers understand the intentions of their human users. A previous company spun out of CALO, Siri, developed an intelligent software assistant that could perform simple tasks, such as planning an evening out, when given voice commands. Apple acquired Siri in April 2010, although the technology has yet to appear in any Apple products.
TrapIt relies on a different part of CALO’s intelligence. “Learning from data is the property we’ve got our hands on,” says CEO and cofounder Gary Griffiths. He explains that in the aftermath of September 11, U.S. government agencies felt they’d had access to data that could have predicted the attacks, but they didn’t know where to look for it. DARPA funded CALO in part to work on this problem. The project sought ways to sift through information to find what might be most relevant to a given topic, and to learn from a user’s response to the information offered. It’s this technology that TrapIt is converting into a consumer product.
At first blush, TrapIt might look like any Web 2.0 site. After signing up, the user can select from existing “traps”—collections of articles related to featured or trending topics, such as the golfer Rory McIlroy, who just won the U.S. Open. The user can also create new traps by entering a few keywords and going through one screen of training data.
However, in either case, the traps then belong to the user, and they change according to his or her tastes alone—even if they were originally created by someone else. TrapIt’s algorithms comb through about 50,000 unique sources of content, analyzing articles to classify the types of information they contain. (The 50,000 sources were vetted by humans to filter out content farms and other material of dubious quality.) TrapIt combines this information with machine-learning analysis of what the user has previously clicked on to recommend new information.
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