From the Lab that Brought You Siri, It’s Trapit—A Personalized Discovery Engine
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they’re really just bringing together stories that have been flagged by the people you follow on Facebook, Twitter, LinkedIn, or Google+. The information is only “personalized” in the sense that no one else has the same group of friends as you.
“This whole idea of using your social networks to find information is not really a solution, it’s an accommodation,” Gary Griffiths, Trapit’s CEO, argued to me recently. “It’s a recognition that there is way more stuff out there than you can ever find, so you just say, ‘I’m going to let my friends find it for me.'”
But what if your friends don’t share all of your interests, or aren’t very good at ferreting out the kinds of obscure news tidbits that might make your day, or are just dull people? The view at TrapIt is that the Web is a lot wider and deeper than either you or your friends can cope with—so wide and deep, in fact, that only software can be trusted to uncover the buried content that would be gold to you but dross to someone else. Hank Notthaft, Jr., chief product officer at Trapit, calls it “a discovery engine built for a Web that has grown so large and dynamic that it demands true personalization…While other platforms, from Google to Facebook and Twitter, are limited to what’s popular or what your so-called friends think is important, Trapit delivers what’s important to you.”
Like Siri, Trapit is an offshoot of the five-year, $200 million CALO research project. The goal of the project, which was based at SRI International in Menlo Park, CA and funded by the Defense Advanced Research Projects Agency (DARPA), was audacious: to create “systems that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise,” in the words of an SRI website.
Siri, which was spun off by SRI as a separate company in 2008 and acquired by Apple in April 2010, represents just one embodiment of that vision. The smartphone app is very good at parsing your spoken requests and assembling answers using the resources and data at hand, such as your contact list, calendar appointments, and current location.
Trapit is a very different embodiment. Its roots are in a program called CALO News Assistant, a prototype system built to help military personnel sift through news articles and blog posts. Users could select topics based on their fields, skills, or assignments and receive related article summaries via e-mail. “CALO learns the kind of articles you read and will highlight articles that best match your interests, focusing your attention on the most important articles,” in the words of one SRI brochure describing the system.
Nothhaft, who had worked as a product manager at WebEx and as a product marketing executive at business-telecom startup LiteScape Technologies, joined SRI in 2009 as an entrepreneur-in-residence, originally with the goal of sniffing out business collaboration and meeting technologies that might be ready for commercialization. He didn’t find anything that was ripe in those areas, but he did learn about CALO News Assistant. Nothhaft says he became “very excited…I’m an analyst by nature, and I’ve always been an information hound, with 2,000 feeds in my RSS reader.”
He thought that if the news assistant could be adapted for mainstream news consumers, it would be a powerful alternative to existing aggregation systems like that RSS reader. But there were two big problems: the news assistant scoured a limited selection of sources, and … Next Page »