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Spurring Group Communication with Machine Learning at Pol.is

By   /  April 24, 2014  /  No Comments

Taylor Soper of Geek Wire reports, “For the three entrepreneurs building pol.is, the problem was simple: Big groups of people trying to communicate effectively.”


Taylor Soper of Geek Wire reports, “For the three entrepreneurs building pol.is, the problem was simple: Big groups of people trying to communicate effectively about a certain topic online was largely inefficient. That’s why they started pol.is, a new Seattle company that has developed a way to combine polling data from hundreds of people with machine learning and interactive data visualization. The end result is a simple, clean way for anyone from college professors to market researchers to efficiently collect and package large amounts of data while enabling users to spur conversation based on all the input. ‘Getting large groups of people to communicate effectively is really painful,’ said CEO Colin Megill. ‘We are solving that’.”


Soper continues, “Megill, who has a social science background, said he’d been thinking about the problem of coordinating group behavior for years. When the Arab Spring and 2007 financial crisis hit, he then wondered how web technology could help fix that problem. A few years later he met co-founder Mike Bjorkegren, who was previously a software developer at Amazon. ‘We wanted it to be really simple, really fast, really parallel, with algorithms doing the work of picking groups it would take people months or years of ground work to do,’ Megill explained. The pair rounded out their founding team with Chris Small, a programs and systems analyst at Fred Hutchinson Cancer Research Center. ‘Chris was instrumental early on in helping us find and implement those algorithms.’ Megill said. ‘The three of us together moved from multiple prototypes to reality over the past year and a half’.”


Read more here.

Image: Courtesy Pol.is


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