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New York’s 311: Greater Efficiency Through Machine Learning

by Eileen McNulty
June 5, 2014
in Machine Learning, News
Home Topics Data Science Machine Learning
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Since 2003, New York has been using the “311” service to field non-emergency calls and questions. In a thriving and ever-developing hub such as New York, there have been alot of questions to answer- the Manhattan call centre receives 60,000 enquiries a day via phone, text, web and mobile app. This adds up to a mammoth 180 million enquiries since the founding of 311. You might imagine, in the age of Siri, the call centre would be a futuristic hub filled with automated voices, using supercomputers to comb through data and find answers. But Markus Mobius, a principal researcher with Microsoft Research, paints a very different picture.

“It looks like some kind of NASA control center,” he said of call centre, staff and operated entirely by human labour, 24 hours a day, 7 days a week. “It’s a very useful system. It’s high demand. But it is very, very labor intensive.”

Mobius, along with three colleagues from Microsoft Research (including a 16-year-old intern) were given the mission of partially automating this huge service. What they devised was a system which used a “router” of Natural Language Processing and Machine Learning, which understands the questions being asked and the information which needs to be extracted, and “bots”, which automatically supply the answers. For example, if someone asked “Is the school open today?” the router would be trained to know “today” equated to today’s date, and send a query to the “Are the schools open?” bot, which would provide the answer.

The innovation of this system, according to Mobius, is that it divides tasks between computers (the NLP and Machine Learning) and humans (programming the bots). They specifically designed the bots to be easy to programme, so that anyone with minimal training could add information to them, allowing the call centre to respond quickly in newly-emerging and emergency situations.

The initial plan for the service is to roll it out via the 311 mobile app, which currently doesn’t allow users to ask live questions, for fear of the service being flooded with queries. Beyond the mobile app, Mobius has grand ambitions for his newly-developed system; “With a relatively small number of bots we think we can automate between 20% and 40% of the questions submitted to 311,” he states.


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Tags: 311Microsoft ResearchNatural Language Processingnew york

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