• Helping the Low-Literate Learn to Navigate Through User Interfaces

    Posted by Rob Knies

    CHI 2013

    For several years, researchers from Microsoft Research India’s Technology for Emerging Markets (TEM) group have been studying how to design applications for economically poor communities such as those found in India.

    In particular, Indrani Medhi, a researcher at the India lab, has been focusing on user interfaces for low-literate and novice technology users. Medhi, who is completing her Ph.D. at the Industrial Design Centre at the Indian Institute of Technology Bombay, has co-written a paper accepted for the Association for Computing Machinery’s 2013 SIGCHI Conference on Human Factors in Computing Systems (CHI). The paper is titled Some Evidence for the Impact of Limited Education on Hierarchical User Interface Navigation and was written with Meera Lakshmanan, a translator and research assistant; Kentaro Toyama, a former head of TEM and now a researcher at the University of California, Berkeley and a fellow of the Dalai Lama Center for Ethics and Transformative Values at the Massachusetts Institute of Technology; and Edward Cutrell, Toyama’s successor as senior research manager of TEM.

    The paper examines one factor in application design for poor communities: the fact that users with little or no education have a diminished capacity to navigate a hierarchical user interface. Medhi’s work has explored ways that UIs can be designed for low-literate people by using text-free iconography that such users can recognize, but the challenge continues.

  • Pursuing Insights About Health and Well-Being from Social Media

    Posted by Eric Horvitz and Munmun De Choudhury

    generic image

    At Microsoft Research, we’ve been exploring the use of data analysis and machine learning to gain insights about health and well-being—and to enhance the quality of health care. Our efforts in this area include research on using data stored in electronic health records to construct predictive models that can provide physicians with advance warning about patient outcomes.

    We’ve worked with colleagues to develop systems that can predict the likelihood that a patient will contract an infection while in the hospital or that a patient being discharged will be readmitted to the hospital within a short time. Some of these models have been deployed and are in use at hospitals throughout the world, providing demonstrated value to patients and physicians.

    Beyond examining data from medical health records about hospitalized patients, we have been interested in the prospects of developing new methods that can transform anonymized data about the search and communications activities of people into a large-scale sensor network for public health. As an example of directions and opportunities in this realm, we recently showed how we can detect previously unknown drug interactions via analysis of anonymized web-search logs. We identified useful signals via analysis of tens of millions of queries sent to search engines by millions of users who had consented to share their search activities with Microsoft for research purposes.

  • Improved Healthcare via Machine Learning: a Way Forward

    Posted by Rob Knies

    Microsoft Research Machine Learning Summit logo

    The Microsoft Research Machine Learning Summit 2013 concluded with a plenary panel discussion titled Data Challenges and Opportunities in the Next Decade. Chaired by Jeannette Wing, Microsoft vice president and head of Microsoft Research International, the discussion included Eric Horvitz, Microsoft distinguished scientist and managing co-director of Microsoft Research Redmond; Michel Cosnard, president of InriaIain Buchan of the University of Manchester; and Lionel Tarassenko of the University of Oxford.

    My previous post ended with Hermann Hauser, co-founder of Amadeus Capital Partners, stating that machine learning would have a profound effect on the future of health care. That was interesting, because I had planned for the final post from the summit to focus on that very subject.

    Buchan is quite aware of that potential. A clinical professor of Public Health Informatics at the University of Manchester and director of the MRC Health eResearch Centre, his research interests lie in building effective models of health and in connecting patients and health professionals with more potent health information.

  • The Sixth Wave Is upon Us

    Posted by Rob Knies

    Microsoft Research Machine Learning Summit logo

    The second day of the Microsoft Research Machine Learning Summit 2013 got off to a rousing start with an hour-long plenary keynote by  serial entrepreneur Hermann Hauser, co-founder of Amadeus Capital Partners.

    Hauser, a physicist and a Fellow of the Royal Society, the Institute of Physics, and the Royal Academy of Engineering, has a long, successful history of in incubating IT companies, including U.K. computer maker Acorn Computers, a former subsidiary of which is now known as ARM Holdings, which dominates the market for chips used in mobile phones.

    His talk was called Machine Learning, the 6th Wave of Computing, and he began by referring back to 1947 and the Electronic Delay Storage Automatic Calculator (EDSAC), a prototype computer constructed by British computing pioneer Maurice Wilkes at the University of Cambridge.

  • Infer.NET: Machine Learning Tailor-Made

    Posted by Rob Knies

    Microsoft Research Machine Learning Summit logo

    One of the featured technologies on display on April 23, the first day of Microsoft Research Machine Learning Summit 2013, was Infer.NET, a powerful, compelling .NET library from Microsoft Research Cambridge.

    Infer.NET is an example of model-based machine learning, as explained by Tom Minka from the Cambridge lab during a morning talk.

    “It’s about trying to get more people to try machine learning,” said Minka, a senior researcher. “The traditional approach to this is that experts build prepackaged learners that are very generic and apply in a robust way to different data sets. But the problem with that approach is that it doesn’t account for domain knowledge. In lots of areas where we want to use machine learning, such as vision or speech or ecology, there is very strong domain knowledge.