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Posted by Rob Knies
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 Inria; Iain 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.
Over at the Next at Microsoft blog, Steve Clayton has just published a post about the latest issue of the Things We’ve Learnt About … series from the Socio-Digital Systems (SDS) group at Microsoft Research Cambridge.This issue, the third in this compelling, visually stunning series of magazine-type treatments, focuses on search and web use—or, more specifically, what it means to move beyond search.The latest copy is available for download, as are its predecessors, which addressed the areas of communication and memory, respectively. What you’ll find is a few dozen pages of incisive text blocks, liberally seasoned with eye-popping graphics, that look beyond the search engines and mechanisms currently in vogue to what the future could hold.
In February 2012, in response to an initiative from the administration of U.S. President Obama to harness technology and innovation to encourage development for longtime scientific challenges such as health, food security, and climate change, the U.S. Patent and Trademark Office launched Patents for Humanity, a program to recognize those who use patented technology to aid the less fortunate.The inaugural winners are in, and prominent among them is Infer.NET, a Microsoft Research Cambridge library for machine learning, which won one of the contest’s four categories, information technology.The awards were presented April 11 in the Dirksen Senate Office Building on Capitol Hill in Washington, D.C. Teresa Stanek Rea, acting undersecretary of Commerce for Intellectual Property and acting director of the U.S. Patent and Trademark Office, made the presentations, and U.S. Sen. Patrick Leahy, chairman of the Senate Judiciary Committee, who introduced last year’s Patents for Humanity Program Improvement Act, spoke during the event. Fred Humphries, Microsoft vice president for U.S. Government Affairs, accepted the award.John Winn, along with Tom Minka one of the inventors of Infer.NET, didn’t exactly see such an honor coming.
Unlocking the future—that was the theme Rick Rashid, Microsoft chief research officer, used to close his opening remarks April 23 during the first day of the Microsoft Research Machine Learning Summit 2013.The event, held at Microsoft’s Le Campus site in Issy-les-Moulineaux, France, just outside of Paris, gathered thought leaders and researchers from a broad range of computing-related disciplines to focus on key challenges in a new era of machine learning and to identify what will be necessary to take advantage of the information resources of today and tomorrow to enhance society at large.Co-chair Evelyne Viegas of Microsoft Research Connections opened the summit with a few introductory remarks before introducing Alain Crozier, president of Microsoft France, who welcomed the approximately 250 attendees to the event. Viegas then took the opportunity to bring Rashid to the stage for his introductory remarks.
Posted by Eric Horvitz and Munmun De Choudhury
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.