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Posted by Rob Knies
On Feb. 28, at the Santa Clara (Calif.) Convention Center, Kate Crawford, principal researcher at Microsoft Research New England, took the stage during the Strata Conference to deliver an illuminating, 17-minute talk entitled Algorithmic Illusions: Hidden Biases of Big Data.During that presentation, she cautioned that data and collections of data are not objective. They are created and shaped by human beings, and understanding the unavoidable hidden biases people bring to data collection and analysis can be as significant as the data themselves.Now, on the heels of that appearance, Crawford is bringing a similar message to a different audience, that of the Harvard Business Review, which has just published her contributed article, Big Data Has a Signal Problem, that underscores the concepts she discussed during Strata 2013.
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.
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.
Any businessperson in a large organization can testify about the challenges growth can bring. As a business gets larger, for example, the number of employees increases. Further growth might mean multiple offices—some, perhaps, located in distant lands.Ideally, you want your employees all tied into the same network, accessing the appropriate resources and communicating effectively. That can grow difficult, though, once the employee count begins to rise and spills into multiple locations. Managing access to network resources is important—and it isn’t easy.That’s where Management of Access Control in the Enterprise (MACE) comes in. This tool, available for download, enables administrators to collect data from one or more servers and visualize that information to understand who has access to what—which user or security group has read/write access to which resources, be it folders, shares, or File Classification Infrastructure (FCI) files.
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.