• Trill Moves Big Data Faster, by Orders of Magnitude

    Posted by George Thomas Jr.

    Trill moves big data faster

    In today’s high-productivity computing environments that process dizzying amounts of data each millisecond, a research project named for “a trillion events per day” may seem relatively ordinary.

    But when you understand that Trill, a new high-performance streaming analytics engine developed by Microsoft researchers, can process data at two to four orders of magnitude faster than today’s streaming engines, well, now you’re getting into “wow” territory, especially considering Trill is just a .NET library.

  • Eric Horvitz Receives AAAI Feigenbaum Prize; Shares Reflections On AI Research

    Posted by Eric Horvitz

    Eric Horvitz on Minds and Machines

    Editor's Note: Eric Horvitz, managing director of Microsoft Research’s Redmond Lab, today shares some reflections upon receiving the AAAI Feigenbaum Prize. Horvitz is being recognized by the AAAI for “sustained and high-impact contributions to the field of artificial intelligence through the development of computational models of perception, reflection and action, and their application in time-critical decision making, and intelligent information, traffic, and healthcare systems.”

    How do our minds work? How can our thinking, perceiving, and all of our experiences arise in networks of neurons? I have wondered about answers to these questions for as long as I can remember. Until just a few decades ago, discussions on mind and brain generally occurred within philosophy and theology. Over the last century, research in psychology, biology, and computer science has brought into focus intriguing results and directions for approaching a science of intelligence.

  • New Research Brings Precision to Sampling Methods Used in Statistics and Machine Learning

    Posted by George Thomas Jr.

    Daniel Tarlow and Tom Minka

    Addressing one of the core problems in statistics and machine learning, Microsoft researchers have developed a new, more efficient algorithm that enables exact sampling.

    Researchers Daniel Tarlow, Tom Minka, and former Microsoft intern Chris Maddison introduced the algorithm in their paper, A* Sampling, one of only two of the 1,700 submitted that received an Outstanding Paper Award at NIPS 2014, the renowned machine learning conference of the Neural Information Processing Systems Foundation.

  • Top Posts of 2014: Deep Learning, Predictive Analytics, and Human-Computer Interaction

    Posted by Microsoft Research

    Best blog posts of 2014

    Our most popular blog posts of 2014 reflect the breadth of our research and our collaborative efforts across multiple product groups as well as with external organizations worldwide. From 3-D visualization to unveiling the mysteries of quantum computing to elevating the science of predictive analytics, learn more about how Microsoft Research continues to advance the state of the art in computing.

  • Addressing Fairness, Accountability, and Transparency in Machine Learning

    Posted by Microsoft Research

    Hanna Wallach

    Machine learning and big data are certainly hot topics that emerged within the tech community in 2014. But what are the real-world implications for how we interpret what happens inside the data centers that churn through mountains of seemingly endless data?

    For Microsoft machine learning researcher Hanna Wallach (@hannawallach), opportunity lies outside the box. As an invited speaker at the NIPS 2014 workshop on Fairness, Accountability, and Transparency in Machine Learning, Wallach spoke about how her shift in research to the emerging field of computational social science led her to new insights about how machine learning methods can be applied to analyze real-world data about society.