• Probabilistic programming goes large scale: From reducing email clutter to any machine learning task

    Posted by George Thomas Jr.

    Probabilistic programming solves inbox clutter

    How is it you seem to be spending more and more time every day sifting through and prioritizing email messages? According to research by The Radicati Group, Inc., the legitimate emails you receive — already upwards of 100 per day — will only continue to increase. So how can you stem the tide of information overload without sacrificing more of your already precious time?

    That's where probabilistic programming becomes relevant to Microsoft's efforts to enhance productivity. In what is believed to be the first large-scale commercial use of this innovative programming paradigm, a recently released feature in Office 365 called Clutter intelligently learns which emails matter most to you and sorts them accordingly, filtering those less-urgent emails into a Clutter folder and allowing users to focus on the most immediately important emails.

    Probabilistic programming solves inbox clutter

  • Cryptographer's challenge: Keeping genetic secrets while advancing genetic research

    Posted by Allison Linn

    Kristin Lauter

    Kristin Lauter is solving a problem you may not even know you have: She's working to keep your most personal data private and secure.

    We're not talking about your bank account balance or even your Social Security number. Lauter, a mathematician and cryptographer, is at the forefront of a push to make sure human genome data can be stored, accessed and used for research – without falling victim to prying eyes.

  • Chinese Tradition Inspires Machine Learning Advancements, Product Contributions

    Posted by George Thomas Jr.

    Machine learning solves Chinese riddles

    A new online Chinese riddle game is steeped in more than just tradition. In fact, the machine learning and artificial intelligence that fuels it derives from years of research that also helps drive Bing Search, Bing Translator, the Translator App for Windows Phone, and other products.

    Launched in celebration of the Chinese New Year, Microsoft Chinese Character Riddle is based on the two-player game unique to Chinese traditional culture and part of the Chinese Lantern Festival. Developed by the Natural Language Computing Group in the Microsoft Research's Beijing lab, it resulted from a challenge between colleagues to build a computer that could solve riddles, and, in turn, has led to numerous machine translation advancements and patents.

  • Microsoft Research Expands Microsoft Band Productivity Functionality

    Posted by Microsoft Research

    Band gets machine-learning enhancements

    When Microsoft Band debuted in October 2014, it was more than a wearable fitness device. In addition to valuable health-related features such as a 24-hour heart-rate monitor, Guided Workouts, and built-in GPS tracking, it allowed users to preview their emails and texts, get calendar alerts, get actionable insights from Microsoft Health, and connect to the Cortana digital assistant.

    Users caught on quickly—and immediately clamored for more productivity features, including the ability to respond quickly and naturally to urgent messages. Thanks to cutting-edge technologies incubated by Microsoft researchers, a variety of new features have been introduced for Windows Phone 8.1 users in the latest update to Microsoft Band, which was released on Monday. They include an innovative virtual keyboard designed specifically for the device and new voice reply functionality powered by Cortana.

  • Machine Learning Gets Big Boost from Ultra-Efficient Convolutional Neural Network Accelerator

    Posted by Doug Burger

    Doug Burger

    (Editor’s Note: Doug Burger, a processor architect by training, is a Microsoft researcher focused on disrupting the very fabric of datacenter processing power in a mobile-first, cloud-first world.)

    I’m excited to highlight a breakthrough in high-performance machine learning from Microsoft researchers.

    Before describing our results, some background may be helpful. The high-level architecture of datacenter servers has been generally stable for many years, based on some combination of CPUs, DRAM, Ethernet, and disks (with solid-state drives a more recent addition). While the capacities and speeds of the components—and the datacenter scale--have grown, the basic server architecture has evolved slowly. This slow evolution is likely to change, however, as the decelerating gains from silicon scaling are opening the door to more radical changes in datacenter architecture.