Microsoft Research Blog

The Microsoft Research blog provides in-depth views and perspectives from our researchers, scientists and engineers, plus information about noteworthy events and conferences, scholarships, and fellowships designed for academic and scientific communities.

  1. Reinforcement learning for the real world with Dr. John Langford and Rafah Hosn

    Episode 75, May 8, 2019- Dr. John Langford, a partner researcher in the Machine Learning group at Microsoft Research New York City, is a reinforcement learning expert who is working, in his own words, to solve machine learning. Rafah Hosn, also of MSR New York, is a principal program manager who’s working to take that work to the world. If that sounds like big thinking in the Big Apple, well, New York City has always…

    May 8th, 2019

  2. SpaceFusion: Structuring the unstructured latent space for conversational AI

    A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn from diverse data sources such as those for conversations, narratives, images, and knowledge could open doors for researchers and scientists to develop AI systems capable of more general intelligence. For deep learning models today, datasets are usually represented by vectors…

    May 8th, 2019

  3. Incentivizing information explorers (when they’d really rather exploit)

    Everyone is familiar by now with recommendation systems such as on Netflix for movies, Amazon for products, Yelp for restaurants and TripAdvisor for travel. Indeed, quality recommendations are a crucial part of the value provided by these businesses. Recommendation systems encourage users to share feedback on their experiences and aggregates the feedback in order to provide users with higher quality recommendations – and, more generally, higher quality experiences – moving forward. From the point of…

    May 7th, 2019

  4. Autonomous soaring – AI on the fly

    The past few years have seen tremendous progress in reinforcement learning (RL). From complex games to robotic object manipulation, RL has qualitatively advanced the state of the art. However, modern RL techniques require a lot for success: a largely deterministic stationary environment, an accurate resettable simulator in which mistakes – and especially their consequences – are limited to the virtual sphere, powerful computers, and a lot of energy to run them. At Microsoft Research, we…

    May 7th, 2019

  5. Minimizing gaps in information access on social networks while maximizing the spread

    Lots of important information, including job openings and other kinds of advertising, are often transmitted by word-of-mouth in online settings. For example, social networks like LinkedIn are increasingly used as a way of spreading information about job opportunities, which can greatly affect people’s career development. The goal of social influence maximization, a well-studied problem, is to maximize the number of people in a social network that receive specific information, given a limited budget with which…

    May 6th, 2019

  6. New Advancements in Spoken Language Processing

    Deep learning algorithms, supported by the availability of powerful Azure computing infrastructure and massive training data, constitutes the most significant driving force in our AI evolution journey. In the past three years, Microsoft reached several historical AI milestones being the first to achieve human parity in the following public benchmark tasks that have been broadly used in the speech and language community: 2017: Speech Recognition on the conversational speech transcription task (Switchboard) 2018: Machine Translation…

    May 6th, 2019

  7. Beyond spell checkers: Enhancing the editing process with deep learning

    “Here’s my conference paper—what do you think?” After hours of agonizing over words and illustrations, sharing a draft document is a moment of great pride. All too often, this is shortly followed by embarrassment when your colleague gets back to you with reams of edits. Many of them may be simple grammar or style fixes or requests for citations—minor suggestions that aren’t as fulfilling or valuable for either author or editor as feedback regarding the…

    May 3rd, 2019

  8. Machine Reading Systems Are Becoming More Conversational

    A team of researchers from the Natural Language Processing (NLP) Group at Microsoft Research Asia (MSRA) and the Speech Dialog Research Group at Microsoft Redmond are currently leading in the Conversational Question Answering (CoQA) Challenge organized by Stanford University. In this challenge, machines are measured by their ability to understand a text passage and answer a series of interconnected questions that appear in a conversation. Microsoft is currently the only team to have reached human…

    May 3rd, 2019

  9. (Th)Inking with data — tapping into the potential of the digital pen

    Whether at work or at home, people are regularly gathering and interpreting information to build their base of knowledge and gain a deeper understanding of the world around them. In this endeavor, they encounter data in many shapes and forms—lines of text in books, timelines and charts in magazines and newspapers, photos in print and online. They’re searching webpages and browsing interactive maps to identify good schools for their children or to find a great…

    May 2nd, 2019

  10. Introducing TORC: A rigid haptic controller that renders elastic objects

    Oxymorons of haptics might be the new normal Consumer virtual reality (VR) systems can immerse users in wonderous virtual worlds, rich in sights and sounds. However, how many times have you reached for an object in VR or augmented reality (AR) and, just as you were about to grab it, you experienced the bitter sensorial shock that the object did not exist in the real world. That break on presence could be about to end.…

    May 2nd, 2019

  11. Deep InfoMax: Learning good representations through mutual information maximization

    As researchers continue to apply machine learning to more complex real-world problems, they’ll need to rely less on algorithms that require annotation. This is not only because labels are expensive, but also because supervised learners trained only to predict annotations tend not to generalize beyond structure in the data necessary for the given task. For instance, a neural network trained to classify images tends to do so based on texture that correlates with the class…

    May 1st, 2019

  12. CHI squared with Dr. Ken Hinckley and Dr. Meredith Ringel Morris

    Episode 74, May 1, 2019 - If you want to know what’s going on in the world of human computer interaction research, or what’s new at the CHI Conference on Human Factors in Computing Systems, you should hang out with Dr. Ken Hinckley, a principal researcher and research manager in the EPIC group at Microsoft Research, and Dr. Merrie Ringel Morris, a principal researcher and research manager in the Ability group. Both are prolific HCI…

    May 1st, 2019