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. Researchers work to secure Azure Blockchain smart contracts with formal verification

    In its young existence, the tamperproof and distributed ledger technology blockchain has already generated a lot of buzz and is being seen as disruptive, influencing approaches in such diverse areas as financial services, supply chains, and governance. To say its future is bright might be an understatement. According to Gartner, the technology is positioned to bring an added business value upward of $360 billion by 2026. One of the key drivers making blockchain-based applications programmable,…

    June 3rd, 2019

  2. What’s in a name? Using Bias to Fight Bias in Occupational Classification

    Bias in AI is a big problem. In particular, AI can compound the effects of existing societal biases: in a recruiting tool, if more men than women are software engineers, AI is likely to use that data to identify job applicants and overscreen for men, creating a vicious circle of bias. Indeed, Amazon recently scrapped its AI recruiting engine project for that reason. Now that AI is increasingly used in high-impact applications, such as criminal…

    May 31st, 2019

  3. Fashion forward: Researchers, designers debut new tech on New York City runway

    In his work in visual merchandising, designer Kenroy Tyrell has used LED lights in displays for trade shows and showrooms. But on this particular day, he found himself using the technology in a way he never anticipated—as part of a garment he had created. Tyrell was one of 20 participants in the Brooklyn Public Library’s BKLYN Fashion Academy, a program geared toward exposing designers to the entrepreneurial side of the industry as they design collections.…

    May 30th, 2019

  4. Machine teaching with Dr. Patrice Simard

    Episode 78, May 29, 2019- Machine learning is a powerful tool that enables computers to learn by observing the world, recognizing patterns and self-training via experience. Much like humans. But while machines perform well when they can extract knowledge from large amounts of labeled data, their learning outcomes remain vastly inferior to humans when data is limited. That’s why Dr. Patrice Simard, Distinguished Engineer and head of the Machine Teaching group at Microsoft, is using…

    May 29th, 2019

  5. Swimming in creative waters: A young artist, an inventor, and a nurturing sea of family and colleagues

    Technical Fellow and Director of Microsoft Research Eric Horvitz was completely floored as he spun around and spied the stunning sculpture that had silently been wheeled in behind him. The evening’s gathering had been arranged to celebrate a special birthday. His wife, Mary, had just motioned to him to come join her as she prepared to deliver remarks to friends that had gathered at their home. But now, he stood before a dazzling and dramatic…

    May 28th, 2019

  6. The productive software engineer with Dr. Tom Zimmermann

    Episode 77, May 22, 2019- If you’re in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he’s here to help. How, you might ask? Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process.…

    May 22nd, 2019

  7. Robust Language Representation Learning via Multi-task Knowledge Distillation

    Language Representation Learning maps symbolic natural language texts (for example, words, phrases and sentences) to semantic vectors. Robust and universal language representations are crucial to achieving state-of-the-art results on many Natural Language Processing (NLP) tasks. Ensemble learning is one of the most effective approaches for improving model generalization and has been used to achieve new state-of-the-art results in a wide range of natural language understanding (NLU) tasks. However, ensemble learning typically consists of tens or…

    May 16th, 2019

  8. Believe your ears – Hitting all the right notes in spatial sound rendering at ICASSP 2019

    Mixed reality (MR) applications and devices are seeing increased adoption, integrating computation into the fabric of our daily lives. This requires realistic rendering of virtual audio-visual content to deliver sensory immersion to MR users. Producing renderings indistinguishable from reality within tight computational budgets is both a tantalizing and challenging goal. A key component is spatial sound rendering, which provides important auditory cues about the locations of various virtual events within 3D environments. Microsoft Research is…

    May 15th, 2019

  9. Speech and language: the crown jewel of AI with Dr. Xuedong Huang

    Episode 76, May 15, 2019 When was the last time you had a meaningful conversation with your computer… and felt like it truly understood you? Well, if Dr. Xuedong Huang, a Microsoft Technical Fellow and head of Microsoft’s Speech and Language group, is successful, you will. And if his track record holds true, it’ll be sooner than you think! On today’s podcast, Dr. Huang talks about his role as Microsoft’s Chief Speech Scientist, gives us…

    May 15th, 2019

  10. Creating AI glass boxes – Open sourcing a library to enable intelligibility in machine learning

    When AI systems impact people’s lives, it is critically important that people understand their behavior. By understanding their behavior, data scientists can properly debug their models. If able to reason how models behave, designers can convey that information to end users. If doctors, judges and other decision makers trust the models that underpin intelligent systems, they can make better decisions. More broadly, with fuller understanding of models, end users might more readily accept the products…

    May 10th, 2019

  11. 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

  12. 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