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.

Most recent

  1. Five ways your academic research skills transfer to industry

    Graduate students are often coached to pursue a career in higher education, yet the number of available tenure-track positions falls far short of the number of candidates looking to fill them. In reality, about 14 percent of graduates with advanced science, engineering, and health degrees obtain a position in academia within three years of graduating, according to a 2018 National Science Foundation report. Instead, the vast majority of those with a PhD will pursue a…

    February 19th, 2020

  2. Data Visualization: Bridging the Gap Between Users and Information Webinar

    Feb. 19, 10  am PT - In this webinar led by Microsoft researcher Dr. Steven Drucker, Partner Research Manager and manager of the Visualization and Interactive Data Analysis (VIDA) Group, learn how information visualization provides an interactive bridge between the raw data and the human user. You will gain an understanding of basic theory behind the visual representation of datasets and explore the ways that psychology impacts our interpretations of visually represented data. Dr. Drucker…

    February 19th, 2020

  3. AI for AI: Metareasoning for modular computing systems

    A new document in a word processor can be a magical thing, a blank page onto which thoughts and ideas are put forth as quickly as we can input text. We can select words and phrases to underline and highlight and add images, shapes, and bulleted lists, and when we need editorial help, we can run a grammar and spell checker. The experience can feel so seamless at times that perhaps we don’t give much…

    February 18th, 2020

  4. Turing-NLG: A 17-billion-parameter language model by Microsoft

    This figure was adapted from a similar image published in DistilBERT. Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. We present a demo of the model, including its freeform generation, question answering, and summarization capabilities, to academics for feedback and research purposes. <|endoftext|>  - This summary was generated by the Turing-NLG language model itself. Massive deep learning…

    February 13th, 2020

  5. ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters

    The latest trend in AI is that larger natural language models provide better accuracy; however, larger models are difficult to train because of cost, time, and ease of code integration. Microsoft is releasing an open-source library called DeepSpeed, which vastly advances large model training by improving scale, speed, cost, and usability, unlocking the ability to train 100-billion-parameter models. DeepSpeed is compatible with PyTorch. One piece of that library, called ZeRO, is a new parallelized optimizer…

    February 13th, 2020

  6. Microsoft Scheduler and dawn of Intelligent PDAs with Dr. Pamela Bhattacharya

    Episode 106 | February 12, 2020 - In a world where productivity is paramount and only a handful of people have personal assistants, many of us are frustrated by the amount of time we spend in meetings, and worse, the amount time we spend planning, scheduling and rescheduling those meetings! Fortunately, Dr. Pamela Bhattacharya, a Principal Applied Scientist in Microsoft’s Outlook group, wants to turn your email into your own personal assistant. And a smart…

    February 12th, 2020

  7. Democratizing data, thinking backwards and setting North Star goals with Dr. Donald Kossmann

    Episode 107 | February 19, 2020 - Dr. Donald Kossmann is a Distinguished Scientist who thinks big, and as the Director of Microsoft Research’s flagship lab in Redmond, it’s his job to inspire others to think big, too. But don’t be fooled. For him, thinking big involves what he calls thinking backwards, a framework of imagining the future, defining progress in reverse order and executing against landmarks along an uncertain path. On the podcast, Dr.…

    February 11th, 2020

  8. Responsible AI with Dr. Saleema Amershi

    Episode 105 | February 5, 2020 - There’s an old adage that says if you fail to plan, you plan to fail. But when it comes to AI, Dr. Saleema Amershi, a principal researcher in the Adaptive Systems and Interaction group at Microsoft Research, contends that if you plan to fail, you’re actually more likely to succeed! She’s an advocate of calling failure what it is, getting ahead of it in the AI development cycle…

    February 5th, 2020

  9. The personal web: Connecting information for better search and recommendation

    In the digital era, almost everyone struggles with the mountains of information they have. Every activity in our lives seemingly generates more information: emails, files, receipts, photos—the list goes on. We have trails of digital information from each of our work projects, vacations, hobbies, and kids’ schools, including websites, files, and calendar appointments, not to mention contacts—the people we work, play, and live with in each of these spaces. We spend a significant amount of…

    February 4th, 2020

  10. Going deep on deep learning with Dr. Jianfeng Gao

    Episode 104 | January 29, 2020 - Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on…

    January 29th, 2020

  11. Open-source library provides explanation for machine learning through diverse counterfactuals

    Consider a person who applies for a loan with a financial company, but their application is rejected by a machine learning algorithm used to determine who receives a loan from the company. How would you explain the decision made by the algorithm to this person? One option is to provide them with a list of features that contributed to the algorithm’s decision, such as income and credit score. Many of the current explanation methods provide…

    January 28th, 2020

  12. Microsoft Investigator fellows accelerate scientific and teaching impact with Azure cloud computing

    I am pleased to announce the winners of the new Microsoft Investigator Fellowship. This fellowship is designed to empower researchers of all disciplines who plan to make an impact with research and teaching using the Microsoft Azure cloud computing platform. Each fellowship provides $100,000 USD annually for two years and various training and community events. We received over 290 proposals from an impressive group of faculty researchers. Ultimately, 15 fellowships were awarded. “The unique value…

    January 28th, 2020