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
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
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
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
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
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
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
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
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
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
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
“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
