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.

Microsoft makes AI debugging and visualization tool TensorWatch open source

The rise of deep learning is accompanied by ever-increasing model complexity, larger datasets, and longer training times for models. When working on novel concepts, researchers often need to understand why training metrics are trending the way they are. So far, the available tools for machine learning training have focused on a “what you see is what you log” approach. As logging is relatively expensive, researchers and engineers tend to avoid it and rely on a…

June 2019

Microsoft Research Blog

A picture from a dozen words – A drawing bot for realizing everyday scenes—and even stories

If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you’d start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you’d…

June 2019

Microsoft Research Blog

HELP! Training assistive indoor agents to ask for assistance via imitation learning

Today people use personal digital assistants for help with scheduling, playing music, turning on or adjusting other devices, and answering basic questions such as “What time’s the game on?” or “Where’s the nearest hardware store?” But what if these assistants could do more to help us in our daily lives? Imagine it’s 10 p.m., and you’ve just settled in for some much-needed sleep when you’re jolted awake by a single thought: Did I leave the…

June 2019

Microsoft Research Blog

Reliability in Reinforcement Learning

Reinforcement Learning (RL), much like scaling a 3,000-foot rock face, is about learning to make sequential decisions. The list of potential RL applications is expansive, spanning robotics (drone control), dialogue systems (personal assistants, automated call centers), the game industry (non-player characters, computer AI), treatment design (pharmaceutical tests, crop management), complex systems control (for resource allocation, flow optimization), and so on. Some RL achievements are quite compelling. For example, Stanford University’s RL team learned how to…

June 2019

Microsoft Research Blog

A phonetic matching made inˈhɛvən

Recently, Microsoft Research Montréal open sourced a phonetic matching component used previously in Maluuba Inc.’s natural language understanding platform. The library contains string comparison utilities that operate on a phoneme level as opposed to a character level. This allows upstream systems to utilize personalized and contextual information that the Automatic Speech Recognition (ASR) systems may not have access to, in order to correct the ASR. This post will shed light on what phonetic matching is…

June 2019

Microsoft Research Blog

Provably efficient reinforcement learning with rich observations

Reinforcement learning, a machine learning paradigm for sequential decision making, has stormed into the limelight, receiving tremendous attention from both researchers and practitioners. When combined with deep learning, reinforcement learning (RL) has produced impressive empirical results, but the successes to date are limited to simulation scenarios in which data is cheap, primarily because modern “deep RL” algorithms are extremely data hungry. In “Provably Efficient RL with Rich Observations via Latent State Decoding”, Microsoft Research PhD…

June 2019

Microsoft Research Blog

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 2019

Microsoft Research Blog

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 2019

Microsoft Research Blog

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 2019

Microsoft Research Blog

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 2019

Microsoft Research Blog

Person looking at televisions for sale in a department store.

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 2019

Microsoft Research Blog

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 2019

Microsoft Research Blog