Microsoft Research

Deep neural networks and other machine learning models have been transformative for building intelligent systems capable of visual recognition, speech and language understanding.

The number one United Nations Sustainable Development Goal is to eliminate poverty, leaving nobody behind.

The ongoing explosion of spatiotemporal tracking data has now made it possible to analyze and model fine-grained behaviors in a wide range of domains.

Deep neural networks (DNNs) have led to significant improvements to the accuracy of machine-learning applications.

In July 2017, a group of Chinese students visiting from the University of Washington stopped by the Microsoft AI and Research offices to learn about Microsoft Translator’s speech translation

Predictive models are finding an increasing number of applications in many industries. As a result, a practical means for trading-off the cost of deploying a model versus its effectiveness is needed.

Thus far, capturing human body motion has only been possible with precisely-positioned sensors and cameras in well-calibrated studio environments.

The characteristic durations of speech sounds vary considerably across languages, dialects, and even speakers of the same dialect.

Text “play EK” to Zo in chat now: Kik: kik.me/zo.ai | Facebook Messenger: m.me/zo | GroupMe Contact: Zo

Overview of important methods and techniques applied in user studies from the perspective of academic research, and also from the perspective of product development.

An overview of social networks and social network analysis. See more on this video at microsoft.com/en-us/research/video/social-network-analysis

An overview of how Automatic Speech Recognition systems work and some of the challenges.

Defining pragmatics has not been easy.

An overview of some aspects of multilingualism for those that are new to the field. This talk introduces important issues and concepts to have in mind when working on your research projects.

How to do natural language processing on social media data. See more on this video at microsoft.com/en-us/research/video/nlp-social-media

Part 2 of Alan W Black's Spoken Dialogue Systems talk based on lectures he gave at Carnegie Mellon University.

Tutorial on what actual dialog systems are like and the problems we have with them, based on lectures Alan gave at Carnegie Mellon University.

Breakthroughs in Artificial Intelligence (AI) have typically shown that AI systems are good at solving specific tasks that have a well-defined goal, such as Speech Recognition, Image Captioning

Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution.

In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. These choices may be somewhat subjective and reasonably vary over some range.

In recent years, there has been an explosion of interest in and research on the use of mental health technologies that aim to treat and manage mental health problems.

The primary, long-standing goal for Augmented Reality (AR) is bringing the real and virtual together into a common space.

In this talk we will discuss our recent work on AI techniques that improve the quality of audio signals for both machine understanding and sensory perception.

Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g.

Speech emotion recognition promises to play an important role in various fields such as healthcare, security, HCI.

A long-term goal of artificial intelligence research is to develop artificial assistants (AI agents) that can collaborate with and empower their users.

Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems.

How can a machine learn from experience?

This session addresses what is human about artificial intelligence. The session will inspect this question from three very different perspectives.

In this tutorial we discuss several recent advances in deep reinforcement learning involving policy gradient methods.