Microsoft Research

In 1982, Richard Feynman first proposed using a “quantum computer” to simulate physical systems with exponential speed over conventional computers.

In the future, every company will be using AI, which means that every company will need a secure infrastructure that addresses AI security concerns.

Microsoft Cognitive Toolkit (CNTK) is a production-grade, open-source, deep-learning library.

Human society is faced with an unprecedented challenge to mitigate and adapt to changing climates, ensure resilient water supplies, sustainably feed a population of 10 billion, and stem a

Machine learning (ML) has demonstrated success in various domains such as web search, ads, computer vision, natural language processing (NLP), and more.

Scientists have long explored the different sensory inputs to better understand how humans perceive the world and control their bodies.

Computer vision is arguably one of the most challenging subfields of AI.

Recent research in recurrent neural models, combined with the availability of massive amounts of dialog data, have together spurred the development of a new generation of conversational systems.

Advances in AI technologies have important ramifications for the development of accessible technologies.

Over the last decade, algorithmic developments coupled with increased computation and data resources have led to advances in well-defined verticals of AI such as vision, speech recognition, natural

Teaching machines to read, process and comprehend natural language documents and images is a coveted goal in modern AI.

Read more at Next at Microsoft: aka.ms/999990 Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the addictive 1980s video game

The Laboratory of Biomedical Database Systems at the Silesian University of Technology provides various algorithmic solutions for protein analysis.

Microsoft Research India started project MEC to understand the adoption of quality educational content in India and how technology can help uplift this adoption rate.

In this talk, I will discuss how to relax design constraints in quantum information processing in two distinct ways.

Error-correcting codes play a crucial role in safeguarding data against the adverse effects of noise during communication and storage.

Static verification of performance properties of programs is an important problem that has attracted a lot of research.

Modeling Ecosystems from Genome to Society - A symposium presenting how an international consortium that is working to build the first full-scale simulation of a complex social-ecological system.

Today, one can Lyft a ride and AirBnb home away from home all in minutes. However, when comes to planning for our children's enrichment activities, it takes us poor parents hours and days.

Unlike traditional machine learning methods, humans often learn from natural language instruction.

The design of fast, scalable, and correct concurrent systems remains a notoriously difficult task.

Social multimedia refers to the multimedia content generated by social network users for social interactions.

The DISCO Nets is a new type of probabilistic model for estimating the conditional distribution over a complex structured output given an input.

Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio.

Bayesian optimisation (BO) is an optimisation method which incrementally builds a statistical model of the objective function to refine its search.

We present an algorithm for policy search in stochastic dynamical systems using model-based reinforcement learning.

Recently, a new document metric called the word mover's distance (WMD) has been proposed with unprecedented results on kNN-based document classification.

Three years ago, James Lloyd and I presented the Automatic Statistician at the joint MSR Cambridge machine learning meeting.

We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler.

A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is