Intelligent machines and intelligent software rely on algorithms that can reason about observed data to make predictions or decisions that are useful. Such systems rely on machine learning and artificial intelligence, combining computation, data, models, and algorithms. Our mission, in the Machine Intelligence theme at Microsoft Research Cambridge, is to expand the reach and efficiency of machine intelligence technology.

We research how to incorporate structured input data such as code and molecules effectively into deep learning models.  We invent new methods so models can accurately quantify their uncertainty when making predictions.  We build models that learn from small data that is corrupted or only partially observed.  We develop deep learning algorithms that apply to interactive settings in gaming and in decision making task, where model predictions have consequences on future inputs.

Improving the performance of machine learning methods demands an ever-increasing scale in computation while retaining flexibility to develop new models.  We research new AI compiler technology that will make it easier to express rich algorithms while effectively utilizing modern accelerators.

Projects

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Deep Program Understanding

This project aims to teach machines to understand complex algorithms, combining methods from the programming languages, software engineering and the machine learning communities.

Project Paidia - game intelligence round robot character

Deep Reinforcement Learning for Games

We aim to teach machines to understand complex algorithms, combining methods from the programming languages, software engineering and the machine learning communities.

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Enterprise Knowledge

The aim of the Enterprise Knowledge project is to automatically extract business knowledge into a single, consistent knowledge base, made up of the entities that really matter to each organisation.

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Infer.Net

Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Infer.NET is open source software under the MIT license.

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Project Causica

In this project we investigate how to best utilize AI algorithms to aid decision making while simultaneously minimizing data requirements (and, therefore, cost).

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TrueMatch

The TrueMatch matchmaking system decides which people should play together in an online multiplayer game. The Coalition have announced (opens in new tab) that Gears 5 will use TrueMatch.

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TrueSkill

The TrueSkill ranking system is a skill-based ranking system designed to overcome the limitations of existing ranking systems, and to ensure that interesting matches can be reliably arranged within a league.

People

Portrait of Dave Bignell

Dave Bignell

SR RESEARCH SCIENTIST

Portrait of Sam Devlin

Sam Devlin

Principal Researcher

Portrait of Adam Foster

Adam Foster

Portrait of Raluca Stevenson

Raluca Stevenson

Senior Research Scientist

Portrait of Wenbo Gong

Wenbo Gong

Senior Researcher

Portrait of Katja Hofmann

Katja Hofmann

Senior Principal Researcher

Portrait of Sarah Lewis

Sarah Lewis

Senior Research Engineer

Portrait of Chao Ma

Chao Ma

Portrait of Krzysztof Maziarz

Krzysztof Maziarz

Principal Researcher

Portrait of Tom Minka

Tom Minka

Senior Principal Researcher

Portrait of Pavel Myshkov

Pavel Myshkov

Senior Researcher

Portrait of Hannes Schulz

Hannes Schulz

Senior Researcher

Portrait of Marwin Segler

Marwin Segler

Principal Researcher

Portrait of Shanzheng Tan

Shanzheng Tan

Researcher / Technical Program Manager

Portrait of Jonathan Tims

Jonathan Tims

Senior Software Development Engineer

Portrait of Ryota Tomioka

Ryota Tomioka

Principal Research Manager

Portrait of Sam Webster

Sam Webster

Senior Software Development Engineer

Portrait of Tian Xie

Tian Xie

Principal Research Manager

Portrait of Yordan Zaykov

Yordan Zaykov

Principal Research Engineering Manager

Portrait of Rianne van den Berg

Rianne van den Berg

Principal Research Manager