Microsoft Research (MSR) has for more than ten years been successfully incubating new products, innovating with new technologies and transferring technology to key Microsoft� products. Its researchers' work touches nearly every product Microsoft Corp. ships, whether by contributing new core technologies, providing new algorithms, developing and sharing code, consulting with product teams, designing new user interfaces, or creating better developer tools.

The following is a list of technologies that began in Microsoft Research and later moved into Microsoft products. This is not a comprehensive list, but it does highlight several major contributions and it demonstrates how Microsoft builds on its past technology transfer successes in successive releases.

The Hardware Systems Group of Microsoft Research, Cambridge, UK has interests in applying sensors, including MEMS devices to build prototype user interfaces including pens, for Desktop, Tablet and Pocket PC . We are also build wearable computer modules. Lyndsay Williams collaborates with Andrew Blake, Antonio Criminisi , John Krumm, and William Vablais. We are part of the Machine Learning Group managed by Chris Bishop.

Our mission in the Social Computing Group is to research and develop software that contributes to compelling and effective social interactions, with a focus on user-centered design processes and rapid prototyping.

Our work includes user interface design, sharing, mobile applications, trust and reputation, collaboration, and story telling. To facilitate the rapid prototyping, we also have an online lab for running studies to evaluate our social user interfaces.

In this project we develop hierarchical probabilistic representations for modeling activities of people. We describe how to use our representation to do sensing, learning, and inference at multiple levels of temporal granularity and abstraction. The approach centers on the use of a Layered Hidden Markov Models (LHMMs), using parameters that are learned from data. LHMMs provide a robust means for modeling diverse human activities. We illustrate the application of LHMMs within an office-awareness situation. We describe the ability to correctly classify in real-time typical office activities such as talking on the phone, being in a meeting with someone else, giving a presentation or just performing work within an office setting.

Our team combines learning science and technology research, development and thought leadership to help move post secondary education to the next generation of technology enhanced learning.

• A Vision for Life Long Learning - Year 2020 (WMV 9 Series) 256Kbps | 1.5Mbps (Right-click to save)