Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
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Research in Focus: Probablistic Programming
The traditional machine-learning approach relied on developing thousands of specific algorithms, each good for teaching machines how to do one or a few particular things. Chris Bishop, Distinguished Scientist, Microsoft Research Cambridge, is devising a…
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Research in Focus: Large-Scale Machine Learning
The emergence of Big Data has outstripped the power of computers. The computing power required to analyze grows exponentially with the data. Francis Bach, SIERRA Research Team Leader, INRIA, and Léon Bottou, Principal Researcher, Microsoft…
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Productivity Tools to Discover and Analyze Data
This project presents non-expert Excel users a set of machine-learning tools seamlessly integrated into Excel. The technology automatically can infer the values of missing cells, detect outliers, and enable users to analyze data tables more…
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Adaptive Machine Learning for Real-Time Streaming
Direct processing of real-time data can provide a crucial edge in the software-and-services industry. Combining such processing with machine learning can provide a reasoning flow and enable runtime updates of the machine-learning model. Customer scenarios…
Microsoft Research Blog
Wing Surveys Her New Opportunity
By Rob Knies, Managing Editor, Microsoft Research When Jeannette Wing joined Microsoft Research in January 2013 as a Microsoft vice president and head of Microsoft Research International, in charge of Microsoft Research’s non-U.S. labs, she…
Project
Distribution Modeller
Since its inception, the Computational Science group has undertaken research and development into new modelling platforms for computational science. The CEESDM project detailed here evolved from the Computational Science Studio project (mentioned in this article)…
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Distribution Modeller 2: FetchClimate –> chart
Preview clip of the prototype ‘Distribution Modeller’ tool under development at the Computational Ecology and Environmental Science group (CEES) at Microsoft Research Cambridge, UK.
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Distribution Modeller 1: import data –> chart
Preview clip of the prototype ‘Distribution Modeller’ tool under development at the Computational Ecology and Environmental Science group (CEES) at Microsoft Research Cambridge, UK.
Project
Automated Problem Generation for Education
Intelligent Tutoring Systems (ITS) can significantly enhance the educational experience, both in the classroom and online. A key aspect of ITS is the ability to automatically generate problems of a certain difficulty level and that…