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Machine learning refers to the ability of a computer to process a range of data, from numbers and text to audio and visual data, and extract and analyze underlying patterns, using statistical algorithms. The goal of this research area is to understand the meaning of data in order to make useful decisions, and in some cases, act on those decisions.
Learn more about Machine Learning Research at Intel
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- Computational Nanovision - Intel researchers are using sophisticated mathematical models and computer vision techniques to measure and create visual representations of nanostructures. The project is exploring the challenge to quantitatively measure and reconstruct structures created by future silicon manufacturing technologies, and to extract information from low signal-noise ratio data.
- Diamond - The goal of the Diamond project is to enable rapid, interactive search of terabyte-scale, non-indexed collections of complex data, such as photo collections, satellite pictures and medical images.
- Distributed Detection & Inference - Researchers at Intel are exploring an alternative, more efficient approach to network security that relies on distributed detection and inference.
- Human Activity Recognition - Intel Research Seattle, in collaboration with the university of Washington, is building a system called Human Activity Recognition, which can automatically infer a wide range of everyday human activities and provide proactive assistance, if needed, to complete an activity.
- Statistical Computing - Intel’s Statistical Computing research team is addressing the problem of learning, prediction and decision making under uncertainty, using a variety of probabilistic techniques. Statistical computing provides the mathematical foundation for all machine learning research underway at Intel.
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Robotics
The emergence of new genre of machine learning tools firmly grounded in statistical methods is particularly exciting. Systems such as those under development by Daphne Koller (Stanford), Dieter Fox (University of Washington) and Sebastian Thrun (Stanford University) use uncertainty to support robotic hypothesis generation, a key stepping stone to anticipation.
Robotics Research
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