Search results for: “ann house”
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SRI relaunches the PARC Forum
The PARC Forum brings together some of the world’s leading thinkers for thought-provoking conversations at the intersection of technology, business, and society.
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Tackling Greenhouse Gas Emissions with Novel Printed Gas Sensors
A low-cost system for detecting methane leaks at natural gas wells. Successful identification and quantification of methane leaks.
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Optical sensors
Customized proprietary sensors and detection systems. Our optical detectors improve performance and reduce size, cost, and complexity.
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Printed Gas Sensing
Sensor system is based on low-power electronics and printed transducers, and can be adapted to a wide variety of gases.
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Makesim
Design feedback for additive manufacturing
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SRI’s David Parekh in conversation with David Leonhardt, author of Ours Was the Shining Future: The Story of the American Dream
Join us as we relaunch the PARC Forum, a nearly 50-year-old Silicon Valley institution that brings together some of the world’s leading thinkers for thought-provoking conversations at the intersection of technology and society.
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Julie Bert
Director, Hardware Research and Technology Lab, Future Concepts Division
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SRI squeezes computer processors between pixels of an image sensor
The microscale computers will speed the processing of volumes of image data generated by autonomous vehicles, robots, and other devices that “see.”
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SRI is developing breakthrough quantum technologies for ultrasensitive sensing
Navigation, medical imaging, and other areas could benefit from powerful new sensors that are based on detecting changes induced upon individual atoms.
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SRI’s new paint provides a sustainable passive cooling solution
Researchers have developed self-cooling paint that reflects sunlight and radiates excess heat out into space.
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With innovative tracking technology, SRI is addressing the hazard of space debris particles
Hundreds of thousands of tiny pieces of space debris presently go untracked in Earth’s orbit, putting human use of space at risk.
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SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
SayNav is a novel planning framework, that leverages human knowledge from Large Language Models (LLMs) to dynamically generate step-by-step instructions for autonomous agents to complicated navigation tasks in unknown large-scale environments. It also enables efficient generalization of learning to navigate from simulation to real novel environments.