Difference between revisions of "Projects"
From Immersive Visualization Lab Wiki
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<td>The effectiveness of AI systems is limited by the machine’s current inability to explain their decisions and actions to human users. The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. The Explainable AI (XAI) program aims to '''create a suite of machine learning techniques that produce more explainable models''', while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. This project is a collaboration with the [https://www.sri.com/ Stanford Research Institute (SRI) Princeton, NJ]. Our responsibility is the development of the web interface, as well as in-person user studies, for which we recruited over 100 subjects so far.<br> | <td>The effectiveness of AI systems is limited by the machine’s current inability to explain their decisions and actions to human users. The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. The Explainable AI (XAI) program aims to '''create a suite of machine learning techniques that produce more explainable models''', while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. This project is a collaboration with the [https://www.sri.com/ Stanford Research Institute (SRI) Princeton, NJ]. Our responsibility is the development of the web interface, as well as in-person user studies, for which we recruited over 100 subjects so far.<br> | ||
'''Publications:''' | '''Publications:''' | ||
Revision as of 01:01, 15 December 2020
Contents |
Current Projects
Helmsley (Larry Smarr, Jurgen Schulze, 2018-2021)
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UCOP funded Catalyst project on cyber-archaeology. “UC San Diego’s Thomas E. Levy Among Recipients of President’s Research Catalyst Award.” UC San Diego News Center, December 9. http://ucs-dnews.ucsd.edu/pressrelease/uc_san_diegos_thomas_e._levy_among_recipi-ents_of_presidents_research_cataly. |
XAI (Jurgen Schulze, 2017-2021)
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The effectiveness of AI systems is limited by the machine’s current inability to explain their decisions and actions to human users. The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. The Explainable AI (XAI) program aims to create a suite of machine learning techniques that produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. This project is a collaboration with the Stanford Research Institute (SRI) Princeton, NJ. Our responsibility is the development of the web interface, as well as in-person user studies, for which we recruited over 100 subjects so far. Publications:
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DataCube (Larry Smarr, Jurgen Schulze, 2018-2021)
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In this project we developed a Bodylogical-powered augmented reality tool for the Microsoft HoloLens to analyze the health of a population such as the employees of a corporation. This video shows a demonstration of the HoloLens application. |
Catalyst (Tom Levy, Jurgen Schulze, 2017-2019)
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UCOP funded Catalyst project on cyber-archaeology. “UC San Diego’s Thomas E. Levy Among Recipients of President’s Research Catalyst Award.” UC San Diego News Center, December 9. http://ucs-dnews.ucsd.edu/pressrelease/uc_san_diegos_thomas_e._levy_among_recipi-ents_of_presidents_research_cataly. |
CalVR (Andrew Prudhomme, Philip Weber, Jurgen Schulze, since 2010)
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CalVR is our virtual reality middleware (a.k.a. VR engine), which we have been developing for our graphics clusters. It runs on anything from a laptop to a large multi-node CAVE, and builds under Linux, Windows and MacOS. More information about how to obtain the code and build it can be found on our main CalVR page. We also wrote a paper on CalVR, and gave a presentation on it. |
