Data and artificial intelligence are deeply interconnected, and understanding their relationship could provide new and interesting approaches to AI techniques. Through the development of FAIR (Findable, Accessible, Interoperable, and Reusable) frameworks, and with the help of high-energy physics as a science driver, the FAIR4HEP project hopes to create an atmosphere for innovation within AI.
The science behind the study of HEP is complex, but the basic gist is that this branch of physics deals with the collisions of subatomic particles accelerated to such a speed they create new elementary particles when they collide. HEP studies the tiniest known particles to solve some of the biggest questions scientists have about our universe. A job of such magnitude demands AI and high-performance computing resources.
Aligning with the Department of Energy’s initiative to advance FAIR data principles – especially in terms of making AI data and models more accessible and reusable – FAIR4HEP is a deeply collaborative project between the University of Illinois at Urbana-Champaign, Massachusetts Institute of Technology, University of California at San Diego and the University of Minnesota.
The NCSA takes great pride in its collaboration with FAIR4HEP, including developing FAIR, creating and sharing data and models, and providing the Delta supercomputer to FAIR4HEP researchers.
AI is one of the most useful tools in modern computing, and it’s clear this technology has enormous relevance within HEP. The FAIR4HEP researchers are working hard to advance innovation within AI and provide novel approaches to this field of study.
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