Data and Models: A Framework for Advancing AI in Science
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
On June 5, 2019, the Office of Science (SC) organized a one-day roundtable to focus on enhancing access to high-quality and fully traceable research data, models, and computing resources to increase the value of such resources for artificial intelligence (AI) research and development and the SC mission.1 In this report, we consider AI to be inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). We consider “data for AI” to mean the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. In part, this roundtable was motivated by the recognition that a large portion of science data currently are not well suited for AI.
- Research Organization:
- USDOE Office of Science (SC) (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI ID:
- 1579323
- Country of Publication:
- United States
- Language:
- English
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