FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy

• URL: http://arxiv.org/abs/2207.00611v1
• Date: Fri, 1 Jul 2022 18:11:12 GMT
• Title: FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy
• Authors: Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K.J. Schmidt, Kyle Chard, Ben Blaiszik and Ian Foster
• Abstract summary: We showcase how to create and share FAIR data and AI models within a unified computational framework. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.
• Score: 1.9270896986812693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data are transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale system at the ALCF AI-Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

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