Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems. This perspective focuses on sc...

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Published inarXiv.org
Main Authors Brewer, Wesley, Kashi, Aditya, Dash, Sajal, Tsaris, Aristeidis, Yin, Junqi, Shankar, Mallikarjun, Wang, Feiyi
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.06.2024
Subjects
Artificial intelligence
Computer Science - Artificial Intelligence
Computer Science - Distributed, Parallel, and Cluster Computing
Computer Science - Learning
Image analysis
Large language models
Medical imaging
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Summary:In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems. This perspective focuses on scientific use cases like cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches. The study outlines the methodologies needed to address such challenges at scale on supercomputers or the cloud and provides exemplars of such approaches applied to solve a variety of scientific problems.
ISSN:2331-8422
DOI:10.48550/arxiv.2406.17812