Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
Machine Learning
2024-06-27 v1 Artificial Intelligence
Distributed, Parallel, and Cluster Computing
Abstract
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.
Cite
@article{arxiv.2406.17812,
title = {Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars},
author = {Wesley Brewer and Aditya Kashi and Sajal Dash and Aristeidis Tsaris and Junqi Yin and Mallikarjun Shankar and Feiyi Wang},
journal= {arXiv preprint arXiv:2406.17812},
year = {2024}
}
Comments
17 pages, 5 figures