Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

TL;DR

This paper discusses how scaling artificial intelligence on high-performance computing platforms can significantly advance scientific discovery across various fields, emphasizing methods, perspectives, and exemplars.

Contribution

It introduces a comprehensive perspective on scalable AI for science, outlining methodologies and providing exemplars for large-scale scientific applications.

Findings

Scalable AI enables complex scientific simulations.

Large language models can enhance scientific inquiry.

Supercomputers facilitate advanced medical and physics research.

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.