Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

TL;DR

It is proposed that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems as cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches.

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.

Figures (5)

• Figure 1: PINN architecture from Chen et al. chen_physics-informed_nanooptics_2020
• Figure 2: DeepONet architecture deeponet_2021.
• Figure 3: Different strategies for deploying machine-learned surrogate models on HPC yin2022strategies.
• Figure 4: Showing the orthogonal nature of scalable AI workflows.
• Figure 5: Ring all-reduce approach to distributed training gibiansky2017bringing.