Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
It addresses the challenge of applying AI at scale for scientific discovery, which is incremental as it builds on existing AI and HPC methods.
The paper explores leveraging scalable AI on high-performance computing to tackle complex scientific problems, such as cognitive simulations and medical image analysis, by outlining necessary methodologies and providing applied 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 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.