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.

文章探讨了在一个后 ChatGPT 的世界中，利用可扩展的人工智能进行科学发现的潜力，并提出了将人工智能在高性能计算平台上扩展起来以解决复杂问题的重要性。该研究侧重于认知模拟、用于科学探究的大型语言模型、医学图像分析和物理注入方法等科学应用案例。该研究概述了在超级计算机或云上解决此类挑战所需的方法，并提供了应用这些方法解决各种科学问题的示例。