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.

翻译：在后ChatGPT时代，本文探讨了利用可扩展人工智能促进科学发现的潜力。我们认为，在高性能计算平台上扩展人工智能对于解决此类复杂问题至关重要。本视角聚焦于认知模拟、用于科学探究的大语言模型、医学图像分析以及物理信息方法等科学应用场景。本研究概述了在超级计算机或云平台上大规模应对此类挑战所需的方法论，并提供了将这些方法应用于解决各类科学问题的具体范例。