We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.
翻译:我们概述了加强AI对科学发现作用的新机遇和挑战。AI对工业的独特目标与AI对科学的目标不同,这在识别数据模式与从数据中发现世界模式之间造成了紧张关系。 如果我们解决与“缩小”域驱动科学模型和数据驱动AI学习机器之间的“差距”相关的基本挑战,那么我们期望这些AI模型能够改变假设生成、科学发现和科学过程本身。