In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

翻译：在这份社区审查报告中,我们讨论了科学领域快速机器学习的应用和技术 -- -- 将电动ML方法纳入实时实验数据处理循环以加速科学发现的概念,该报告的材料以快速ML促进科学社区举办的两个讲习班为基础,涵盖三个主要领域:在一些科学领域快速ML应用;培训和实施性能和资源效率高的ML算法的技术;以及运用这些算法的计算结构、平台和技术。我们还在可以找到共同解决办法的多个科学领域提出了重叠的挑战。本社区报告旨在为通过综合和加速ML解决方案的科学发现提供大量实例和灵感。随后,对技术进步进行了高级别的概述和组织,包括提供大量能够实现这些突破的源材料。