![]() This report aims to summarize the progress in the community to understand how our scientific challenges overlap and where there are potential commonalities in data representations, ML approaches, and technology, including hardware and software platforms. Scientists and engineers from particle physicists to networking experts and biomedical engineers are represented and can interact with experts in fundamental ML techniques and compute systems architects. One of the underlying benefits of ML is the portability and general applicability of the techniques that can enable experts from seemingly unrelated domains to find a common language. The community brings together an extremely wide-ranging group of domain experts who would rarely interact as a whole. Two workshops have also been organized through this growing community and are the source for this report. It is in this spirit that the Fast Machine Learning for Science community 1 has been built. To fully unleash the power of ML and accelerate discoveries, it is necessary to embed it into our scientific process, into our instruments and detectors. The more efficiently we can test our hypotheses, the faster we can achieve discovery. This is leading to an explosion of data that must be interpreted, and ML is proving a powerful approach. ![]() Scientific discoveries come from groundbreaking ideas and the capability to validate those ideas by testing nature at new scales-finer and more precise temporal and spatial resolution. ML is also powering scientific advances which can lead to future paradigm shifts in a broad range of domains, including particle physics, plasma physics, astronomy, neuroscience, chemistry, material science, and biomedical engineering. Machine learning (ML) is making a huge impact on our society and daily lives through advancements in computer vision, natural language processing, and autonomous vehicles, among others. ![]() 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. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. 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. In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
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