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AI for Science: Scaling in AI for Scientific Discovery | AI for Science

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Dramatic developments in AI have led to its increasing adoption in science as a means to model complex phenomena, generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. The main goal of this series of workshop is to discover synergy across a variety of scientific fields, encourage interdisciplinary discussions, and enhance the flow of knowledge between AI and various scientific communities. Throughout history, bridging seemly different fields has brought overarching benefits, with notable examples: entropy in thermodynamics and information theory, neuroscience and AI, and algorithms inspired by discoveries in science (e.g. genetic algorithm, simulated annealing and diffusion-based generative models). In the current era, successes of AI methods in different fields of science have alluded to the general effectiveness of common themes: large simulated datasets, enfor

AI for Science: Scaling in AI for Scientific Discovery | AI for Science About Dramatic developments in AI have led to its increasing adoption in science as a means to model complex phenomena, generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. The main goal of this series of workshop is to discover synergy across a variety of scientific fields, encourage interdisciplinary discussions, and enhance the flow of knowledge between AI and various scientific communities. Thro

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