AI for Science: Scaling in AI for Scientific Discovery

AI is integrated into scientific discovery ever more profusely to augment and accelerate research, helping scientists to 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 Science 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 AI era, successes of AI methods in different fields of science have alluded to the general effectiveness of collecting large simulated data, finding suitable architectures, enforcing invariances/equivariances, and utilizing foundation models. Our mission is to bring more scientists to attend ICML to share different perspectives on the use of AI, and to illuminate exciting research directions for AI researchers. In the following, we concentrate our discussion in this workshop on Scaling in AI for Science.Scaling models has addressed challenges once deemed insurmountable, including predicting 3D protein structures, simulating molecular behaviors, forecasting global climate shifts, discovering new physical laws, and proving theorems. As we enhance the scale of models, data sets, and application areas, there are challenges and opportunities that emerge which transcend individual scientific fields. This workshop aims to gather the AI for Science community from various disciplines to engage in meaningful dialogues about scaling AI for scientific breakthroughs. The expansion of model sizes offers a contrast to the scientific method, employed by scientists since the Renaissance, which emphasizes simplicity and reductionism. Although the primary goal of science is to unveil fundamental laws, the increased complexity of scaled models often complicates their interpretability. Nonetheless, these scaled models have shown extraordinary adaptability and efficiency in tackling complex challenges, providing significant benefits to both science and industry. As AI extends its reach to a broader range of scientific questions, our workshop will delve into the role of scalable AI in current scientific endeavors: what further contributions can we expect from AI in research? How can we effectively harness AI techniques? And how does AI influence the objectives and methods of science?To address these questions, we have invited a selection of speakers and panelists recognized for their understanding of scaling's impact on AI for Science. They will discuss how scaling introduces new dimensions and trade-offs in the development of methodologies, theoretical insights, interpretability, and discovery, sharing their expertise with the broader ML and scientific communities. These subjects will foster deep discussions on the critical impact and urgent inquiries surrounding scaling in AI for scientific exploration, drawing a diverse group of participants from the scientific, industrial, and ML research communities. Our objective is to uncover both the promising opportunities and the emerging challenges of this evolution, promoting a collaborative setting that encourages the sharing of insights and strategies across various fields. Significantly, our participants will benefit from cross-disciplinary synergies. Such synergy is vital for identifying the unique advantages and challenges of AI as a versatile tool for science advancement, sparking inspiration for its application in other untapped scientific domains.