AI for Science: from Theory to Practice

Yuanqi Du · Max Welling · Yoshua Bengio · Marinka Zitnik · Carla Gomes · Jure Leskovec · Maria Brbic · Wenhao Gao · Kexin Huang · Ziming Liu · Rocío Mercado · Miles Cranmer · Shengchao Liu · Lijing Wang

Hall C2 (level 1 gate 9 south of food court)
[ Abstract ] Workshop Website
Sat 16 Dec, 6:15 a.m. PST

AI is being increasingly integrated into scientific discovery 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. It has solved scientific challenges that were unimaginable before, e.g., predicting 3D protein structures, simulating molecular systems, forecasting global climate, and discovering new scientific laws. Despite this promise, several critical gaps stifle algorithmic and scientific innovation in "AI for Science," and the overarching goal of this workshop is to grow AI for Science by closing these gaps: * Gap 1: Science of science. The principles of scientific methods have remained unchanged since the 17th century. How AI can facilitate the practice of scientific discovery itself often remains undiscussed. For example, instead of the numerous hypothesis-experiment cycles to make sense of a scientific phenomenon, can AI reason and output natural laws directly?* Gap 2: Limited exploration at the intersections of multiple disciplines. Solutions to grand challenges stretch across various disciplines. For example, protein structure prediction requires collaboration across physics, chemistry, and biology, and single-cell imaging of whole tumors can be approached by cosmology algorithms that connect cells as stars.* Gap 3: Unified ecosystems of datasets, models, and scientific hypotheses. Comprehensive ecosystems and engagements of the research community, e.g., accumulation of datasets, open-source platforms, and benchmarks, are needed to reliably evaluate AI tools and integrate them into scientific workflows and instruments so that they can contribute to scientific understanding or acquire it autonomously. The workshop will emphasize this indispensable ingredient to the success of AI for Science and engage in discussions around it.* Gap 4: Responsible use and development of AI for science. Interest in AI across scientific disciplines has grown, but very few AI models have progressed to routine use in practice. We plan to present a roadmap and guidelines for accelerating the translation of AI in science. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment).* Gap 5: Lack of educational resources. A critical element to increase the adoption of AI for scientific discovery across disciplines is to create accessible education materials and AI-lab protocols for both AI researchers and scientists with different areas of expertise, seniority, and level of interest.* Gap 6: Unrealistic methodological assumptions or directions. While AI researchers strive for methodological advances, they can make unrealistic assumptions that can limit the applicability of new algorithms, their adoption in real-world settings, and transition into implementation (e.g., at a particle accelerator, genome sequencing lab, or quantum chemistry lab). For example, while state-of-the-art molecule generation AI models perform well on benchmarks, they often generate molecules that can't be synthesized in a lab.

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