Foundation Models for Science: Progress, Opportunities, and Challenges

The integration of artificial intelligence (AI) and machine learning (ML) into scientific discovery represents a pivotal shift in traditional methodologies. Historically, scientific exploration has been systematic and logical, but AI and ML promise to transform fundamental discoveries. This shift enhances interdisciplinary dialogue and stimulates innovative problem-solving, enriching the scientific community's ability to tackle complex problems. Foundation models, such as GPT-3 and CLIP, have revolutionized computer vision and natural language processing, providing versatile, pre-trained bases for various applications. Leveraging these models addresses critical challenges like long-term planning and multi-modal reasoning, essential for applications in robotics and dialogue systems. The integration of AI-for-Science and foundation models offers a transformative force in scientific domains, solving complex problems and enabling domain-specific adaptations. This synergy is poised to radically improve the modeling of complex phenomena, making it a crucial investment for future scientific advancements. This workshop aims to bring together experts to discuss and collaborate on transformative questions and challenges in advancing scientific problems through foundation models.