The Association for the Advancement of Artificial Intelligence's 2025 Spring Symposium Series was held in Burmingame, California, March 31-April 2, 2025. There were eight symposia in the spring program: AI for Engineering and Scientific Discoveries, AI for Health Symposium: Leveraging Artificial Intelligence to Revolutionize Healthcare, Current and Future Varieties of Human-AI Collaboration, GenAI@Edge: Empowering Generative AI at the Edge, Human-Compatible AI for Well-being: Harnessing Potential of GenAI for AI-Powered Science, Machine Learning and Knowledge Engineering for Trustworthy Multimodal and Generative AI, Symposium on Child-AI Interaction in the Era of Foundation Models, Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. This report contains summaries of the workshops, which were submitted by some, but not all, of the workshop chairs. This symposium aims to advance and diversify the application of AI in emerging engineering and scientific discovery domains. Inspired by progress in large language models, generative AI, and AI-assisted scientific computing, we seek to foster new collaborations between industry and academia to tackle challenging problems in materials, manufacturing, and life sciences. We also plan to explore new directions in human-machine interaction for accelerating knowledge discovery and address related ethical considerations. Through invited speakers, panel discussions, and contributions from researchers with cross-disciplinary expertise, we hoped to cultivate partnerships that drive transformative advances in both AI and scientific research. No formal report was filed by the organizers for this symposium.

Human feedback is central to the alignment of Large Language Models (LLMs).

This section provides a comprehensive overview of the CSMV dataset. This extensive time range allows for the inclusion of a diverse set of content, capturing the evolution of sentiments over the course of more than two years. The distribution of labels in our CSMV dataset is shown in Figure 1. In Figure 1a, the opinion labels are distributed as follows: positive - 47%, neutral - 42%, and negative - 11%. Negative comments are clearly in the minority.

While agents' learning is data-driven, sampling from millions

More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities.

Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation.

Advances in end-to-end rPPG approaches have focused on this transformation where attention mechanisms are crucial for feature extraction.

This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles.