This presentation explores the mutually reinforcing relationship between artificial intelligence (AI) and high-performance computing (HPC) through applied research, education, and workforce development initiatives centered on Anvil, an NSF-funded national advanced computing resource. We highlight a 2025 NSF Research Experiences for Undergraduates (REU) case study in which undergraduate researchers used large language models (LLMs) to improve Anvil’s user support infrastructure by automatically generating FAQs from historical support tickets, demonstrating AI for Anvil. At the same time, Anvil’s scalable, production-grade environment enabled realistic AI model training and evaluation, illustrating Anvil for AI. Beyond research, the session addresses ethical AI implementation frameworks, governance considerations, and classroom integration. We also discuss expanding K-12 and educator outreach, including AI-enhanced CyberSafe Heroes and Code Explorers summer camps, and a new K-12 teacher in-service focused on practical AI in the classroom. Together, these efforts demonstrate a sustainable model for responsible AI adoption that spans national cyberinfrastructure, undergraduate research, and early-pipeline education.
The changing funding environment necessitates changes in how we provide and charge for research computing services. In this talk, we'll go over what we are discussing in Utah, including some level of operation re-charge, subscription plans, compute as a service with several priority levels, and persistent services on VMs. We hope to initiate discussion on these topics among the attendees to share their thoughts and experiences.
Language models use word-level embeddings that are trained using text using a pre-train, fine-tune training and evaluation regime. In this presentation, we will see how the embeddings can be enriched with visual knowledge as they are pre-trained and fine-tuned on multiple linguistic tasks. Knowledge of python is needed; experience with torch and huggingface helps, but is not required.
