Physics-informed Machine Learning (ML) continues to emerge as a leading paradigm integrating artificial intelligence (AI) with engineering dynamic systems. This approach provides new capabilities in real-time sensing, learning, decision-making, and predictions that are ethical, efficient, reliable, safe, and imbued with uncertainty quantification. The foundations of physics-informed ML are rooted in four key disciplines: (i) controlContinue reading "Workshop | Machine Learning for Science & Engineering"

Join this Zoom webinar to hear from senior leadership in the U.S. Department of Education about their newly released document, "Designing for Education with Artificial Intelligence: An Essential Guide for Developers."

Featuring Imme Ebert-Uphoff (CSU/CIRA), this one-hour talk focuses on Measuring Sharpness of AI-Generated Meteorological Imagery.

Envision How AI Can Tackle Critical Global Challenges in Ag There is a pressing need for agricultural innovation to meet the demands of a growing global population amidst challenging environmental conditions. Attendees at the AI for Agriculture Summit will help develop a long-term technical vision and implementation strategy for leveraging agricultural AI advancements, starting withContinue reading "AI for Agriculture Summit: A Visioning Conference"

Join us via Zoom for a talk to introduce the main issues and themes pertaining to the ethics of AI, featuring Clayton Peterson, Associate Professor of Philosophy, Université du Québec à Trois-Rivières. Analyzing what the ethics of AI are by reflecting on our understanding of both ethics and AI, the aim of this session isContinue reading "INVITEd Talk: Understanding Ethics of AI"

A Framework for Accelerating Scientific Discovery Explore the depth and diversity of research methodologies being explored in knowledge-guided machine learning (KGML) for a wide range of scientific applications, as well as the gaps in the current state of KGML research providing novel opportunities to advance AI foundations while accelerating discoveries in problems of high societal relevance.

During this workshop, awards for the MLCAS Corn Yield Prediction Using Satellite Data competition will be announced. The challenge for MLCAS 2024 focuses on the use of satellite imagery datasets from the U.S. Midwest to predict genotype-specific plot-level yield for the 2023 multi-state maize hybrid trials.  

Save the Date! The Summit for AI Institutes Leadership 2024 is set for October 7-9 in Pittsburgh, Pennsylvania, and Expo Day is scheduled for October 10. Join us for these four days of collaboration, innovation, and knowledge sharing!