Interpretable and Data-Efficient Learning for Autonomous Systems

Watch a 42-minute lecture exploring interpretable and data-efficient learning approaches for autonomous systems, delivered by Arizona State University Assistant Professor Zhe Xu. Dive into cutting-edge research at the intersection of symbolic methods and deep learning, examining how these techniques can enhance autonomous system development. Learn about innovative algorithms and methodologies that prioritize both interpretability and data efficiency in artificial intelligence applications. Originally presented as part of Arizona State University's AI curriculum, this talk provides valuable insights into combining logic programming with deep learning approaches to advance toward goals like artificial general intelligence (AGI).