Understanding why neural AI models behave the way they do, particularly when they fail, has become one of the central topics in recent years. In this talk, I will demonstrate how linguistic principles provide a powerful lens to diagnose and predict model behaviors, ultimately contributing to the development of more robust and interpretable AI systems. I will exemplify this line of research in our group with two specific cases: (1) multimodal reasoning with an awareness of reference frames, and (2) logical reasoning across multiple semantically equivalent forms. By analyzing how these AI models behave, I will highlight patterns and limitations in their decision-making processes, offering insights into their shortcomings and potential improvement. I will conclude by discussing my recent thoughts of interdisciplinary collaboration in advancing both AI research and linguistics.
This event is open to the public with emphasis on graduate students in machine learning, computer science, ECE, statistics, mathematics, linguistics, medicine, as well as PhD-level data scientists in the GTA.