Professor Sasha Rush discusses the importance of learning and inference in AI, state-space models as an alternative to Transformers, efficiency enhancements in NLP systems through techniques like sequence level knowledge distillation, and the evolution of research perspective towards empirical approaches in NLP systems.

Sasha Rush, an Associate Professor at Cornell Tech and a researcher at Hugging Face, delves into the intricacies of Natural Language Processing. He shares insights from his PhD thesis on Lagrangian Relaxation and its relevance today. The conversation touches on the balance between discrete and continuous algorithms, the evolution of coding practices, and the importance of community in open-source innovations. Additionally, they explore navigating depth and breadth in academia and the necessity of risk-taking in research for true innovation.