Explore deep learning's data efficiency challenges and potential solutions in this comprehensive lecture by Carnegie Mellon University's Zachary Lipton. Delve into innovative approaches for enhancing labor efficiency in human-interactive systems, including dialogue policy learning, deep active learning for NLP, and strategies for handling noisy and limited labeled data. Examine the concept of active learning with partial feedback and discover a novel method for reducing NLP models' reliance on spurious data associations. Gain insights into the intersection of machine learning, social impact, and applications in clinical medicine and natural language processing from this expert in the field.