Task-oriented spoken dialog system is a prominent component in today’s virtual personal assistant (e.g. Alexa, Siri), which enables people to perform everyday tasks by interacting with devices via voice interfaces. Recent advances in deep learning enabled new research directions for end-to-end dialog modeling. Such data-driven end-to-end learning systems address many limitations of conventional dialog systems. This talk will review the research work on deep learning and reinforcement learning for neural dialog systems. We will further discuss hybrid dialog learning frameworks that combine offline training and online interactive learning with human-in-the-loop. This talk will conclude with the challenges and directions in further advancing data-drive conversational AI systems. Bing Liu is a research scientist in Facebook working on conversational AI. His research interests focus on machine learning for spoken language processing, natural language understanding, and dialog systems. He develops conversational AI system that learns from both offline annotated samples and online interactions. Bing received his Ph.D. degree from Carnegie Mellon University in 2018 where he worked on deep learning and reinforcement learning for task-oriented dialog systems. Before joining Facebook, he interned at Google Research working on end-to-end learning of neural dialog systems.