Daniel Wu

XRL - eXplainable Reinforcement Learning in a Nutshell

Thanks to the encouraging advancement in machine learning technology, AI has become more ubiquitous in our daily lives. From task automation, decision making, cost optimization, human augmentation, medical diagnosis to autonomous driving and robotics, AI is realizing its promise to greatly enhance efficiency in modern life. Given our increasing dependency on AI systems, it is paramount to ensure AI development adheres to the principles of responsible AI. Explainability is one such foundational principle of responsible AI. In the reinforcement learning setting where intelligent agents learn by themselves with little human intervention, explainability is even more important in establishing trust and confidence with the users. This talk aims to provide an overview of XRL and a brief survey of XRL techniques.

Daniel Wu is a technical leader who brings more than 20 years of expertise in software engineering, AI/ML, and high-impact team development. He is the Head of Commercial Banking AI and Machine Learning at JPMorgan Chase where he drives financial service transformation through AI innovation. His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform.

Daniel is passionate about the democratization of technology and the ethical use of AI - a philosophy he shares in the computer science and AI/ML education programs he has contributed to over the years. He holds a computer science degree from Stanford University.

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