The Future of Health Grant is a support program dedicated to accelerating early‑stage digital health startups developing solutions relevant to the Swiss healthcare system.
\r\nCreated by CSS and the EPFL Innovation Park, it combines equity‑free funding, expert guidance and access to healthcare stakeholders to help founders progress from prototype to real‑world validation.
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\r\nFor whom\r\n
The talk is jointly organized by the EPFL AI Center and the EPFL Machine Learning for Biomedicine Lab (MLBIO) as part of the AI Fundamentals seminar series.
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\r\nTitle
\r\nAgents That Know What They Know: Interpretability and Introspection for Lifelong Learning
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\r\nAbstract
\r\nOne of the hallmarks of intelligence is the ability to learn from experience. In artificial intelligence, we frame this as lifelong learning: autonomous agents that continue to adapt after deployment as they encounter new tasks and environments. In this talk, I will argue that successful lifelong learning agents require a form of introspection: they must be able to reason about the scope and limits of their own knowledge. This self-knowledge allows an agent to answer three fundamental questions. What do I know? When can I reuse what I know? And what do I still need to learn? I will present recent work from my group showing how ideas from interpretability and explainability can surface the knowledge needed to answer these questions. I will first show how learned representations can be used to identify when inputs are novel, enabling out-of-distribution detection. Then, I will discuss how this can be built upon to give agents a way to distinguish between situations where prior knowledge can be safely reused and situations where new learning is needed. Together, this work shows that interpretability is not only a tool for transparency, but a mechanism for building agents that recognize, reuse, and acquire knowledge over time.
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\r\nBio
\r\nJoseph Campbell is an Assistant Professor in the Department of Computer Science at Purdue University, where he leads the Collaborative AI for Machines and People Lab. His research bridges machine learning and robotics, with a focus on explainable machine learning and lifelong learning. Before joining Purdue, he was a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University, and he received his Ph.D., M.S., and B.S. degrees from Arizona State University. His research is supported by Amazon, the Toyota Research Institute, and the National Science Foundation.