Science (Audio)
Learning Beyond the Data: Adam Klivans on Distribution Shift and the Future of AI
19:40
Trustworthy machine learning requires models that still work when real-world data changes, and Adam Klivans, Ph.D., Director of the Institute for Foundations of Machine Learning (IFML), emphasizes learning under “distribution shift” as a major barrier to relying on models to predict disease across different patient populations. IFML focuses on foundational algorithms and mathematical techniques…