Professor Dietterich is Distinguished Professor (Emeritus) and Director of Intelligent Systems at Oregon State University. He is widely celebrated as one of the founders of machine learning. Among his research contributions were the invention of error-correcting output coding to multi-class classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models.
Talk title: Steps Toward Robust Artificial Intelligence
Synopsis: AI technologies are being integrated into high-stakes applications such as self-driving cars, robotic surgeons, hedge funds, control of the power grid, and weapons systems. These applications need to be robust to many threats including cyberattack, user error, incorrect models, and unmodeled phenomena. This talk will survey some of the methods that the AI research community is developing to address two general kinds of threats: The “known unknowns” and the “unknown unknowns”. For the known unknowns, methods from probabilistic inference and robust optimization can provide robustness guarantees. For the unknown unknowns, the talk will discuss three approaches: detecting model failures (e.g., via anomaly detection and predictive checks), employing causal models, and constructing algorithm portfolios and ensembles. For one particular instance of model failure—the problem of open category classification where test queries may involve objects belonging to novel categories—the talk will include recent work with Alan Fern and my students on providing probabilistic guarantees.
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