TILOS-HDSI Seminar: Adam Oberman – AI Safety Theory: The Missing Middle Ground

The next TILOS-HDSI seminar will be Wednesday, November 12 at 11am PST with Adam Oberman (McGill University). The title is AI Safety Theory: The Missing Middle Ground.

Talk Information

Speaker: Adam Oberman (McGill University)

Date & Time: Wednesday, November 12 @ 11am PST

Venue: HDSI 123

Abstract: Over the past few years, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI, there is also a risk of harm. In order to benefit from AI while mitigating the risks, we need a grounded theoretical framework.

The current AI safety theory, which predates generative AI, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”, “honest” or “dishonest”. But in practice safety is probabilistic, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms, and is essential for technical solutions as well as for making good policy decisions.

In this talk I will:

1. Review current AI risks (from misuse, from lack of reliability, and systemic risks to the economy) as well as important future risks (lack of control).
2. Review theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.
3. Explain why technical and theoretical safety solutions are valuable, even by contributors outside of the major labs.
4. Discuss some gaps in the theory and present some open problems which could address the gaps.

Bio: Adam Oberman is a Full Professor of Mathematics and Statistics at McGill University, a Canada CIFAR AI Chair, and an Associate Member of Mila. He is a research collaborator at LawZero, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models, reinforcement learning, optimization, calibration, and robustness. Earlier in his career, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin.