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DTSTART;TZID=America/Los_Angeles:20251112T110000
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DTSTAMP:20260404T033135
CREATED:20251104T173955Z
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UID:7730-1762945200-1762948800@tilos.ai
SUMMARY:TILOS-HDSI Seminar: AI safety theory: the missing middle ground
DESCRIPTION:Adam Oberman\, McGill University \nAbstract:  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. \nThe 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. \nIn this talk I will: \n\nReview current AI risks (from misuse\, from lack of reliability\, and systemic risks to the economy) as well as important future risks (lack of control).\nReview theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.\nExplain why technical and theoretical safety solutions are valuable\, even by contributors outside of the major labs.\nDiscuss some gaps in the theory and present some open problems which could address the gaps.\n\n\nAdam 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.
URL:https://tilos.ai/event/tilos-hdsi-seminar-with-adam-oberman-mcgill-ai-safety-theory-the-missing-middle-ground/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/webp:https://tilos.ai/wp-content/uploads/2025/11/oberman-adam-e1762277416983.webp
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DTEND;TZID=America/Los_Angeles:20251119T120000
DTSTAMP:20260404T033135
CREATED:20251105T193505Z
LAST-MODIFIED:20260227T215217Z
UID:7735-1763550000-1763553600@tilos.ai
SUMMARY:TILOS-SDSU Seminar: Certifiably Correct Machine Perception
DESCRIPTION:David Rosen\, Northeastern University \nAbstract: Many fundamental machine perception and state estimation tasks require the solution of a high-dimensional nonconvex estimation problem; this class includes (for example) the fundamental problems of simultaneous localization and mapping (in robotics)\, 3D reconstruction (in computer vision)\, and sensor network localization (in distributed sensing). Such problems are known to be computationally hard in general\, with many local minima that can entrap the smooth local optimization methods commonly applied to solve them. The result is that standard machine perception algorithms (based upon local optimization) can be surprisingly brittle\, often returning egregiously wrong answers even when the problem to which they are applied is well-posed. \nIn this talk\, we present a novel class of certifiably correct estimation algorithms that are capable of efficiently recovering provably good (often globally optimal) solutions of generally-intractable machine perception problems in many practical settings. Our approach directly tackles the problem of nonconvexity by employing convex relaxations whose minimizers provide provably good approximate solutions to the original estimation problem under moderate measurement noise. We illustrate the design of this class of methods using the fundamental problem of pose-graph optimization (a mathematical abstraction of robotic mapping) as a running example. We conclude with a brief discussion of open questions and future research directions. \n\nDavid M. Rosen is an Assistant Professor in the Departments of Electrical & Computer Engineering and Mathematics and the Khoury College of Computer Sciences (by courtesy) at Northeastern University\, where he leads the Robust Autonomy Laboratory (NEURAL). Prior to joining Northeastern\, he was a Research Scientist at Oculus Research (now Meta Reality Labs) from 2016 to 2018\, and a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) from 2018 to 2021. He holds the degrees of B.S. in Mathematics from the California Institute of Technology (2008)\, M.A. in Mathematics from the University of Texas at Austin (2010)\, and ScD in Computer Science from the Massachusetts Institute of Technology (2016). \n\nHe is broadly interested in the mathematical and algorithmic foundations of trustworthy machine perception\, learning\, and control. His work has been recognized with the IEEE Transactions on Robotics Best Paper Award (2024)\, an Honorable Mention for the IEEE Transactions on Robotics Best Paper Award (2021)\, a Best Student Paper Award at Robotics: Science and Systems (2020)\, a Best Paper Award at the International Workshop on the Algorithmic Foundations of Robotics (2016)\, and selection as an RSS Pioneer (2019).
URL:https://tilos.ai/event/tilos-sdsu-seminar-with-david-rosen-northeastern/
LOCATION:Lamden Hall 341 (SDSU) and Virtual\, San Diego\, CA\, 92182\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
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