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Andy Slavitt and Stanford experts discuss how AI adoption in healthcare is accelerating faster than organizational readiness, creating a critical imperative for leaders to personally engage with the technology while new CMS payment models and FDA pathways signal a policy shift toward rapid AI integration and patient empowerment.
Fine-tuning large language models risks sudden catastrophic failure of safety guardrails, which break abruptly rather than gradually like capability metrics. Researchers demonstrate that dynamically segmenting training data into safe and unsafe portions—instead of binary filtering—maintains both safety alignment and model performance.
This seminar outlines research on resilient autonomy for robots in degraded environments like underground mines and caves, emphasizing unified perception models that process pixels once for multiple geometric tasks, multi-modal sensor fusion (thermal, IMU, radar), and novel representations enabling long-range semantic reasoning without depth.
This seminar introduces a robot motion planning framework that embeds physics-based PDE priors (specifically the Eikonal equation) into neural networks, achieving real-time inference in high-dimensional spaces while eliminating the need for weeks of expert data gathering required by conventional data-driven methods.
Drawing on the Computers Are Social Actors (CASA) framework, this seminar explores how large language models function as uniquely anthropomorphic social agents, examining user privacy behaviors with AI companions and arguing for HCI interventions that address the asymmetrical risks of these corporate-owned yet socially-intimate relationships.