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This lecture covers approximate offline methods for solving POMDPs when exact solutions are computationally impossible, focusing on QMDP—a technique that solves the problem as a fully observable MDP and executes policies by weighting pre-computed Q-values with current belief states represented as alpha vectors.
This seminar argues that GenAI's effectiveness in education hinges on 'knowledge engineering'—the systematic mapping of expert cognitive processes—to ensure high-fidelity, personalized feedback. A randomized trial demonstrates that TAs using AI suggestions based on detailed reasoning rubrics produced significantly better student learning outcomes than human-only feedback.
Despite AI solving complex closed systems like chess decades ago, autonomous driving remains unsolved due to the 'open world' problem of unbounded physical complexity. This creates fundamental gaps in physical reasoning and safety validation that current foundation models struggle to overcome, requiring new comparative metrics to measure real-world reliability.
This seminar challenges the traditional linear model of 'informal-to-formal' notation development, arguing that humans dynamically create new notations through collaborative practice while current AI systems are limited to 'instant formalization' into existing structures. The speaker presents a three-stage historical model of notation evolution—from culturally situated invention through community dispersion to institutional sanctification—to guide future HCI system design.
Stanford professor James Landay argues that truly human-centered AI requires expanding beyond traditional user-centered design to address community and societal-level impacts, while tackling embedded Western cultural biases and the global rise of sovereign AI initiatives.
Former EY Global Chief Innovation Officer Jeff Wong argues that traditional industries must abandon generational planning cycles for portfolio-based AI innovation strategies, leveraging vast proprietary data assets while requiring leaders to personally adopt the tools they seek to implement.
This Stanford seminar presents advanced autonomous navigation systems using Gaussian splatting and vision language models to solve traversability and social compliance in outdoor environments, while introducing a paradigm shift toward companion robots that assist aging populations with mobility and health monitoring.
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.