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DTSTAMP:20260404T200318
CREATED:20250828T201521Z
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UID:7316-1709899200-1709902800@tilos.ai
SUMMARY:AI Ethics in Research Webinar
DESCRIPTION:Please join Dr. Nisheeth Vishnoi from Yale and Dr. David Danks from UC San Diego who will discuss their Research in AI Ethics. Professor Danks develops practical frameworks and methods to incorporate ethical and policy considerations throughout the AI lifecycle\, including different ways to include them in optimization steps. Bias and fairness have been a particular focus given the multiple ways in which they can be measured\, represented\, and used. Professor Vishnoi uses optimization as a lens to study how subjective human and societal biases emerge in the objective world of artificial algorithms\, as well as how to design strategies to mitigate these biases.\nThis event is a great opportunity to learn about the constantly evolving issues of AI Ethics in research and the societal impact of AI. It will also provide a platform for students to gain insights and valuable advice that can help them in their future career pursuits. \n\nNisheeth Vishnoi is the A. Bartlett Giamatti Professor of Computer Science and a co-founder of the Computation and Society Initiative at Yale University. He studies the foundations of computation\, and his research spans several areas of theoretical computer science\, optimization\, and machine learning. He is also interested in understanding nature and society from a computational viewpoint. Here\, his current focus includes understanding the emergence of intelligence and developing methods to address ethical issues at the interface of artificial intelligence and humanity. \n\nDavid Danks is Professor of Data Science and Philosophy and affiliate faculty in Computer Science and Engineering at University of California\, San Diego. His research interests range widely across philosophy\, cognitive science\, and machine learning\, including their intersection. Danks has examined the ethical\, psychological\, and policy issues around AI and robotics across multiple sectors\, including transportation\, healthcare\, privacy\, and security. He has also done significant research in computational cognitive science and developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award\, as well as an Andrew Carnegie Fellowship. He currently serves on multiple advisory boards\, including the National AI Advisory Committee.
URL:https://tilos.ai/event/ai-ethics-in-research-webinar/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
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DTEND;TZID=America/Los_Angeles:20240320T110000
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CREATED:20250828T201417Z
LAST-MODIFIED:20250828T201417Z
UID:7315-1710928800-1710932400@tilos.ai
SUMMARY:TILOS Seminar: How Large Models of Language and Vision Help Agents to Learn to Behave
DESCRIPTION:Roy Fox\, Assistant Professor and Director of the Intelligent Dynamics Lab\, UC Irvine \nAbstract: If learning from data is valuable\, can learning from big data be very valuable? So far\, it has been so in vision and language\, for which foundation models can be trained on web-scale data to support a plethora of downstream tasks; not so much in control\, for which scalable learning remains elusive. Can information encoded in vision and language models guide reinforcement learning of control policies? In this talk\, I will discuss several ways for foundation models to help agents to learn to behave. Language models can provide better context for decision-making: we will see how they can succinctly describe the world state to focus the agent on relevant features; and how they can form generalizable skills that identify key subgoals. Vision and vision–language models can help the agent to model the world: we will see how they can block visual distractions to keep state representations task-relevant; and how they can hypothesize about abstract world models that guide exploration and planning. \n\nRoy Fox is an Assistant Professor of Computer Science at the University of California\, Irvine. His research interests include theory and applications of control learning: reinforcement learning (RL)\, control theory\, information theory\, and robotics. His current research focuses on structured and model-based RL\, language for RL and RL for language\, and optimization in deep control learning of virtual and physical agents.
URL:https://tilos.ai/event/tilos-seminar-how-large-models-of-language-and-vision-help-agents-to-learn-to-behave/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
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