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The Triton Neurotech and TNT Academy team is excited to announce their first event in their Professor Talk series! Join them for a talk put on by Dr. Bradley Voytek on Wednesday, May 8th from 7-8pm in Henry Booker Room, Jacobs Hall 2512. Dr. Voytek is a Professor in the Department of Cognitive Science, the Halıcıoğlu […]
Abstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However, teaching AI agents to reliably interact with our physical world has proven challenging, which is in part due to a lack of large and sufficiently diverse robot datasets. In this talk, I will cover ongoing efforts of the Open X-Embodiment project–a collaboration between 279 researchers across 20+ institutions–to build a large, open dataset for real-world robotics, and discuss how this new paradigm is rapidly changing the field. Concretely, I will discuss why we need large datasets in robotics, what such datasets may look like, and how large models can be trained and evaluated effectively in a cross-embodiment cross-environment setting. Finally, I will conclude the talk by sharing my perspective on the limitations of current embodied AI agents, as well as how to move forward as a community.
Speaker: Sarah H. Cen Abstract: We have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in […]
Abstract: Although influenza is one of the best-studied viruses, vaccine effectiveness remains around 20-50%. A sizable fraction of people exhibit a weak or short-lived antibody response following vaccination, yet we cannot identify these individuals a priori nor ascertain whether a different vaccine would have served them better. In this talk, we demonstrate how machine learning can […]
When Monday July 22nd 2:00pm Where: HDSI MPR 123 Zoom Info: http://bit.ly/HDSI-Seminars Title: "Tackling Acute Respiratory Distress Syndrome (ARDS) : Integrated and Holistic Approaches" Abstract: ARDS is a complex heterogenous disorder which forms a significant health care burden. Pathophysiologically, ARDS is caused by multiple aetiologies which can lead to the diversity in clinical presentation seen. In our work with […]
When Wednesday July 24th 10:00am *Updated Where: HDSI 123 * Updated Zoom Info: https://ucsd.zoom.us/j/99334315002 *Updated Title: What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications Abstract: This talk presents a theory characterizing the types of functions neural networks learn from data. Specifically, the function space generated by deep ReLU networks consists of compositions of functions from the […]
Beyond their use in traditional data analytics problems, AI and Machine Learning techniques are changing the way real-world complex systems (like vehicles, airplanes, and even industrial production lines) are designed, tested and fabricated. When building these systems, engineers rely heavily on modeling and computer-assisted simulations. This seminar showcases how MATLAB and Simulink can help integrate […]
Talk Information: When Monday Oct 21st 1:00pm Where: HDSI MPR 123 Zoom Info: http://bit.ly/HDSI-Seminars Title: Generative Social Choice Abstract: "The mathematical study of voting, social choice theory, has traditionally only been applicable to choices among a few predetermined alternatives, but not to open-ended decisions such as collectively selecting a textual statement. This limitation is addressed by generative social choice, a design methodology […]
ABSTRACT The advent of deep learning has completely revolutionized how we perceive data to obtain superhuman performance across all fields of modern science. However, despite the remarkable empirical successes of deep learners, the theoretical guarantees for their statistical accuracy remain rather pessimistic. In particular, the data distributions on which deep learners are generally applied, such […]
Talk Information: When Wednesday Oct 30th 1:00pm Where: HDSI MPR 123 Zoom Info: http://bit.ly/HDSI-Seminars Title: Modeling and Inference of Complementarity Mechanisms in Networks. Abstract: "In many networks, including networks of protein-protein interactions, interdisciplinary collaboration networks, and semantic networks, connections are established between nodes with complementary rather than similar properties. What is complementarity? The Oxford Dictionary asserts that […]