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Abstract: Images are everywhere, especially images of the real world, and visual computing is important both for reconstructing useful models from these images and for providing us humans with interactive […]
Abstract: In this talk, I will present my work on developing and responsibly deploying generative AI systems that unlock and augment human creative potential in music. While we all possess […]
Transformers learn in-context by (functional) gradient descent Xiang Cheng, TILOS Postdoctoral Scholar at MIT HDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 Abstract: Motivated by the in-context learning phenomenon, we investigate how the Transformer neural […]
The applications of large language models (LLMs) are increasingly complex and diverse, necessitating efficient and reliable frameworks for building and deploying them. In this talk, I will begin with algorithms and systems for serving LLMs for everyone (FlexGen, S-LoRA, VTC), highlighting the growing trend of personalized LLM services. My work addresses the need to run LLMs locally for isolated individual needs. It also tackles the problem of efficiency and service fairness when resource sharing among many users is required. Once we have efficient deployment, a primary concern is the reliability of generation. The second part of this talk aims to address this issue by exploring verifiable code generation. To achieve this, I adopt tools in formal verification to facilitate LLMs in generating correctness certificates alongside other artifacts (Clover). Finally, I will touch on future research avenues, such as integrating formal methods with LLMs and developing programming systems for generative AI.
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 […]
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 […]
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 […]
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 […]
