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Abstract: "Effective communication is crucial in healthcare for ensuring successful clinical interactions, as it affects how patients respond, the decisions being made by both patients and clinicians, and the outcomes of treatments. Recent developments in Natural Language Processing (NLP) aim to improve and support these interactions within clinical settings. In this talk, I will discuss […]
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 tools for visualization and analysis. These methods aid real world sensing and measurement, scientific and medical imaging, and media and the arts. The past three […]
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 a remarkably sophisticated intuition for and appreciation of music, conventional tools for creative musical expression (e.g., instruments, music notation) are inaccessible to those of us […]
Registration Required through 12Twenty: Join a panel of HDSI undergraduate and graduate students discussing their internship expectations and realities. Learn about their experiences finding roles, navigating the hiring process, lessons learned, and skills developed. There will also be plenty of time for Q&A. Don't miss this opportunity to gain valuable insights from your peers!
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 network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent, which enables it to […]
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 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 […]