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10:30 am / 1:00 pm
February 6
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Sharon Levy (University of California, Santa Barbara) “Responsible AI via Responsible Large Language Models”

Hackerman Hall B17 @ 3400 N. Charles Street, Baltimore, MD 21218
12:00 pm / 1:15 pm
February 6
While large language models have advanced the state-of-the-art in natural language processing, these models are trained on large-scale datasets, which may include harmful information. Studies have shown that as a result, the models exhibit social biases and generate misinformation after training. In this talk, I will discuss my work on analyzing and interpreting the risks of large language models across the areas of fairness, trustworthiness, and safety. I will first describe my research in the detection of dialect bias between African American English (AAE)vs. Standard American English (SAE). The second part investigates the trustworthiness of models through the memorization and subsequent generation of conspiracy theories. I will end my talk with recent work in AI safety regarding text that may lead to physical harm.
Sharon is a 5th-year Ph.D. candidate at the University of California, Santa Barbara, where she is advised by Professor William Wang. Her research interests lie innatural language processing, with a focus on Responsible AI. Sharon's research spans the subareas of fairness, trustworthiness, and safety, with publications in ACL, EMNLP, WWW, and LREC. She has spent summers interning at AWS, Meta, and Pinterest. Sharon is a 2022 EECS Rising Star anda current recipient of the Amazon Alexa AI Fellowship for Responsible AI.
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CIS & MINDS Seminar – David Hogg

Clark Hall, 110
12:00 pm / 1:00 pm
February 7

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David W.Hogg, PhD        


New York University

?Bringinga classical-physics perspective to machine learning (and everything)?

 Abstract:  Physics was revolutionized in the20th century after the realization that the laws should be expressed in termsof coordinate-free, geometric objects like vectors and tensors, and that thelaws should respect diffeomorphism symmetries. All data are taken usingphysical measuring devices (such as cameras), so all data aregoverned by thesesame principles. I will show that thinking about machine-learning methods as ifthey were physics problems can have good effects across a wide range ofdata-analysis tasks and domains, even outside of the natural sciences. I'llshow some toy examples, and describe some open problems. (Work in collaborationwith Soledad Villar and others at JHU.)

Biography: David W. Hogg is Professor ofPhysics and Data Science at New York University, and the Group Leader forAstronomical Data at the Flatiron Institute of the Simons Foundation. He workson engineering, precision measurement, and discovery in astronomical data.Current projects include searching for planets around other stars, mapping thedark matter in the Milky Way, and measuring precisely the cosmologicalparameters. He helps to operate and calibrate large astronomical projectsincluding the SloanDigital Sky Surveys and the Terra Hunting Experiment.

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LCSR Seminar: Mark Savage “Resumes”

Hackerman B17
12:00 pm / 1:00 pm
February 8
Link for Live Seminar
Link for Recorded seminars ? 2022/2023 school year
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