In-person and online: “Hands-On Large Language Models: Language Understanding and Generation”
Friday 25 October, noon (EDT)
On Friday 25 October 2024, noon (EDT), we host Jay Alammar, Cohere, on his new book “Hands-On Large Language Models: Language Understanding and Generation”.
All welcome via Zoom or in-person at 700 University Ave, Level 9, Room 9195.
Jay Alammar is Director and Engineering Fellow at Cohere (pioneering provider of large language models as an API). In this role, he advises and educates enterprises and the developer community on using language models for practical use cases.
ICYMI
Last week’s talk on ““Predicting the Unobserved: Integrating ‘Something Else’ Sexual Identity Responses in Health Disparity Studies Using Machine Learning and Resampling Techniques” is available:
Upcoming
Friday 1 November 2024, noon (EDT)
Xiaojun Su, Unilever
Xiaojun Su is a Machine Learning Lead, Horizon 3 Labs, Unilever where she leads cross-functional teams of data engineers, software developer, data scientists, postgraduate researchers, and 3rd party vendors to launch in-house models to drive significant ROIs. She holds a M.Sc from the University of Toronto.
Friday 8 November 2024, noon (EST)
Jacob Baldwin, Pro Football Focus (PFF)
Jacob Baldwin is a Senior Data Scientist at PFF. He holds an online M.S. degree in Applied Mathematics from the University of Washington, and graduated from Clarkson University with a B.S. in Physics, a B.S. in Applied Mathematics, and a minor in Computer Science.
Wednesday 13 November 2024, noon (EST)
Sayash Kapoor, Princeton
“CORE-Bench and other AI agent use cases”
Sayash Kapoor is a computer science Ph.D. candidate at Princeton University’s Center for Information Technology Policy. His research focuses on the societal impact of AI. He has previously worked on AI in industry and academia, including Facebook, Columbia University, and EPFL Switzerland. He received a best paper award at ACM FAccT, an impact recognition award at ACM CSCW, and was included in TIME’s inaugural list of the 100 most influential people in AI. His recently released book with Arvind Narayanan, AI Snake Oil, looks critically at what AI can and cannot do.
Friday 22 November 2024, noon (EST)
Yiqin Fu, Stanford University
Born and raised in China, Yiqin Fu spent many of her formative years in the U.S. and the U.K. Yiqin (pronounced ee-ching) is studying towards a Ph.D. in political science at Stanford University, after having worked as a research associate at Yale Law School’s Paul Tsai China Center in New Haven, Connecticut and Beijing, China. She holds a B.A. in Philosophy, Politics, and Economics from the University of Oxford and is broadly interested in innovation, U.S.-China relations, and comparative political and electoral systems.
Friday 29 November 2024, noon (EST)
Caroline Weis, gsk.ai
Caroline Weis is a Senior AI/ML Engineer and team lead at gsk.ai. In 2021, she completed her PhD in Machine Learning for Computational Biology and Healthcare at ETH Zurich. Her research interests lie in the development of personalized healthcare through data analysis and machine learning on medical and biological data.