UW Data Science Seminar: Emma Strubell

When

02/09/2023    
4:30 pm – 5:30 pm

Please join us for a UW Data Science Seminar event on Thursday, February 9th 4:30 to 5:20 p.m. PST. The seminar will feature Emma Strubell from the Language Technologies Institute at Carnegie Mellon University.

Use this zoom link to join

 

“Environmental implications of large language models: What, why, how?”

Abstract: Large, pre-trained language models (LMs) produce high quality, general purpose representations of word (pieces) in context, shifting the paradigm for representation learning in NLP. Unfortunately, training and deploying these models comes at a high computational cost, limiting their development and use to the relatively small set of individuals and organizations with access to substantial computational resources. In this talk I’ll discuss: Why this is a problem, what we can do to make large pre-trained language models more accessible, and how we can leverage our expertise to help mitigate climate change beyond the operational emissions due to large LM training.

Bio: Emma Strubell is an Assistant Professor at the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University, and a Visiting Scientist at the Allen Institute for Artificial Intelligence. Previously she held research scientist roles at Google and FAIR after earning her doctoral degree in 2019 from the University of Massachusetts Amherst. Her research lies at the intersection of natural language processing and machine learning, with a focus on green (computationally efficient) AI and providing pragmatic solutions to practitioners who wish to gain insights from natural language text. Her work has been recognized with a Madrona AI Impact Award, best paper awards at ACL 2015 and EMNLP 2018, and cited in news outlets including the New York Times and Wall Street Journal.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

The 2022-2023 seminars will be virtual, and are free and open to the public.