Generative machine learning, specifically in the form of large
language models (LLMs), has radically changed the natural language
processing (NLP) landscape. Over the last three years, we have seen
LLMs produce amazing and, sometimes, interesting (?) results. In this talk,
we will explore the strengths, but more importantly the limitations of
LLMs. What is the role of traditional approaches NLP landscape? Are
task-specific machine learning models dated? These are some of the
questions we will explore.

Speaker
Dr. William Mattingly
Machine Learning Postdoctoral Fellow,
Smithsonian Institution’s Data Science Lab, and
HuggingFace Fellow

William Mattingly is currently the Machine Learning Postdoctoral Fellow at the Smithsonian Institution’s Data Science Lab and a HuggingFace Fellow. He earned his PhD in History in 2020 from the University of Kentucky. He has one book, Python for Digital Humanists and his articles have focused on ethical machine learning, computational approaches to medieval and biblical source material, and multilingual natural language processing. He is the lead machine learning engineer for the NEH-funded Placing the Holocaust project, Co-PI for the Harry Frank Guggenheim-funded Bitter Aloe Project, Co-PI for the twice ACLS-funded Personal Writes the Political project, and PI for Python Tutorials for Digital Humanities.