The Prospect of Generative Language Processing

Overview

Abstract

In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing. It traverse the outcomes of a host of transnational partnerships, highlighting impactful intersections of AI with archives and cultural preservation, education, health, and media.

Brief Biography

Muhammad Abdul-Mageed is an Associate Professor and Canada Research Chair in natural language processing and machine learning at the University of British Columbia (UBC). His research focuses on large language models, cross-modal socio-pragmatics, and deep representation learning. This program is driven by a goal to innovate more equitable, efficient, and interactive machines for improved human health, safer social networking, and reduced information overload. Dr. Abdul-Mageed has published more than 120 articles in peer reviewed venues and his group has won several international competitions. He is a founding steering member of UBC's Center for Artificial Intelligence and Language Sciences Institute, Director of the UBC Deep Learning and NLP Group, Co-Director of the I Trust AI Partnership, and Co-Lead of the Ensuring Full Literacy Partnership. Abdul-Mageed's research has been supported by Amazon, AMD, Canadian Foundation for Innovation, Google, Natural Sciences and Engineering Research Council of Canada, and Social Sciences and Humanities Research Council of Canada.