St. George's University Invited Talk on Artifical Intelligence and Latest AI Research

Nov 16, 2023·
Jason Lucas
Jason Lucas
· 1 min read
Image credit: Jason Lucas
Abstract
Artificial intelligence has progressed rapidly since its origins in the 1950s, with natural language processing emerging as a critical subfield focused on enabling human-language understanding in machines. In this invited talk, we will discuss a highly influential paper from the 2023 Empirical Methods in Natural Language Processing conference proceedings – “Fighting Fire with Fire." This work introduces new techniques for adversarial attacks against AI systems, revealing vulnerabilities by inducing model misclassifications. However, it also demonstrates the dual capacity to combat maliciously generated text. Discussing both aspects shows how the field is advancing while addressing crucial issues around safety and security. Overall, the 2023 conference features over 900 papers spanning innovations in Large Langauge Models, translation, and more – with a highly-selective 23% acceptance rate. This represents the remarkable progress in AI, while also highlighting important frontiers related to robustness, quality assurance, and positive real-world impact as the technology grows more advanced. We look forward to an engaging overview of the latest advancements and future directions for AI.
Location

Online Presentation

True Blue, St. George's, Caribbean WI

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Jason Lucas
Authors
Jason Lucas
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.