Past talk · AI/ML Seminar Series
Challenges in Improving and Applying Generative Models
Research Scientist, Fundamental AI Research (FAIR) at Meta, New York
- Date & time
- Wednesday, April 10, 2024 · 11:00 AM
- Location
- Donald Bren Hall 4011
Abstract
The emergence of powerful, ever more universal models such as ChatGPT, and stable Diffusion, made generative modeling (GM) undoubtedly a focal point for modern AI research. In the talk, we will discuss applications of GM and how GM fits into a vision of autonomous machine intelligence. We will critically examine the sustainability of scaling AI models, a prevalent approach driving remarkable advancements in GM. Despite significant successes, I highlight the substantial physical, economic, and environmental limitations of continuous scaling, questioning its long-term feasibility. Furthermore, we will discuss inherent limitations in current high performance models that lead to a lack of tractability of statistical queries necessary to enable reasoning.
About the speaker
Karen Ullrich is a Research Scientist at Fundamental AI Research (FAIR) at Meta in New York. Her research agenda is centered on developing principled strategies based on information theory to mitigate the limitations of generative models. Through her doctoral and postdoctoral work, she has introduced model-agnostic methods for reducing model complexity and computational demands, significantly advancing the field of data compression and efficiency in AI models.