אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Generative models, such as ChatGPT and DALL-E, are used by millions of people daily for tasks ranging from programming and content creation to resume filtering. These models often create the impression of being “intelligent,” which can incentivize careless use in critical applications. While generative models are empowering, they appear to be black boxes, and their misuse can result in harmful or unlawful outcomes.

In this talk, I will present algorithms and tools for dissecting and analyzing generative models using holistic, causal, and data-centric approaches.
By applying these methods to state-of-the-art models, we can foster trust in these technologies by uncovering human-interpretable concepts that underpin their behavior, scrutinizing their extensive training data, and evaluating their learning processes.

Finally, I will reflect on how generative models have transformed the field of AI and discuss the challenges that remain in ensuring their responsible development and use.

Bio: Yanai Elazar is a Postdoctoral Researcher at AI2 and the University of Washington. Prior to that, he completed his PhD in Computer Science at Bar-Ilan University. He is interested in the science of generative models, for which he develops algorithms and tools for understanding what makes models work, how, and why.