Computational Safety for Generative AI

[]
Large language models (LLMs) and Generative AI (GenAI) are at the forefront of frontier AI research and technology. With their rapidly increasing popularity and availability, challenges and concerns about their misuse and safety risks are becoming more prominent than ever. In this talk, we introduce a unified computational framework for evaluating and improving a wide range of safety challenges in generative AI. Specifically, we will show new tools and insights to explore and mitigate the safety and robustness risks associated with state-of-the-art LLMs and GenAI models, including (i) safety risks in fine-tuning LLMs, (ii) LLM red-teaming and jailbreak mitigation, (iii) prompt engineering for safety debugging, and (iv) robust detection of AI-generated content.
Where: Webinar (Join link will be provided after registration)
PDHs: One Hour (Issued ONLY by prior email request)
Speaker(s): Pin-Yu Chen,
Agenda:
545 pm start
645 pm end
Virtual: https://events.vtools.ieee.org/m/559586