Soc(AI)ety Seminars, Part 6: Can LLMs Reason and Plan?

Episode Topic: Can LLMs Reason and Plan? 

Large Language Models (LLMs) are on track to reverse what seemed like an inexorable shift of AI from explicit to tacit knowledge tasks. Trained as they are on everything ever written on the web, LLMs exhibit “approximate omniscience”–they can provide answers to all sorts of queries, but with nary a guarantee. This could herald a new era for knowledge-based AI systems–with LLMs taking the role of (blowhard?) experts. Listen in to Subbarao Kambhampati, professor of computer science at Arizona State University, who will reify this vision and attendant caveats in the context of the role of LLMs in planning tasks.

Featured Speakers:

• Subbarao Kambhampati, professor of computer science, Arizona State University

Read this episode's recap over on the University of Notre Dame's open online learning community platform, ThinkND: https://go.nd.edu/e11e2e.

This podcast is a part of the ThinkND Series titled Soc(AI)ety Seminars. 

Thanks for listening! The ThinkND Podcast is brought to you by ThinkND, the University of Notre Dame's online learning community. We connect you with videos, podcasts, articles, courses, and other resources to inspire minds and spark conversations on topics that matter to you — everything from faith and politics, to science, technology, and your career.

• Learn more about ThinkND and register for upcoming live events at think.nd.edu.
• Join our LinkedIn community for updates, episode clips, and more.

Chapters

• 0:00 Introduction to Human-AI Interaction
• 3:13 The Role of AI in Interdisciplinary Collaborations
• 9:59 Understanding Generative Machine Learning
• 14:31 Training and Functionality of LLMs
• 18:17 Challenges and Misconceptions in LLMs
• 29:38 Prompting and Fine-Tuning LLMs
• 40:32 Challenges in AI Planning
• 53:35 The Role of External Verifiers