Modeling Human Behavior with Generative Agents with Joon Park
EPISODE 632
|
JUNE
5,
2023
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About this Episode
Today we’re joined by Joon Sung Park, a PhD Student at Stanford University. Joon shares his passion for creating AI systems that can solve human problems and his work on the recent paper Generative Agents: Interactive Simulacra of Human Behavior, which showcases generative agents that exhibit believable human behavior. We discuss the use of empirical methods in studying these systems and the conflicting papers on whether AI models have a worldview and common sense. Joon talks about the importance of context and environment in creating believable agent behavior and shares his team's work on scaling emerging community behaviors. He also dives into the importance of a long-term memory module in agents and the use of knowledge graphs in retrieving associative information. The goal, Joon explains, is to create something that people can enjoy and empower people, solving existing problems and challenges in the traditional HCI and AI field.
About the Guest
Joon Park
Stanford
Connect with Joon
Resources
- Paper: Generative Agents: Interactive Simulacra of Human Behavior
- Paper: Evaluating Human-Language Model Interaction
- Paper: Working with AI to persuade: Examining a large language model’s ability to generate pro-vaccination messages
- Paper: Scaling Transformer to 1M tokens and beyond with RMT
- Paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- Paper: Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
- Paper: Are Emergent Abilities of Large Language Models a Mirage?
- AI Agents and Data Integration with GPT and LLaMa with Jerry Liu
- Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo

