Joon Park: Generative Agents and Human-Computer Interaction
The Gradient: Perspectives on AI
Introduction
June Park is a computer scientist at Stanford University. His recent work on generative agents captured many people's imaginations. We discuss his perspectives on interaction and selections from his work, including studies on slow algorithms and antisocial behavior in online communities.
In episode 77 of The Gradient Podcast, Daniel Bashir speaks to Joon Park.
Joon is a third-year PhD student at Stanford, advised by Professors Michael Bernstein and Percy Liang. He designs, builds, and evaluates interactive systems that support new forms of human-computer interaction by leveraging state-of-the-art advances in natural language processing such as large language models. His research introduced the concept of, and the techniques for building generative agents—computational software agents that simulate believable human behavior. Joon’s work has been supported by the Microsoft Research PhD Fellowship, the Stanford School of Engineering Fellowship, and the Siebel Scholarship.
Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pub
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter
Outline:
* (00:00) Intro
* (01:43) Joon’s path from studio art to social computing / AI
* (05:00) Joon’s perspectives on Human-Computer Interaction (HCI) and its recent evolution
* (06:45) How foundation models enter the picture
* (10:28) On slow algorithms and technology: A Slow Algorithm Improves Users’ Assessments of the Algorithm’s Accuracy
* (12:10) Motivations
* (17:55) The jellybean-counting task, hypotheses
* (22:00) Applications and takeaways
* (28:05) Deliberate engagement in social media / computing systems, incentives
* (32:55) Daniel rants about The Social Dilemma + anti- social media rhetoric, Joon on the role of academics, framings of addiction
* (39:05) Measuring the Prevalence of Anti-Social Behavior in Online Communities
* (48:30) Statistics on anti-social behavior and anecdotal information, limitations in the paper’s measurements
* (51:45) Participatory and value-sensitive design
* (52:50) “Interaction” in On the Opportunities and Risks of Foundation Models
* (53:45) Broader insights on foundation models and emergent behavior
* (56:50) Joon’s section on interaction
* (1:01:05) Daniel’s bad segue to Social Simulacra: Creating Populated Prototypes for Social Computing Systems
* (1:02:50) Context for Social Simulacra and Generative Agents, why Social Simulacra was tackled first
* (1:24:05) The value of norms
* (1:26:20) Collaborations between designers and developers of social simulacra
* (1:30:00) Generative Agents: Interactive Simulacra of Human Behavior
* (1:30:30) Context / intro
* (1:45:10) On (too much) coherence in generative agents and believability
* (1:52:02) Instruction tuning’s impact on generative agents, model alignment w/ believability goals, desirability of agent conflict / toxic LLMs
* (1:56:55) Release strategies and toxicity in LLMs
* (2:03:05) On designing interfaces and responsible use
* (2:09:05) Capability advances and the capability-safety research gap
* (2:14:12) Worries about LLM integration, human-centered framework for technology release / LLM incorporation
* (2:18:00) Joon’s philosophy as an HCI researcher
* (2:20:39) Outro
Links:
* Research
* A Slow Algorithm Improves Users’ Assessments of the Algorithm’s Accuracy
* Measuring the Prevalence of Anti-Social Behavior in Online Communities
* On the Opportunities and Risks of Foundation Models
* Social Simulacra: Creating Populated Prototypes for Social Computing Systems
* Generative Agents: Interactive Simulacra of Human Behavior
Get full access to The Gradient at thegradientpub.substack.com/subscribe
The AI-powered Podcast Player
