Joon Park: Generative Agents and Human-Computer Interaction
The Gradient: Perspectives on AI
The Future of Machine Learning
The bar for performance went up, right? We could literally achieve much higher performance on many of the metrics and tasks. That axis was felt very fresh. And we really went in and started to think about, okay, what would that mean? What would they enable us to do?
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.
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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
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