Event Details

Location: CR#689 ISI-MDR

Speaker: Hao Zhu, Stanford University

REMINDER:

Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom.

If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event. Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins.

If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location.Join Zoom Meeting
https://usc.zoom.us/j/98699643447?pwd=59bYaPQunEwvO3kiZM8jel8s2efWnu.1

Meeting ID: 986 9964 3447
Passcode: 804448

Unlike frontier AI models trained on static datasets, humans learn through dynamic interactions with other people and the world. This fundamental difference in learning methodology not only makes language agents less sample-efficient than humans but also introduces significant risks when these agents are deployed to interact with real humans in the real world. Building agents that can efficiently learn through interaction with other agents, humans and the world is a challenging problem. In this presentation, I will outline three foundational approaches we’ve developed to address this challenge:

(1) Learning through exploration on the internet (NNetNav-live) — We deploy an open-ended agent (without explicit task instructions) to explore the web, gather experience and retroactively label and train on the data.

(2) Learning from human normative decision-making (EgoNormia) — We explore methods for agents to observe and internalize social norms in physical interactions through crowd-sourced annotation with context perturbation.

(3) Learning to build metrics from human feedback (AutoLibra, in prep) — We present a framework for automatically building behavior evaluation metric systems that help both humans understand agent performance, and agents improve the policy based on human feedback.

These complementary approaches offer a path toward creating AI agents that can more effectively learn, adapt, and integrate into our open social world.”     Hao Zhu is a postdoctoral researcher in the Computer Science Department at Stanford University. He finished his PhD from CMU. He is interested in AI agents, human-agent interaction, robotics and embodied AI, and what AI agents tell us about human social and embodied cognition.

Speaker Bio

Hao Zhu is a postdoctoral researcher in the Computer Science Department at Stanford University. He finished his PhD from CMU. He is interested in AI agents, human-agent interaction, robotics and embodied AI, and what AI agents tell us about human social and embodied cognition.

If speaker approves to be recorded for this seminar, it will be posted on the USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI

Subscribe here to learn more about upcoming seminars: https://www.isi.edu/events/ 

For more information on the NL Seminar series and upcoming talks, please visit:

https://www.isi.edu/research-groups-nlg/nlg-seminars/

Hosts: Jonathan May and Katy Felkner