Event Details

REMINDER:

Meeting hosts only admit 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) beforehand so we’ll be aware of your attendance and let you in.

In-person attendance will be permitted for USC/ISI faculty, staff, students only. Open to the public virtually via the zoom link and online.

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

https://nlg.isi.edu/nl-seminar/ 

Is it possible to tame powerful AI systems even when we struggle to determine the ground truth ourselves? In this talk, I will cover two example NLP tasks: 1) automatically searching for goal-relevant patterns in large text collections and explaining them to humans in natural language; 2) labeling complex SQL programs using non-programmers with the aid of our AI system and achieving accuracy on par with database experts. In both cases, we build tools that help humans scrutinize the AI’s behavior with high effectiveness but low effort, bringing new insights that human experts have not anticipated.

Speaker Bio

Ruiqi Zhong is a 4th year Ph.D. student advised by Jacob Steinhardt and Dan Klein, working on NLP and AI Alignment. His research aims to enable humans to effectively supervise AI systems on tasks where the ground truth is hard to obtain. He reads about epistemology and labor economy in his spare time.

Mr. Zhong also works closely with Prof. Jason Eisner at Semantic Machines. Ruiqi finished his undergrad at Columbia University, where he worked with Prof. Kathleen McKeown on NLP. 

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