HAI Weekly Seminar with Percy Liang
Natural language promises to be the ultimate interface for interacting with computers, allowing users to effortlessly tap into the wealth of digital information and extract insights from it.
Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
Natural language promises to be the ultimate interface for interacting with computers, allowing users to effortlessly tap into the wealth of digital information and extract insights from it.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
How do AI agents influence knowledge work? This paper finds that agents shift worker effort from implementation to supervision, which especially benefits verifiable work and expert workers. I use data from the coding platform Cursor to study agents in software production.
.png&w=1920&q=100)
How do AI agents influence knowledge work? This paper finds that agents shift worker effort from implementation to supervision, which especially benefits verifiable work and expert workers. I use data from the coding platform Cursor to study agents in software production.
Today, virtual assistants such as Alex, Siri, and Google Assistant have given a glimpse into how this long-standing dream can become a reality, but there is still much work to be done.
In this talk, I will discuss building natural language interfaces based on semantic parsing, which converts natural language into programs that can be executed by a computer. There are multiple challenges for building semantic parsers: how to acquire data without requiring laborious annotation, how to represent the meaning of sentences, and perhaps most importantly, how to widen the domains and capabilities of a semantic parser. Finally, I will talk about a new promising paradigm for tackling these challenges based on learning interactively from users.
.png&w=3840&q=100)