LIGHT: Training Agents That Can Act and Speak With Other Models and Humans In A Rich Text Adventure Game World

Thursday, March 4, 2021, 11:00 am - 12:00 pm PSTiCal
This talk will be live streamed only, it will Not Be recorded.This event is open to the public.
NL Seminar
Jason Weston (FAIR/NYU)
Video Recording:

Abstract: LIGHT is a rich fantasy text adventure game environment featuring dialogue and actions between agents in the world, which consist of both models and humans. I will summarize work on building this research platform, including crowdsourcing and machine learning to build the rich world environment, training agents to speak and act within it, and deploying the game for lifelong learning of agents by interacting with humans. See

Light - Parl
LIGHT Learning in Interactive Games with Humans and Text. The LIGHT project is a large-scale fantasy text adventure game research platform for training agents that can both talk and act, interacting either with other models or with humans.

(and the talk!) for more.

Bio: Jason Weston is a research scientist at Facebook, NY and a Visiting Research Professor at NYU. He earned his PhD in machine learning at Royal Holloway, University of London and at AT&T Research in Red Bank, NJ (advisors: Alex Gammerman, Volodya Vovk and Vladimir Vapnik) in 2000. From 2000 to 2001, he was a researcher at Biowulf technologies. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2003 to 2009 he was a research staff member at NEC Labs America, Princeton. From 2009 to 2014 he was a research scientist at Google, NY. His interests lie in statistical machine learning, with a focus on reasoning, memory, perception, interaction and communication. Jason has published over 100 papers, including best paper awards at ICML and ECML, and a Test of Time Award for his work "A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning", ICML 2008 (with Ronan Collobert). He was part of the YouTube team that won a National Academy of Television Arts & Sciences Emmy Award for Technology and Engineering for Personalized Recommendation Engines for Video Discovery. He was listed as the 16th most influential machine learning scholar at AMiner and one of the top 50 authors in Computer Science in Science

« Return to Upcoming Events