Abstract

Abstract: Natural language communication has long been considered a defining characteristic of human intelligence; as such, it acts as a north star in the pursuit of artificially intelligent agents. This talk thus focuses on the research question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language and actions in service of achieving a goal. It will focus on creating reinforcement learning agents inspired by communication strategies in humans that can: (1) interactively gather and align to human preferences via feedback to shape “how” a given task is executed, and (2) learn world models that tell an agent “what” action to take in the current context and “why” to take that action given the dynamics of a grounded environment.

Bio:

Prithviraj or Prithvi or Raj is an Assistant Professor at UCSD leading the PEARLS Lab and a Research Scientist at Databricks (via MosaicML). He was previously a researcher at Ai2 and before that received his PhD at Georgia Tech. You can find out more about his work at https://prithvirajva.com