Collaborative Conversational Systems
At PARC, we believe that partnering with machines is integral to the future of how we live and work. A new era of intelligent systems will be characterized by trust and understanding between humans and machine. For this reason, our Human-Machine Collaboration team is focusing its work on explainability – developing theories and experimental systems to build fully explainable AI (XAI) systems.
Our approach to XAI involves deep reinforcement learning and novel human-machine interfaces. It’s inspired by examples of how humans form teams, collaborate and learn. PARC’s work with government and commercial partners stands out in the field of XAI because of its multidisciplinary foundations: common ground theory (from psychology and linguistics), competency-based approaches to learning and testing (from educational theory and intelligent tutoring systems), and hierarchical approaches to abstraction and segmentation (from Artificial Intelligence).
Our work is centered around a series of Focus Areas that we believe are the future of science and technology.
We’re continually developing new technologies, many of which are available for Commercialization.
PARC scientists and staffers are active members and contributors to the science and technology communities.