AI for the robot age

Vicarious is developing artificial general intelligence for robots. By combining insights from generative probabilistic models and systems neuroscience, our architecture trains faster, adapts more readily, and generalizes more broadly than AI approaches commonly used today.

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Research themes

The ability to generalize from a few training examples is one of the hallmarks of human intelligence. This ability is required for robots to work effectively in a variety of environments without arduous reprogramming.
Our algorithms learn models of the world that are then applied flexibly in a wide variety of situations. Our research emphasizes representations that enable task generality.
Underscoring our research strategy is the aim to discover the underlying properties of intelligence from neuroscience and cognitive science. We draw from the wealth of neuroscience literature to understand the representational structure and inductive biases that enable human brains to learn and generalize.
Teaching robots new tasks becomes easy if they have a conceptual understanding of the world just like humans. We develop algorithms that can learn abstract concepts from sensorimotor experience.

Advisors

Prof. Fei-Fei Li

Dr. Li is the Director of the Stanford AI Lab, as well as an Associate Professor at Stanford. Prior to joining Stanford, she was on faculty at Princeton University and University of Illinois Urbana-Champaign. Research by Fei-Fei and her colleagues has been published in top-tier journals and conferences such as Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, IJCV, IEEE-PAMI, and others. Fei-Fei is a recipient of the 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship and a number of Google Research awards.

Prof. Bruno Olshausen

Professor Olshausen is the Director of the Redwood Center for Theoretical Neuroscience at UC Berkeley, as well as a Professor of the Helen Wills Neuroscience Institute. He serves on the Editorial Board of Vision Research and the Journal of Computational Neuroscience and was Chair of the Gordon Research Conference on Sensory Coding and the Natural Environment in 2004. In 2002, he co-edited the book Probabilistic Models of Perception and Brain Function (MIT Press).

Prof. Alan Yuille

Professor Yuille is the Director of the UCLA Center for Cognition, Vision, and Learning, as well as a Professor at the UCLA Department of Statistics, with courtesy appointments at the Departments of Psychology, Computer Science, and Psychiatry. He is affiliated with the UCLA Staglin Center for Cognitive Neuroscience, the Center for Brains, Minds and Machines, and the NSF Expedition in Computing, Visual Cortex On Silicon.