We use our understanding of human learning to create artificial intelligence that learns more like people do. In turn, we use artificial intelligence and other technologies to model and assist human learning, and we apply the sorts of computational principles we work with in other areas that might benefit people. Because we have plenty of intuition on the settings in which learning thrives, we'll look to have fun, grow, and get enough sleep while we're at it.
Keywords: reinforcement learning, computer vision, curiosity, interactive learning, multi-agent learning, optimal experiment design, active learning, cognitive models, childhood learning, learning differences, autism spectrum, learning technologies, assistive tech, augmented reality, mixed reality.
We aim to create a virtuous cycle of advances in foundational AI and human cognition with an eye towards real-world applications.
MODELING HUMAN LEARNING AND DEVELOPMENTAL DIFFERENCES
We seek to create computational models of human learning and development, and in particular, models of developmental differences such as Autism Spectrum Disorder. To do this, we engineer artificial systems that learn as humans do, in complex, real-world situations, and we compare artificial behavior to human behavior.
ENGINEERING INTERACTIVE LEARNING IN ARTIFICIAL SYSTEMS
We look to develop machines that learn through autonomous exploration of and interaction with their environments -- as humans learn. To do this, we use deep reinforcement learning and employ and develop techniques in curiosity, active learning, and self-supervised learning. In doing so, we hope to create artificial systems that can learn more autonomously, flexibly, and robustly, with less demand on data.
BUILDING LEARNING TOOLS
By this, we mean both (1) tools that facilitate human learning and inquiry, and (2) tools that themselves can learn and adapt to users and tasks. We’re particularly interested in developing wearable and augmented reality tools, in the mold of the Autism Glass Project.
Interested in our work? Interested in working with us?
nhaber at stanford dot edu