Title: Flexible physical problem-solving in minds and machines
Abstract: Every human culture we know of creates and re-purposes
objects as tools to achieve their goals. These capabilities come so
easily to us that we often forget how complex these behaviors are.
Despite the universality of tool use in people, only a handful of
other animals use objects in this way, and we tend to think of these
as some of the most intelligent behaviors that other species display.
In this talk, I will discuss my research program aiming to illuminate
the computational and cognitive foundations of this kind of flexible
physical problem-solving. By combining perspectives from cognitive
science, machine learning, and robotics, my research suggests that the
flexibility and efficiency of human physical problem-solving is
supported by combining learning with structured knowledge in the form
of objects, relations, and physics. These ingredients can explain both
complex cognitive phenomena such as how people effortlessly learn to
use new tools, and advanced capabilities in machines such as highly
realistic simulation and tool innovation. By taking better advantage
of problem structure, and combining it with general-purpose methods
for statistical learning, we can develop more robust and
data-efficient machine intelligence while also better explaining how
natural intelligence learns so much from so little.
Bio: Kelsey is currently a Senior Research Scientist at DeepMind. She
received her PhD from MIT in the Computational Cognitive Science
group, and her BSc from the University of British Columbia in physics.
Her work has received awards including the international Glushko prize
for best dissertation in cognitive science, a best paper award from
Robotics: Science and Systems (R:SS), and an NSERC PhD fellowship.
Spanning robotics, machine learning, and cognitive science, her work
aims to elucidate the mechanisms that give rise to adaptive and
efficient learning, especially in the domain of physical
problem-solving.
Joint Job Talk with Computer Science: Dr. Kelsey Allen
