Josh Tenenbaum

March 23, 2012
Wu & Chen Auditorium
(101 Levine Hall)

14th Annual Lecture

Joshua Tenenbaum



Josh Tenenbaum
Department of Brain and Computer Sciences

Modeling common-sense reasoning with probabilistic

Abstract: Artificial intelligence (AI) has made great strides over its 60 year
history, building computer systems with abilities to perceive, reason,
learn and communicate that come increasingly close to human
capacities.  Yet there is still a huge gap.  Even the best current AI
systems make mistakes in reasoning that no normal human child would
ever make, because they seem to lack a basic common-sense
understanding of the world: an understanding of how physical objects
move and interact with each other, how and why people act as they do,
and how people interact with objects, their environment and other
people to achieve their goals.  I will talk about recent efforts to
capture these core aspects of human common sense in computational
models that can be compared with the judgments of both adults and
young children in precise quantitative experiments, and used for
building more human-like AI systems.

These models of intuitive physics and intuitive psychology
take the form of "probabilistic programs": probabilistic generative
models defined not over graphs, as in many current AI and machine
learning systems, but over programs whose execution traces describe
the causal processes giving rise to the behavior of physical objects
and intentional agents.  Perceiving, reasoning, predicting, and
learning in these common-sense physical and psychological domains can
then all be characterized as approximate forms of Bayesian inference
over probabilistic programs.


University of Pennsylvania