[Colloquium] 11/2 Distinguished Lecture Series: Josh Tenenbaum, Massachusetts Institute of Technology ​

[Mary Marre via Colloquium]

*Distinguished Lecture Series:  *

*Josh Tenenbaum, Massachusetts Institute of Technology* ​




[image: Inline image 1]
  Photo credit: Azeddine Tahiri



*Wednesday, November 2, 2016 at 11:00 am*



*Toyota Technological Institute at Chicago*

*6045 S. Kenwood Avenue*

*Room #526**​**/530*

*Josh Tenenbaum <http://web.mit.edu/cocosci/josh.html>, PhD*
Professor,
Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology


------------------------------


*Title:* Engineering and Reverse-Engineering Common Sense



*Abstract:* Many recent successes in computer vision, machine learning and
other areas of artificial intelligence have been driven by methods for
sophisticated pattern recognition, such as deep neural networks. But human
intelligence is more than just pattern recognition. In particular, it
depends on a suite of commonsense capacities for modeling the world: for
explaining and understanding what we see, imagining things we could see but
haven’t yet, solving problems and planning actions to make these things
real, and building new models as we learn more about the world. I will talk
about how we are beginning to capture these distinctively human capacities
in computational models using the tools of probabilistic programs and
program induction, embedded in a Bayesian framework for inference from
data. These models help to explain how humans can perceive rich
three-dimensional structure in visual scenes and objects, perceive and
predict objects' motion based on their intrinsic physical characteristics,
and learn new visual object concepts from just one or a few examples.


*Bio:* Josh Tenenbaum is Professor of Computational Cognitive Science in
the Department of Brain and Cognitive Sciences at MIT, a principal
investigator at MIT's Computer Science and Artificial Intelligence
Laboratory (CSAIL), and a thrust leader in the Center for Brains, Minds and
Machines (CBMM).  His research centers on perception, learning, and
common-sense reasoning in humans and machines, with the twin goals of
better understanding human intelligence in computational terms and building
more human-like intelligence in machines.  The machine learning and
artificial intelligence algorithms developed by his group are currently
used by hundreds of other science and engineering groups around the world.

Tenenbaum received his PhD from MIT in 1999, and was an Assistant Professor
at Stanford University from 1999 to 2002 before returning to MIT.  His
papers have received awards at the Cognitive Science (CogSci), Computer
Vision and Pattern Recognition (CVPR), Neural Information Processing
Systems (NIPS), and Uncertainty in Artificial Intelligence (UAI)
conferences, the International Conference on Learning and Development
(ICDL) and the International Joint Conference on Artificial Intelligence
(IJCAI).  He has given invited keynote talks at all of the major machine
learning and artificial conferences, as well as the main meetings of the
Cognitive Science Society, the Cognitive Development Society, the Society
for Mathematical Psychology, and held distinguished lectureships at
Stanford University, the University of Amsterdam, McGill University, the
University of Pennsylvania, the University of California, San Diego, and
the University of Arizona.  He is the recipient of the Early Investigator
Award from the Society of Experimental Psychologists, the Distinguished
Scientific Award for Early Career Contribution to Psychology from the
American Psychological Association, and the Troland Research Award from the
National Academy of Sciences, and is a fellow of the Society of
Experimental Psychologists and the Cognitive Science Society.



Host: Nathan Srebro, *nati at ttic.edu* <nati at ttic.edu>





Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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