New Scientist article about PNAS paper

Slashdot linked to a New Scientist article about Javier and Boom’s recent PNAS publication. Great job guys!  Here is the New Scientist link:

http://technology.newscientist.com/article/dn12879-giggling-robot-becomes-one-of-the-kids-.html

Original slashdot thread:

http://hardware.slashdot.org/article.pl?sid=07/11/06/2018230

NPR Coverage on the RUBI Project

NPR Morning Edition, November 6, 2007 Story on the RUBI Project

Friday Nov 9, 3pm: Talk on Development of Number Concepts

The Center for Human Development

Distinguished Speaker Series

Presents a talk by:

Elizabeth M. Brannon, Duke University

Friday, November 9, 2007

3:00-5:00pm

AP&M

Room 4301

Evolutionary and Developmental Precursors to a Concept of Number

Adult humans quantify, label, and categorize almost every aspect of the world with numbers. The ability to use numbers is one of the most complex cognitive abilities that humans possess and is often held up as a defining feature of the human mind. In my talk I will present a body of data that demonstrates that there are strong developmental and evolutionary precursors to adult mathematical cognition that can be seen by studying human infants and nonhuman primates. I will demonstrate the similarities in the psychophysics of numerical discrimination in adults, monkeys, and human infants, explore the relationship between the representation of number and continuous variables, and experimentally illustrate the amodal and abstract nature of nonverbal number representation. Further I will describe research using ERPs with infants, fMRI with children, and single-unit recordings with monkeys that suggests that the neural bases of numerical cognition are similar throughout development and likely to be evolutionarily ancient.

Wed Nov 7, 3 pm. Talk on Sparse Learning

TALK, Wednesday, November 7, 3 – 4:30 PM, EBU3-4140

Subspectral Algorithms for Sparse Learning, Optimization & Inference

Baback Moghaddam
Jet Propulsion Laboratory
California Institute of Technology

I will present a general class of “subspectral” algorithms (sparse
eigenvector techniques) for solving NP-hard combinatorial optimization
problems in three applied domains: (Un)Supervised Learning (e.g. PCA &
LDA), Quadratic/Entropic Optimization (e.g. Least-Squares & MaxEnt)
and 3) Bayesian Inference (e.g. Automatic Relevance Determination).
Efficient algorithms for both optimal and approximate greedy solutions
are derived using analytic eigenvalue bounds. Sample applications
presented include “sparse PCA” for variable selection (in statistics),
“sparse LDA” for classification (gene discovery), sparse kernel
regression (robotics & control), sparse quadratic programming
(portfolio optimization), graph model selection (sensor networks) and
sparse Bayesian inference for computer vision (face recognition &
OCR).

*Biography:* Baback Moghaddam joined the Jet Propulsion Laboratory in 2007
as a Principal Member, with the Machine Learning and Instrument Autonomy Group.
Prior to JPL he was a Senior Research Scientist at the Mitsubishi Electric Research
Laboratory (MERL) since 1997. He received his Ph.D. in Electrical Engineering and
Computer Science from the Massachusetts Institute of Technology in 1997, where
he was a member of the Vision & Modeling Group of the MIT Media Laboratory
since 1992. As part of his doctoral work at MIT he developed an automatic face
recognition system which won the 1996 DARPA “FERET” Face Recognition
competition. He has authored numerous articles on 2D face recognition and 3D
facial modeling in leading journals and conferences, including the core chapter in
Springer-Verlag’s Biometric Series, The Handbook of Face Recognition.