Dr. Moshe Looks
Moshe Looks, Ph.D. is Software Designer and Researcher at Google
where he conducts research in program induction and artificial general
intelligence.
Moshe is a practitioner of artificial intelligence as originally
conceived:
a sister discipline of cognitive science, concerned with the
mechanization of human thought. His approach is integrative and based
on
the construction and combination of methods and inductive biases that
exploit progressively more and more of the patterned structure of
reality. Eventually, this will exceed the limited amount of structure
that humans currently exploit. Along the way, he uses these methods to
build systems that solve hard problems in computational biology,
defense, and finance.
How to guide the construction of these methods? Where do good inductive
biases come from? He
claims that the best approach, at present, is to
draw inspiration from human cognition, at the level of Marr’s
computational theory (description of the problems the system attempts
to solve). This contrasts with approaches to AI that ignore cognitive
science, as well as those that attempt to copy the algorithms and
representations of human cognition (which are not yet very well
understood — as Marvin Minsky recently put it, “neuroscience has
no
theory for the middle level”), or its implementation (neurons).
Moshe coauthored
Backbone guided local search for maximum satisfiability,
A novel local search algorithm for the traveling salesman problem
that
exploits backbones,
NL Comprehension via Integrative AI and Human-Computer
Interaction,
Toward a Pragmatic Understanding of the Cognitive Underpinnings of
Symbol Grounding,
Novamente: An Integrative Architecture for General
Intelligence,
Mixing Cognitive Science Concepts with Computer Science Algorithms
and Data
Structures: An Integrative Approach to Strong AI, and
Exploring Android Developmental Psychology in a Simulation
World.
Moshe earned a Bachelor’s Degree
(magna cum laude)
in Computer Science from The Hebrew
University of Jerusalem in 2002, a Master’s Degree in Computer Science
from Washington University in St. Louis in 2005 with the thesis
Learning Computer Programs with the Bayesian Optimization
Algorithm,
and a Doctorate in Computer Science from Washington University in
St. Louis in 2006 with the dissertation
Competent Program Evolution.
Read his LinkedIn profile.
