Proposal for Support of an
Artificial Intelligence (or Computational Intelligence?)
Centre in CRI
Caesarea Rothschild Institute
University of Haifa
What is Artificial Intelligence?
Artificial Intelligence is defined as the "study and design of intelligent agents" where
an intelligent agent is a system that perceives its environment and takes actions which
maximizes its chances of success (Russell & Norvig 2003). Today, we can see the use of
intelligent agents all around us. Every time we make a cell phone call or send an
email, AI programs are used. Robots help identify and rescue people buried under
debris, and intelligent agents help in medical diagnosis. However, artificial
intelligence is only in its infancy. We still cannot create a computer that has the
conversational abilities of a small child, or that can accurately describe a photograph
or recognize a familiar face in a crowd.
The research of AI began about 50 years ago. A more recent term related to AI is
Computational Intelligence. Computational Intelligence involves iterative
development or learning, using tools such as neural networks and other heuristic
algorithms. Naturally, other disciplines related to learning, such as Computational
Cognition, Theory of Rationality and Cognitive Psychology are also strongly related to
Computational Intelligence. The new Department of Cognition at the University of
Haifa, together with the Neuro-computational Lab at CRI can support this growing
and developing field.
Computational Intelligence projects in CRI
Modeling a Patient in a Virtual Reality Environment for Use in
Uri Feintuch, Eugene Mednikov, Larry Manevitz and Ronen Spiegel. This is a
collaborative project with the Laboratory for Innovations in Rehabilitation
Technology (LIRT).
This project utilizes artificial neural networks to automatically develop a model of a
patient using data obtained from the user’s activities in the Virtual Reality
Environment. This is technically challenging because of the noise in the data source.
Once developed, this patient-specific model can then be used for differential
diagnosis, for prognosis, as well as for testing the effectiveness of different variations
in rehabilitation schemes.
Reading the Mind: Using Machine Learning to Identify Cognitive Tasks
from Physiological (MRI) Data
David Hardoon, Larry Manevitz, Rafael Malekh and Omer Boehm
Is it possible to look at an MRI image and infer from it what the person is thinking
about? In this research, a group of subjects are shown different pictures, and by using
machine learning techniques, it is possible to predict, with very high probability,
which picture each person is looking at.
Any lie detector is capable of transforming physiological information to cognitive
information. However, this research has much higher promise, in actually, not only
telling if a person is lying or not, but perhaps, reading his thoughts. In addition to
visual tasks, the researchers also conducted experiments where they attempted to
determine which motor tasks were being performed.
Simulating a Psychological Model of Interactions Between Right and Left
Cortical Hemispheres During Reading
Hananel Hazen, Orna Peleg, Zohar Eviatar, and Larry Manevitz, CRI
Reading, especially in Hebrew, which is printed without vowels, is a very difficult
task. Many children and adults have problems in performing this daily necessary task.
This research shows that, unlike theories in the cognitive science literature, some
simple assumptions about the connections betweens types of information in the right
and left hemisphere of the brain can explain experimental results on reading
ambiguous words. For example, we show that the postulated differences between the
right and left hemispheres explain isolated visual field experiments and explain how
one hemisphere can recover from a misleading solution to the ambiguity.
This research may lead to a better understanding of how we read, the different
problems in reading, and better methodical methods to help people with reading
A Large-Scale Model of the Cortex
Larry Manevitz, Maya Maimon, Paul Gabbott and Carmit Tzadok
This research is related to human memory and its functions. We study how different
components of the brain cooperate to store memories, which are later retrieved. We
build large scalable models which can compute with millions of neurons.
This interdisciplinary research between doctors, neurophysiologists, and computer
scientists (???) may lead to a better understanding of different types of memory loss,
and hopefully, to finding a cure for some of these problems.
One approach to understanding how the human cortex computes is to explore how the
individual neurons compute and how they are put together to form emergent
computation. However, since the numbers are so large, there are theories of its
computation which can only be tested in very large models. One approach is to use in
vivo or ex vivo models but this is subject to the difficulty of control. A
complementary approach is to use computational models. However, large models
present significant computational resource difficulties. This is true for constructing
and running such models, and also for analyzing and understanding their outputs.
In this work we build large scalable models which can compute with millions of
neurons. The basic unit of these models is a discretized version of the integrate and
fire neurons. (The discretization is the key to avoiding numerical analysis problems.)
We also implement information theory based tools to understand the results. One of
the results explains why specific physiological structures in the brain lead to more
reliable computations.
Improving Human-Machine Interface in Games
Shay Bushinsky, CRI, and Bruno Lepri, ITC-IRST
Computer chess programs have reached a very high level of play: in early 2003 Deep
Junior, the current World Champion in Computer chess drew with the world
champion, Kasparov, after a memorable series of matches. However, computers are
still quite primitive when it comes to explaining the game, their and the opponent's
moves, and their artificial intelligence.
Young grandmasters, who have vast experience playing against, or with their AI
counterparts, report that computers exert overwhelming influence over them. They
trust computer analysis as if it came from a "Chess God", and tend to ignore their
"chess instincts" which would have been, sometimes, a better choice over the AI
engine proposals.
This research aims to build a chess commentary which exposes plans and tactics
within the chess engine search. The chess engine will also reveal the level of
confidence of its own evaluations. This will benefit human experts and allow them to
make a rational choice between the computer's advise and their own intuition.
This research is interdisciplinary between cognitive science, psychology, and AI.
It is hoped that this research will be applicable to all two player zero-sum and perfect
information games. Ultimately, this research promises a better insight into the future
world, where human and machine will be on the same side, cooperating deeper and
Support sought for the Centre
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