Download Proposal for Support of an

Proposal for Support of the Israeli Center for
Computational Intelligence in CRI
Caesarea Rothschild Institute
University of Haifa
What is Computational Intelligence?
Artificial Intelligence (AI) 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 machine learning, using tools such as neural networks and other
heuristic algorithms. In addition, Computational Intelligence investigates the
underlying computational basis to computations performed in natural settings; in
particular it tries to understand the computational theory underlying human
intelligence. Computational Intelligence is also interested in man-machine leveraged
intelligence. 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
1.
Modeling a Patient in a Virtual Reality Environment for Use in
Diagnostics
Uri Feintuch, Eugene Mednikov, Larry Manevitz and Ronen Spiegel. This is a
collaborative project with the Laboratory for Innovations in Rehabilitation
Technology (LIRT).
The idea of this project is to have the computer learn a “model” of the user based on
observing the user’s activity inside a virtual reality environment. This is done by
using certain architectures of artificial neural networks and machine learning
algorithms. Once developed, this patient-specific model can then be used as a
substitute for the patient himself, in order to perform differential diagnosis and
prognosis. Moreover, one can then experiment on the model (instead of the patient)
to find specific patient-tailored protocols for therapy and rehabilitation schemes.
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2.
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. Moreover, this capability is developed
automatically by the computer learning from examples.
This work is significant in its combining state of the art machine learning techniques
with state of the art non-invasive brain scanning techniques.
3.
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 some other 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 collaborative research (between computer scientists and psychologists) may lead
to a better understanding of how we read, the different problems in reading, and better
methodical methods to help people with reading problems.
4.
A Large-Scale Model of the Cortex
Larry Manevitz, Maya Maimon, Paul Gabbott and Carmit Tzadok
This work is dedicated to building a tool that will allow researchers to experiment
with different models of how the cortex operates. The significance of this particular
tool is that it allows one to efficiently work with millions of reasonably realistic
artificial neurons. This allows one to examine hypotheses on the structure of the
brain that require large numbers of connections and elements.
This research is related to brain structure function. In collaboration with brain
scientists and anatomists, specific theories about how the brain computes can be
tested.
The technical tools implemented include a discretized version of “integrate and fire”
neurons and tracking mechanisms based on the Theory of Information.
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5.
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
deeper.
6. Modeling the Causes of Amnesia: Detailed Structure of Memory
Assaf Gilboa, Larry Manevitz
In this work, a detailed computational model of components of the brain related to
memory storage and retrieval based on known physiological and psychological data is
being developed. If successful, this will help to explain such phenomena as
anterograde and retrograde amnesia as well as false memories and details about loss
of memory retrieval caused by, e.g. Alzheimer’s disease.
In time, such models could lead to some rehabilitation treatment of these diseases.
6. Computational Linquistics
7. Intelligent and Heuristic Pattern Recognition
Support sought for the Centre
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There are many opportunities for interested donors to support the work of the
Computational Intelligence Centre, ranging from support of a specific program
or research fellowship, to endowments for sub-activities up to endowment of the
entire center. Donors to the Centre will be appropriately acknowledged by the
center, by the Caesarea Rothschild Institute and by the University of Haifa.
The center is very interested in having on-going connection and involvement of
its donors; and those interested are invited to contact the director of CRI directly
or any of the researchers.
Some examples are given below:
Endowment of the Entire Center
The entire center currently operates based on ad hoc project based grants. There is
need for an endowment of about $3,000,000 (giving an annual return of about
$250,000) or some other equivalent arrangement.
However, specific sub divisions of the activity can be endowed separately
or specific activities directly supported.
Examples:
Full Endowment of Neuro-Computation Laboratory: $500,000
Graduate and Post-Doctoral Fellowships about $15,000 per fellowship; about
$300,000 per year is needed.
Endowment of a Yearly Research Grant: $80,000 per year per grant
(these grants would be awarded internally by the center by the director)
Endowment of a Research Visitor Grant: About $30,000 per year is needed for
short term Research visitors
Endowment of Annual Prestigious Lecture Series in Computational Intelligence
($8,000 per year)
Endowment of bi-annual international meeting on Computational Intelligence
($20,000 per meeting)
Endowment of small scale local symposiums ($5,000 per symposium x 2 per year)
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