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<font face="Times New Roman">I have a patent on stacked neural
networks that when trained can solve many stage of development
problems that flat ones cannot.</font><br>
<pre class="moz-signature" cols="72">My Best,
Michael Lamport Commons, Ph.D.
Assistant Clinical Professor
Department of Psychiatry
Beth Israel Deaconess Medical Center
Harvard Medical School
234 Huron Avenue
Cambridge, MA 02138-1328
Telephone (617) 497-5270
Facsimile (617) 491-5270
Cellular (617) 320–0896
<a class="moz-txt-link-abbreviated" href="mailto:Commons@tiac.net">Commons@tiac.net</a>
<a class="moz-txt-link-freetext" href="http://dareassociation.org/">http://dareassociation.org/</a>
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On 12/25/2011 8:07 AM, Juyang Weng wrote:
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Please give your views and suggestions so that we can improve the
BMI web in its page: Why-Me? <br>
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<h4 align="center">I Am a Computer Scientist</h4>
<p align="left">The prevailing approaches in Computer Science (CS)
and Artificial Intelligence (AI) fall into the domain of
symbolic processing. Not many researchers have sufficient
background in connectionist (neural network) approaches, which
already have over 30 years of history of phenomenal growth. If
CS researchers have an opportunity to learn brain-like signal
processing, they will find that their ideas of symbolic
reasoning (e.g., finite automata, Hidden Markov Models, Markov
Decision Process, and knowledge-base) are beautifully used by
the brain, but in a deeper emergent way. For example, Marvin
Minsky 1991 correctly criticized that artificial neural networks
then were “scruffy”. The same seems not true any more (Weng
2010) — the brain appears to use emergent representations that
are fundamentally different from symbolic models such as Finite
Automata, Hidden Markov Models, and Markov Decision Processes.
In addition, we should reconsider (symbolic) NP-hard or
NP-complete problems in light of new brain models. Computational
understanding of brain-mind would drastically change the
“landscape” of CS. As another example, the brain of a child
learns new concepts and a new language that the parents have not
heard about before the child birth — a capability likely will
solve a wide array of AI bottleneck problems. Computational
understanding of brain-mind could drastically change the
“landscape” of AI. </p>
<h4 align="center">Why Learning Computer Science?</h4>
<p align="left">Many researchers thought that computers are just
tools, as the tools help them to automate some tasks (e.g.,
generate plots). This narrow-minded view is no longer true.
Computer-like symbolic manipulation and recombination have
inspired many psychologists and AI researchers to question the
sufficiency of the traditional artificial neural networks (e.g.,
Minsky 1991). However, many neural network researchers do not
understand or even care about such questions, simply
disregarding them as “not my problem”. The recent establishment
(weng 2010) that the base network of symbolic AI systems (i.e.,
FA) is a special case of a brain-mind network DN indicates the
necessity and urgency for all researchers and students in EE,
Psychology, neuroscience, biology, and mathematics to learn
computer science, especially the automata theory and
computational complexity theory. To understand how the brain
biology works, one must understand at least how an automaton
operates on symbols and how symbols are related to meanings in
computers. No, traditional AI theories are not close to what the
brain does, but they are necessary for understand how the brain
network does symbolic AI.</p>
<br>
<pre class="moz-signature" cols="72">--
--
Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
3115 Engineering Building
Michigan State University
East Lansing, MI 48824 USA
Tel: 517-353-4388
Fax: 517-432-1061
Email: <a moz-do-not-send="true" class="moz-txt-link-abbreviated" href="mailto:weng@cse.msu.edu">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://www.cse.msu.edu/%7Eweng/">http://www.cse.msu.edu/~weng/</a>
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