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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>
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<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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