[Bmi] Discipline computer science and why learning computer science

[Michael Lamport Commons]

I have a patent on stacked neural networks that when trained can solve 
many stage of development problems that flat ones cannot.

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
Commons at tiac.net
http://dareassociation.org/




On 12/25/2011 8:07 AM, Juyang Weng wrote:
>
> Please give your views and suggestions so that we can improve the BMI 
> web in its page: Why-Me?
>
>
>         I Am a Computer Scientist
>
> 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.
>
>
>         Why Learning Computer Science?
>
> 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.
>
>
> -- 
> --
> 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:weng at cse.msu.edu
> URL:http://www.cse.msu.edu/~weng/
> ----------------------------------------------
>
>
>
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