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Casting Their Neural Nets

memory., George Johnson; George Johnson, an editor of The Week in Review of The New York Times and the author of ''Machinery of the Mind,'' is completing a book about . New York Times , Late Edition (East Coast); New York, N.Y. [New York, N.Y]24 Dec 1989: A.12.

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ABSTRACT (ABSTRACT)

The neural net enthusiasts are as far from their goal of making intelligent machines as are their competitors in artificial intelligence. But for some reason people who have reviled the artificial intelligentsia, as they like to call them, are suddenly embracing neural networks. The philosopher Hubert Dreyfus has made his career using obscure arguments from Heidegger and other philosophers to denounce artificial intelligence as theoretically impossible. But he was so impressed by neural nets that he rewrote parts of his 1985 book ''Mind Over Machine'' (written with his brother, Stuart Dreyfus), allowing that thinking machinery might not be so unthinkable after all.

 

It is surprising that as good a science writer as Mr. [Jeremy Campbell] would fall into this same confusion, believing that neural nets constitute ''an approach that is radically different from much of the Western philosophical tradition.'' His first book, ''Grammatical Man,'' was an exhilarating meditation on information and entropy, order and chaos - the poles of the dynamo that generates life. ''The Improbable Machine'' elegantly describes the importance of neural networks in studying the brain-mind connection. But in trying to make neural nets seem like an upheaval rather than a variation on a theme, he turns artificial intelligence into a caricature that few of its adherents would recognize.

 

IN fact, the neural nets themselves, with all their wonderful properties, are usually simulations run on digital computers. It's clear from his book that Mr. Campbell knows this, so it is baffling that he can approvingly quote the Dreyfus brothers, who seem to believe that neural networks are contributing to the downfall of the idea that the brain is a formal system. Stranger still is Mr. Campbell's contention that the representations inside both brains and artificial neural nets have a quality he calls ''aboutness,'' which the empty symbols of a digital computer supposedly cannot have. Where does this elusive quality go when a neural net is being simulated on a regular old computing machine? Artificial neural networks are formal systems. Deny that brains fall into the same category and you're in danger of becoming a holist, worshipping a ghost in the machine.

 

 

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DETAILS

 

Subject:

BOOK REVIEWS

 

People:

JOHNSON, GEORGE CAMPBELL, JEREMY

 

Company / organization:

Name: Simon &Schuster Inc; NAICS: 511130; SIC: 2731, 7372, 8732, 2741; DUNS: 00-149-5969

 

Publication title:

New York Times, Late Edition (East Coast); New York, N.Y.

 

Pages:

A.12

 

Publication year:

1989

 

Publication date:

Dec 24, 1989

 

Section:

A

 

Publisher:

New York Times Company

 

Place of publication:

New York, N.Y.

 

Country of publication:

United States

 

Publication subject:

General Interest Periodicals--United States

 

ISSN:

03624331

 

CODEN:

NYTIAO

 

Source type:

Newspapers

 

Language of publication:

English

 

Document type:

Review

 

ProQuest document ID:

427459834

 

Document URL:

https://search.proquest.com/docview/427459834?accountid=8243

 

Copyright:

Copyright New York Times Company Dec 24, 1989

 

Last updated:

2017-11-15

 

Database:

Global Newsstream

 

 

 

A neural network approach to routing and flow allocation problems in communications networks

Kamoun, Faouzi . Concordia University (Canada), ProQuest Dissertations Publishing, 1990. MM64696.

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ABSTRACT

Recently, neural networks have been proposed as new computational tools for solving constrained optimization problems. This thesis is concerned with the application of neural networks to flow allocation and routing problems in communications networks. The solutions to these problems involve Linear Programming and shortest-path computations. The existing neural networks have been improved for these applications. The flow allocation and routing problems have been formulated in a convenient way, that make them solvable by neural networks. This will enable these complicated problems to be solved in real time.

 In this thesis, the general principles involved in the design of such neural networks to solve routing and flow allocation problems are discussed. The computational power and speed of neural optimization networks are demonstrated through computer simulations. Some of the issues surrounding the applications of neural networks to these routing problems are also addressed. The key features of the neural network approach, namely a potential for high computation power and speed, high degree of robustness and fault tolerance, low power consumption and real time operation are highlighted and suggestions for further research are proposed.

 

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Subject:

Electrical engineering; Artificial intelligence

 

Classification:

0544: Electrical engineering; 0800: Artificial intelligence

 

Identifier / keyword:

Applied sciences

 

Number of pages:

134

 

Publication year:

1990

 

Degree date:

1990

 

School code:

0228

 

Source:

MAI 30/04M, Masters Abstracts International

 

Place of publication:

Ann Arbor

 

Country of publication:

United States

 

ISBN:

9780315646964, 0315646969

 

Advisor:

Ali, M. K. Mehmet

 

University/institution:

Concordia University (Canada)

 

University location:

Canada

 

Degree:

M.A.Sc.

 

Source type:

Dissertations &Theses

 

Language:

English

 

Document type:

Dissertation/Thesis

 

Dissertation/thesis number:

MM64696

 

ProQuest document ID:

303901982

 

Document URL:

https://search.proquest.com/docview/303901982?accountid=8243

 

Copyright:

Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.

 

Database:

ProQuest Dissertations &Theses Global

 

 

 

Neural network programming and portability

Bavan, Arumugam Siri . University of London, University College London (United Kingdom), ProQuest Dissertations Publishing, 1991. 10609979.

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ABSTRACT (ENGLISH)

Artificial neural networks, inspired by the neural structure of the brain, is a rapidly expanding field of research based on algorithms to solve a wide spectrum of tasks including speech recognition, image processing, planning, optimisation and other pattern processing tasks. Although a growing number of neural models have been developed to support a variety of applications, neural network programming is still mainly done using conventional languages. This thesis investigates the problems concerned with the programming of neural network models and their portability. The main goal of this thesis is to propose and develop a programming system that can facilitate the implementation of a range of neural network models on a range of hardware. This led to the design and implementation of a programming system called NPS, and a specialised neural network implementation language called NIL. NIL, which forms the neucleus of the programming system NPS, is a low level, machine independent network specification language designed to map a spectrum of neural models onto a range of architectures and thus supporting portability. The neural network programming system NPS provides the user with a system consisting of: A programming language, NIL, to specify network models. A utility, to save partially trained networks for further training. o Libraries of functions and algorithms, to aid the network construction and the execution of standard models. The neural network programming language NIL consists of two major components: A network implementation sub-language, which provides mechanisms for specifying the functions of the nodes and the interconnection topology of the network. A manipulation sub-language, which provides interactive control and modification facilities for use during the training and the recall phase of the network. These sub-languages together produce a low level, machine independent network specification language that can be used to port neural network models. Chapter 1 introduces the thesis and the background concepts, namely, neural networks, and programming systems for neural networks. In chapter 2, a survey of neural network programming systems is presented. In chapter 3, the proposed NPS programming system is presented. In chapter 4, a detailed description of the NIL language is presented. In chapter 5, implementation details of the NPS and NIL is presented. In chapter 6, an assessment of NPS and NIL is presented. Finally in chapter 7, conclusions are drawn and future work is discussed.

 

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DETAILS

 

Subject:

Artificial intelligence; Computer science

 

Classification:

0800: Artificial intelligence; 0984: Computer science

 

Identifier / keyword:

(UMI)AAI10609979 Applied sciences Artificial neural networks

 

Number of pages:

183

 

Publication year:

1991

 

Degree date:

1991

 

School code:

6022

 

Source:

DAI-C 75/03, Dissertation Abstracts International

 

Place of publication:

Ann Arbor

 

Country of publication:

United States

 

ISBN:

9781369832853

 

University/institution:

University of London, University College London (United Kingdom)

 

Department:

Computer Science

 

University location:

England

 

Degree:

Ph.D.

 

Source type:

Dissertations &Theses

 

Language:

English

 

Document type:

Dissertation/Thesis

 

Dissertation/thesis number:

10609979

 

ProQuest document ID:

1914308606

 

Document URL:

https://search.proquest.com/docview/1914308606?accountid=8243

 

Copyright:

Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.

 

Database:

ProQuest Dissertations &Theses Global

 

 

 

Implementation of feedforward artificial neural networks with learning using standard CMOS technology

Choi, Myung-Ryul . Michigan State University, ProQuest Dissertations Publishing, 1991. 9129443.

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