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See also:
http://www.medparse.com/gwmcv.htm .............
http://www.medparse.com/rvgodell.htm
.............
http://www.medparse.com/rvcognis.htm
.............
http://www.netautopsy.org/rvflatte.htm
United States Government Work, uncopyrighted, public-domain.
This document does not necessarily represent the views
or policies of any United States Government agency.
This document is provided "as is", without warranty of any kind,
express or implied, including but not limited to the warranties
of merchantability, fitness for a particular purpose and
noninfringement. In no event shall the authors be liable for any
claim, damages or other liability,
whether in an action of contract, tort or otherwise, arising
from, out of or in connection with the document or the use or
other dealings made with the document. Published in: Neurocomputing.
2001 Jan;42(1):.
Aleksandr I, Morton H.
An Introduction to Neural Computing. Second Edition.
London: International Thomson Computer Press. 1995.
ISBN 1-85032-167-1, 284 pages.
This book is an overview of neurocomputing
that surveys a complex and growing area of study
on a level accessible to the advanced undergraduate
in science, engineering, or mathematics.
A NEURAL NET, as readers of this journal are well-aware,
is a set of connected nodes, or NEURONS,
consisting of input-nodes, output-nodes, and
internal or 'hidden' nodes.
The goal of a neurocomputing system is to train a neural net optimally
to predict known outputs for a given set of inputs (training set);
then to apply this training information
to as-yet-unexamined inputs (test set).
The scope, complexity, and relative weighting
of the connections, and the application of these models
to practical computing problems, form much of the intellectual content
of neurocomputing.
The book begins with the historical origins of neurocomputing:
the range and limitations of the Turing-von-Neumann single-processor
computer design;
the McCulloch-Pitts model of neural nets;
and the inadequacy of traditional statistical and numerical analysis models
for managing complex data analysis.
The book follows the historical development
of neural nets through the Minsky-Papert concept of the
multilayered perceptron,
Hopfield's contributions to associative computer memories,
and the theoretical and practical aspects of parallel computing
and very large system integration.
The book is replete with worked-out mathematical examples
and general questions for discussion,
including the authors' own responses to the questions,
collected at the end of the book.
The book describes an important current debate in neurocomputing:
the so-called hard-learning question, namely, whether a
neurocomputing training algorithm should be allowed
to use only error measurements from the output layer
to adjust its internal parameters (the classical view),
or whether other information can influence calculations
on the neural net. One alternative: jostle the neural net
a little bit with artificial 'heat noise', according to a
'Boltzmann temperature model', akin to annealing in metallurgy.
Another alternative: add more nodes, more connections, and adjust
the weights among these objects, using some appropriate model.
A meta-alternative: design the neural net to recognize
general properties of the output.
such as edges or branches in a visual output,
The book clearly makes the case, as it must, against
its chief competitor among paradigms for managing large data sets
(and possibly for receiving grant funds),
namely, artificial intelligence. A neural net is a set of
nodes and connections that store experimental knowledge
obtained by training on task examples.
Artificial intelligence is a set of apriori deductive rulebases.
If an artificial intelligence system yields the wrong answer
for a particular dataset, then the human software architect
must isolate the faulty rule and replace it.
Thus artificial intelligence is forever dependent upon
human authors and troubleshooters.
It is a common experience that an artificial intelligence system
dies soon after its creator retires or moves to another institution.
The authors missed a chance to comment on a possible borderland
between neurocomputing and artificial intelligence:
a system grounded in a deductive rulebase but fine-tuned
by training on focused task examples.
Perhaps either the mathematical complexity
or the professional disaffection between the two fields is too great.
As a physician, I was disappointed by the cursory coverage of
neurocomputing in medicine. It's certainly not for lack of funded research
or publications over the past several decades. Medical advisory systems,
which are briefly described in the final chapter, have been studied
for years, and are largely a flop. It's not that computers
can't issue reasonable medical advice; but rather that physicians
don't need that kind of help from computers. A much more pressing task
is to organize and present all the relevant records for
a particular patient to the physician; the rest is less of a problem.
The book concludes with a discussion of Artificial Consciousness.
Long the domain of pulp science fiction novels and late-night television
shows, this is an area that deserves more attention in the technical
literature. Can we settle upon mathematical descriptions of such concepts
as self-awareness, representation of meaning,
language learning and translation, intention, and emotion?
I would add a few of my own:
personal privacy, acceptable public disclosure, deceit.
At the end of the book, the 'Comments on Exercises' was excellent;
often I found myself referring to it to help me understand
a particular spin or nuance intended by the author. The reference
section was adequate, but might have been somewhat larger, considering
how easy it is to obtain technical references on the internet. The index
was at best fair, which is unpardonable in a modern technical work.
I continually found myself remembering that I had read about something
earlier in the book, wanting to reread the original section,
and being disappointed by the index. Minor flaws, but all in all,
a good introduction to neurocomputing.
Last updated: 1/6/2006, by G. William Moore, MD, PhD.