Apprentices of Wonder: Inside the Neural Network Revolution [Paperback]

William F. Allman (Author)

Book Description

Publication Date: August 1, 1990

In the vein of The Soul of a New Machine, a dramatic chronicle of a new revolution in brain-mind science comes this accessible book on the scientists who are creating startling new theories of how the mind works as the forge a new kind of artificial intelligence called neural networks--or, the first thinking machines

Product Details

• Paperback: 228 pages
• Publisher: Bantam (August 1, 1990)
• Language: English
• ISBN-10: 0553349465
• ISBN-13: 978-0553349467
• Product Dimensions: 6 x 0.5 x 9 inches
• Shipping Weight: 13.8 ounces (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #2,911,757 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

5.0 out of 5 stars A POPULARLY-WRITTEN SURVEY OF MUCH CURRENT (CIRCA 1989) BRAIN/MIND RESEARCH, September 16, 2010

By 

Steven H. Propp (Sacramento, CA USA) - See all my reviews
(TOP 500 REVIEWER)    (REAL NAME)   

This review is from: Apprentices of Wonder: Inside the Neural Network Revolution (Paperback)

William Allman is an editor with U.S. News and World Report.

He states in the Introduction to this 1989 book, "How does the brain work? This book was inspired by that seemingly simple question, and while I expected the answer to be complicated, I soon found that the question was also more compled than I had imagined... As with most areas of science, there are more people involved in this new approach than can be accomodated between the covers of a book. I've focused on a small group of researchers, chosen as much for their eloquence and character as for their scientific contributions. These researchers should be seen as a representative slice..."

Here are some representative quotations from the book:

"Researchers created computers that could often do thinking tasks such as math and logic problems faster and more accurately than our brains could do them. Once these problems were mastered, the researchers moved on to trying to find the rules and symbols that would enable computers to do the thinking tasks we do in everyday life, such as understanding speech and recognizing visual images. Despite twenty-five years of effort, however, these problems have yet to be solved." (Pg. 9)
"Neuroscientists have their hands full just trying to discover how a SINGLE neuron works; figuring out how billions of them work together is a task reserved for the distant future." (Pg. 44)
"'We lose five percent of our neurons every year,' says Hopfield. 'Yet our mental capacities don't diminish---in some cases they improve. But if you cut five percent of the wires in a conventional computer, it grinds to a halt." (Pg. 141)
"'It's pretty much hopeless to guess how the brain works based on pure thought,' (Terry Sejnowski) says. 'Nobody is going to sit down in a room with a paper and pencil and understand what is going on, for the simple reason that biology is not always elegant.'" (Pg. 178)
"The classical model has failed to explain our common sense---that effortless, fuzzy, and pervasive aspect of our minds that we use every day to get along in the world. This ability is the essential part of our brain's cognitive powers, encompassing our remarkable abilities to gain insights, understand language, and perceive the world around us." (Pg. 192)
"Most brain and mind scientists think that eventually they will unravel the many secrets of our 'engine of thought.' But they have no illusions about the difficulty of the task." (Pg. 194)

4.0 out of 5 stars An accountant who plays jazz for a hobby??, September 30, 2005

By 

Yu Zhang - See all my reviews
(REAL NAME)   

This review is from: Apprentices of Wonder: Inside the Neural Network Revolution (Paperback)

I've just finished reading the book "Apprentices of wonder - Inside the Neural Network Revolution", by William F. Allman. It was an old book, published in 1989. This book tells us stories about those pioneers in neural network research and development. The most interesting part of the book is about the fight between symbolic AI and connectionism AI. In the end, the author concluded that neither side won and the future of AI could be some form of the combination of these two. I totally agree with the author on this.

In the book, in order to show that day-to-day human reasoning is not symbolic (totally rely on logic rules), the author gave us a very interesting study by psychologists Daniel Kahneman, Paul Slovic, and Amos Tversky.

In this study, subjects were given a short description of a person and then asked to guess which professions and hobbies the person was most likely to have. Here we have:

Russ is 34 years old. He is intelligent, but unimaginative, compulsive, and generally lifeless. In school, he was strong in mathematics but weak in social studies and humanities.

Please rank in oder the following statements by their probability, using 1 for the most probable and 8 for the least probable.

<ul>
<li> Russ is a physician who plays poker for a hobby.
<li> Russ is an architect.
<li> Russ is an accountant.
<li> Russ plays jazz for a hobby.
<li> Russ surfs for a hobby.
<li> Russ is a writer.
<li> Russ is an accountant who plays jazz for a hobby.
<li> Russ climbs mountains for a hobby.
</ul>

The results of the study? Most people, quite reasonably, ranked "Russ is an accountant" as most probable. They also ranked "Russ plays jazz for a hobby" as very unlikely. However, most people also said the probability that "Russ is an accountant who plays jazz for a hobby" is higher than the probability that "Russ plays jazz for a hobby." But this violates the laws of probability! It is impossible for a statement combining two unrelated (even related - yuz) elements to be more probable than either element alone. The author claimed that even those students trained in probability and decision science made the same mistake!

The current probability theory defines that P(AB)=P(A)P(B|A)=P(B)P(A|B), since P(X) is never bigger than 1, P(AB) will never be bigger than P(A) or P(B).

This is really interesting, we are not only having born optical illusions, we also have born mental illusions! This explained a lot about my stupid decisions made in the past:-) What is truly wrong?? Our common-sense neural network or the symbolic probability theory?