Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems) [Paperback]

Christopher J. Thornton (Author)

Book Description

ISBN-10: 0262700875 | ISBN-13: 978-0262700870 | Publication Date: February 7, 2002

This study of learning in autonomous agents offers a bracing intellectual adventure. Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. Although theorists of machine learning tell us that all learning methods contribute some form of bias and thus involve a degree of creativity, Thornton carries the idea much further. He describes an incremental process, recursive relational learning, in which the results of one learning step serve as the basis for the next. Very high-level recodings are then substantially the creative artifacts of the learner's own processing. Lower-level recodings are more "objective" in that their properties are more severely constrained by the source data. Thornton sees consciousness as a process at the outer fringe of relational learning, just prior to the onset of creativity. According to this view, we cannot assume consciousness to be an exclusively human phenomenon, but rather the expected feature of any cognitive mechanism able to engage in extended flights of relational learning.Thornton presents key background material in an entertaining manner, using extensive mental imagery and a minimum of mathematics. Anecdotes and dialogue add to the text's informality.

Editorial Reviews

About the Author

Chris Thornton is Lecturer in Artificial Intelligence at the School of Cognitive and Computing Sciences, University of Sussex at Brighton, England.

Product Details

• Paperback: 216 pages
• Publisher: A Bradford Book (February 7, 2002)
• Language: English
• ISBN-10: 0262700875
• ISBN-13: 978-0262700870
• Product Dimensions: 9 x 6.1 x 0.4 inches
• Shipping Weight: 10.9 ounces (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #4,307,337 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

1 of 1 people found the following review helpful:

3.0 out of 5 stars patchy but interesting, August 7, 2000

By 

Dean McKenzie - See all my reviews

This review is from: Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems) (Hardcover)

Sure, the book jumps around a bit, is patchy when it comes to technical details and is fairly poorly referenced, but there's some interesting and inspiring ideas here. However, if you're from a country with a lousy exchange rate with the US (such as poor old Australia) then wait for the paperback edition!

7 of 10 people found the following review helpful:

1.0 out of 5 stars pretty trashy, June 15, 2000

By A Customer

This review is from: Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems) (Hardcover)

I was very disappointed by this book. He makes a valid point that most machine learning research is concerned with attribute-based (propositional) representations, and that many problems require relational (first-order) representations, but this is not a novel claim.

He calls propositional learning "fence'n'fill" algorithms, because they basically carve up the input space (e.g., a perceptron uses linear boundaries). The advantage is that they are fast and well-understood. In the final chapter, he proposes an algorithm for relational learning which is based on top of a standard fence-n-fill algorithm, but doesn't explain it well, and doesn't give any compelling evidence that it works. The papers on his web site are no better.

He intersperses what little technical material he has with some historical anecdotes about code-breaking during WWII, etc. It's not really clear what the connection is. Overall, the book just does not hang together.

If you felt inclined to buy this book, I would recommend you check out Andy Clark's excellent "Being There" instead.

5.0 out of 5 stars Very significant book if you want to know the limitation of neural networks in A.I., November 7, 2010

By 

King Yin Yan (Lantau, Hong Kong) - See all my reviews
(REAL NAME)   

This review is from: Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems) (Paperback)

This is a very good book. I'm surprised it is out of print and overlooked (0 review on Amazon so far).

The thesis is that spatial classification techniques (such as neural networks, support vector machines, principle component analysis, etc) are inadequate for classifying certain types of data that have "relational" or "logical" structure. This is related to the "propositional fixation of neural networks" as pointed out first by John McCarthy, but Thornton takes a more detailed look into the problem.

I may return here to write more about it...