About the Book: This  book provides a nice collection of historical, biological, and evolutionary arguments about the differences between biological neural architectures and computer architectures. The book begins with a discussion comparing the differences between analog and digital computation, then provides an introduction to neuroscience, eventually leading to an assessment of the current state of machine learning and what we might expect to see in the near and distant future.

Book Style and Organization:The essential argument of the book is best summarized by the old joke where you have someone looking for keys under a street light and another person asks the person looking for the keys “where did you lose your keys”? And the person looking under the street light says: “I lost my keys in the dark over there but the light is much better over here!” In other words, Anderson is trying to explain that if we are looking for “Keys” (i.e., the essential “key” features of biological computation) then we need to try to remain focused on biology and try to avoid distracting solutions which might be computationally easy or sociologically popular to implement but essentially implement strategies which don’t appear consistent with the biology. His method of argument is an informal but carefully crafted historical/evolutionary argument which begins in the past and moves to the future.

Indeed, Anderson's writing style corresponds roughly to a fascinating dinner conversation where multiple related topics are discussed. A seemingly innocent conversation thread might initially present itself as a series of interesting anecdotes. But upon reflection one realizes that a profound point has been made. In fact, Anderson plays this game at multiple levels by slipping in subtle and profoundly important arguments about the nature of biological and digital minds and computation. Thus, the book should be of interest to both novices and experts. Anderson's writing style reminds me of kid cartoons where the main text is designed for kids but the more sophisticated subtext is designed to provide humor for the adults. Similarly, Anderson's writes at multiple levels making the text suitable for multidisciplinary audiences who have varying levels of expertise and background in biological neuroscience and artificial intelligence.

As previously mentioned, the style of the book is very informal. Although successive chapters build upon discussions from previous chapters, the chapters are relatively self-contained essays and informal discussions. Fairly deep topics are covered in an informal and engaging way. So basically you can read the chapters in order or at random. You can read them in one sitting or multiple sittings.

Target Audience: This text is recommended for everyone. The general public will find this book to be a useful fun introduction to how computers work, how the brain works, and what are biologically inspired machine learning algorithms. Machine learning algorithm researchers will benefit from a unique historical, biological, and evolutionary perspective with insights into design choices for future machine learning architectures.

About the Author: Professor James A. Anderson is the author of After Digital: Computation as Done by Brains and Machines  who is a Computational Modeler and Cognitive-Neuroscientist at Brown University. Professor Anderson was one of the early pioneers in the field of artificial neural networks. In the 1970s, his research provided critical seeds which encouraged the resurgence of neural network research in the mid-1980s. Since then he has continued his work in artificial neural network modeling and in the words of Cognitive-Neuroscientist and Computational Modeler Professor James McClelland from Stanford University  “[Professor Anderson has] experienced the rise and fall and rise again of neural networks”. Professor James Anderson is also the author of the book Talking Nets where he interviews many of the pioneers in the field of Neural Networks. I strongly recommend that you take a look at the book Talking Nets as well to obtain a good historical perspective on the origins of the field of Artificial Neural Networks from the perspective of the original pioneers in the field.