What Is Thought?

by Eric B. Baum

Book Overview

In " What Is Thought?" Eric Baum proposes a computational explanation of thought. Just as Erwin Schrodinger in his classic 1944 work "What Is Life? "argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation. Baum argues that the complexity...

• Format: Hardcover
• Language: English
• ISBN: 0262025485
• ISBN13: 9780262025485
• Release Date: January 2003
• Publisher: Bradford Book
• Length: 492 Pages
• Weight: 2.10 lbs.
• Dimensions: 1.3" x 7.1" x 9.4"

Customer Reviews

Reviewing "What is Thought"

In many respects Baum's book is orthodox cognitive science: "the discussion in this book follows what I perceive to be folk wisdom among computer scientists interested in cognition." (page 2) In fact, it is probably the best such text that I've read in years. I highly recommend this book to anyone studying cognitive systems. Baum basically agrees with Werbos' definition of an intelligence: "a system to handle all of the calculations from crude inputs through to overt actions in an adaptive way so as to maximize some measure of performance over time" (P. J. Werbos, IEEE Trans. Systems, Man, and Cybernetics, 1987, pg 7). Or, in Baum's words: "I am proposing to think about creatures...that are given a reward function...learning and computing algorithms...The creatures then apply these algorithms to maximize reward during life." (page 396) Of course programs that do exactly that have been around for a long time: "Adaptive systems using learning matrices" (K. Steinbuch and E. Schmitt, Biocybernetics in Avionics, Gordon and Breach, 1967, pg 751). In his book Baum frequently equates reward/fitness/utility, U, with number of offspring a creature has, N. In fact, a more biologically accurate model (for mammals) might be U=(N-2)/L where L is the creature's lifespan. But Baum is quite UNorthodox in that he believes in an extreme dependence on innateness. He believes that via our DNA we receive a large number of computational subroutines which contain a great deal of knowledge about the world. Baum believes that "semantics comes from compression...If one compresses enough data into a small representation, the representation captures real semantics, real meaning about the world." (page 102) But, unfortunately, a number of DIFFERENT models may fit the data. As Baum himself admits: "there are likely many possible locally optimal solutions as good as the one evolution has come up with that may differ considerably in detail." "There may be many compact discriptions ...aliens might think of the world using a substantially different description..." (page 212) So something which has "meaning" for you, with your model of the world, may have NO meaning for someone else (having some different world view). Baum seems to admit as much on page 226: "...there is some evidence for an evolved module for religious faith, which might well exist whether or not there is in actuality an anthropomorphic god." Unfortunately, then all meaning is purely RELATIVE and it makes no sense for Baum to talk about some "concept really present in the world." (page 162) Rather, concepts are defined (INVENTED) by people in the course of their efforts to organize their observations of the world. Our concepts need not really exist IN the world. They are best regarded as mental fictions. Although Baum frequently distinguishes animal intelligence from human level intelligence he makes no room for the existance of an artificial intelligence which is not isomorphic to human reasonin

Thoughtful

The first half of this book is an overview of the field of artificial intelligence that might be one of the best available introductions for people who are new to the subject, but which seemed fairly slow and only mildly interesting to me. The parts of the book that are excellent for both amateurs and experts are chapters 11 through 13, dealing with how human intelligence evolved. He presents strong, although not conclusive, arguments that the evolution of language did not involve dramatic new modes of thought except to the extent that improved communication improved learning, and that small catalysts created by humans might well be enough to spark the evolution of human-like language in other apes. His recasting of the nature versus nurture debate in terms of biases that guide learning is likely to prove more valuable at resisting the distortions of ideologues than more conventional versions (e.g. Pinker's). His arguments have important implications for how AI will progress. He convinced me that it will be less sudden than I previously thought, by convincing me that truly general-purpose learning machines won't work, and that much of intelligence involves using large quantities of data about the real world to choose good biases with which to guide our learning.

The profound made simple

The review published in Nature does a better job than I could, so I'll excerpt it. "'What is thought?' is not a new question. For Aristotle, thought was what the soul does, and for Descartes it was the unequivocal evidence of one's existence. For Eric Baum, a US expert in machine learning, it is a computer program. This is not a superficial assertion: Baum pursues the idea with elegance, clarity, and considerable pursuasion... "It is important not to treat the idea that thought is a program in too superficial a fashion. Popular texts often include explanations such as `brain is like hardware and mind is like software.' Baum intends a level of sophistication far above this. Thought for him is the process that 'understands' the complexities of the world... "Baum's central point is that it is possible for programs to evolve, adapt, and learn, making them more powerful than anything that a programmer can concoct... "Baum gives a reasoned response to John Searle's claim that no program can 'understand' the world, and to Roger Penrose's contention that conscious insight lies outside the logic that can be achieved by computation... "this is a splendid book for discovering what is new. It will enthrall some computer scientists and provoke some philosophers. And it should engage general readers who wish to enjoy a clear, understandable description of many advanced principles of computer science."

Is Evolution The Secret To Intelligence?

Why can humans rapidly carry out tasks, such as learning to talk or recognizing an object, that seem intractable for computers?According to Eric Baum, the human brain is much like a computer, but it runs programs that are different from the ones usuallywritten by human computer programmers. The programs run by the brain are insightful or ``compressed''; they have built ina good deal of knowledge or ``understanding'' about the nature of the world. Human programmers have difficulty generating such efficient or compressed programs (except for limited special purposes), because to do so requires vast computing resources, far beyond what one can accomplish with pencil and paper or even with presently available computer assistance. The key to understanding intelligence, according to Baum, is the theory of evolution; in the process that brought humans into being, evolution cycled through many billions of generations of organisms, in the course of which, in effect, vast computational resources were brought to bear on the problem of generating useful algorithms. The real secret to thought is thus stored in our DNA, which preprograms us with algorithms thatare more efficient and powerful than the ones usually available to computer scientists. With this starting point, Baum proposes answers to many old riddles. Our sense of ``self'' reflects our origin in an evolutionary struggle for survival toward which all components of our biology are directed. ``Free will'' is a useful approximation because of the great complexity of our brains (and our limited knowledge about them) and the concommitant difficulty of predicting a person's behavior. Baum illustrates his arguments with numerous examples drawn from biology, psychology, and computer science; the material is generally quite interesting, though at times perhaps too detailed for a casual reader. His arguments are surprisingly persuasive, and, while certainly no expert, I suspect that Baum is closer to the mark than most of the old and new classic writers on these problems.

A deep and brilliant book

Baum's book aims -- and in my estimation succeeds brilliantly -- at illuminating what we know and don't know about computation and the modeling of mind: memory, learning, perception, reasoning, etc. Baum summarizes the main perspectives of various schools of thought on the topic, notably including both proponents of the artificial intelligence enterprise as well as critics, plus neural, sociobiological, psychological and philosophical points of view. He summarizes the main results of computer science and shows their relevance to mind. Best of all, the book is very well-written, and despite the fact that it includes considerable technical depth, it does not presuppose prior knowledge of the subject and should therefore be accessible to a broad audience.