When a computer scientist like Eric Baum bluntly states that the mind is a computer program, he is asking for harsh reviews from various academic groups, except for most computer scientists, who, like me, willingly agree with this statement.

What is thought? How did it evolve? Can we model it scientifically? These are some of the main questions that Baum tackles, in this large and ambitious book. Combining insights from computer science, information theory, cognitive science, and evolutionary theory, Baum proposes a set of theories for the processes involved in thought, without necessarily being able to point to the underlying biological mechanisms at work. In 1944, before the discovery of deoxyribonucleic acid (DNA), the great physicist Erwin Schrodinger wrote a book called What is life? [1], in which he looked at the processes required for life to exist and reproduce. Remarkably, Schrodinger predicted many of the features of DNA, based on his knowledge of physics, and his understanding of the problems that genetic reproduction has to solve. Baum acknowledges that he is seeking to do exactly the same to answer the question of what thought is.

Baum’s starting point is that thought and computation are synonymous. Noncomputationalist explanations are rejected, including all nonmaterial explanations, which makes a lot of sense; thinking is what brains do, and brains are physical objects when all is said and done.

Baum builds his theory on a number of very simple building blocks. First, evolutionary principles are paramount. Any explanation must be based on evolutionary theory, or it fails. A theory that has any value must obviously explain not just how things are now, but also how things got this way.

Second, the semantics of the world must be encoded in DNA. The world is enormously complex; how is it that we can carry a model of the world in our heads? How is it that we can interpret the rich structure around us, unless we are able to extract the signal from the noise? Baum uses the idea of information compression as a key principle. We can make sense of the world because we can extract key features from it. These features are built in somehow, and the only place that this can be encoded is in DNA.

Based on his own work in the fields of evolutionary algorithms and neural networks, Baum proceeds to show how these principles can be used to build a wide-ranging theory of how we think, and how thinking evolved. Along the way, he looks at some of the major issues that any convincing theory has to address. These include how organisms, from bacteria to homo sapiens, extract information about the structure of the world; how we are able to generalize experience, and to abstract from the specific to the general; and how we deal with the explosion of possibilities that face us every day, and whittle down choices to something we can handle.

Computer science, and artificial intelligence (AI) in particular, are used to explore these issues in more detail. The failures of AI to model intelligence are discussed in some detail, making the most of the opportunity to show why things have failed, and also pointing out where there have been some limited successes. The book makes good use of “standard” problems, such as the traveling salesman problem, to illustrate some of the complex issues at work.

While the book is written largely for the nonexpert, there is a fair degree of mathematical content, though this can be skipped without really breaking the flow of the argument. It certainly covers many of the more interesting areas of computer science, with good coverage of neural networks, genetic algorithms, and reinforcement learning.

The book makes many interesting points, and is worth reading as an account of how computationalists model our thought processes. This is a book that provokes the very thing that it studies: thought.