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Review of  What Is Thought?

Reviewer: Eduard Barbu
Book Title: What Is Thought?
Book Author: Eric B. Baum
Publisher: MIT Press
Linguistic Field(s): Cognitive Science
Issue Number: 15.1716

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Date: Fri, 4 Jun 2004 19:56:59 +0300
From: Eduard Barbu
Subject: What is Thought?

AUTHOR: Baum, Eric
TITLE: What Is Thought?
SERIES: Bradford Books
YEAR: 2004

Eduard Barbu
Institute for Artificial Intelligence, Romanian Academy

Written by an advocate of Strong Artificial Intelligence
point of view, the book has every reason to be considered a
controversial one. Eric Baum proposes a computational model
that is meant to explain everything (mind, evolution,
language etc.). When the ambition is so big it is no
wonder that there will be voices rising against it. My
review will have the following structure. In this
introduction I will try to put the Baum's work in a global
research context for a scientific theory of consciousness.
I will present then the main ideas in every chapter and I
will address what I think are inconsistencies and what are
week points that should be elaborated. Finally I will
question Baum picture of the mind.

Some years ago philosopher John Searle (1980) distinguished
two points of view in Artificial Intelligence (AI):
a. The Strong AI (SAI) thesis is that human have cognitive
states by implementing the right kind of computation. Its
followers' hope is that computer scientists will find the
program of ''human mind''.
b. The Weak AI thesis is that the only significant
contribution of AI to the understanding of human
psychology is by building useful tools for testing
scientific hypothesis.

For a better understanding of Baum enterprise I will use
Penrose's classification of theories about mind and
Sloman's analyses of the field of SAI. According to Penrose
(1990) there are four viewpoints on the nature of mind:

A. All thinking, all sensations, feelings, etc. are
the result of implementing the appropriate
computation. This is SAI.

B. Consciousness is the result of the physical
actions of the brain. The computation just simulates
cognitive states but cannot actually produce them.

C. Consciousness is a characteristic of the brain
but the actions of the brain cannot be simulated

D. Science will never explain consciousness. This
is the mystical viewpoint on the nature of

SAI itself is not compact and easy to classify. The
different positions in the field are marked by different
views on what can be a theory of implementation (see Sloman
1992 for a detailed analysis). Because Baum thinks that
Searle can implement the algorithm which supposedly
describes the language competence of a Chinese speaker, he
is an adept of Sloman's T2 thesis which requires that
''computation be causally related to an explicit program'' .
By relying on Occam's razor for finding the best theory
compatible with the data, Baum escapes a well-known
objection to SAI. The objection states that if the behavior
alone is sufficient for the possession of cognitive states
then a program implemented as a giant lookup table, whose
behavior is equivalent with the behavior produced by an
intelligent system, will posses mentality.

The main tenet of this book is that mind is an evolved
program encoded in a compact code (about 10 Mega of DNA).
However this code cannot be replicated yet because we have
not the computational resources the evolution had. But with
the aid of evolutionary programming and using Occam's razor
we will succeed in the future to find the program of mind.

Chapter 1. Introduction
The introductory chapter has two parts. In the first part
the author lays out in short all his chief ideas. The
second part is just a road map to the rest of the book.
Baum starts by stating that his purpose is to present a
picture of the mind which is consistent with all our
present knowledge. He continues by rejecting the mystical
conception of the mind and then he presents SAI as if it
were the only alternative to mysticism. As we saw this is
not true, there are two other theories labeled B and C
above, which are worth considering. It is necessary at
least to present the other scientifically compatible points
of view and try to refute them.

Then he presents the main ideas that he tries to sustain
and defend in the rest of the book:

1. Mind is a computer program. More precisely mind is a
modular program with dedicated subroutines. These
subroutines are used in different contexts, thus
facilitating learning. The analogy and the metaphor are
explainable by code reuse.

2. ''Thought is all about semantics.'' For an entity to have
meaning is to have the capacity to capture and exploit ''the
compact structure of the world''.

3. The best theory is the simplest one. If there are many
theories (in our case programs) which explain the same
facts and made accurate predications, to choose the right
one we should use Occam's razor.

Unfortunately this chapter contains some inconsistencies:
For example, at page 3 Baum says that: ''The execution of a
computer program is always equivalent to pure syntax.'' On
any construal this statement is false. If a program in
execution (a process) is just syntax and the mind is a
computer program how can mind have semantics? A follower of
SAI would say that a process, if it were to be considered
intelligent, would have semantics.

At the same page the author claims that ''mind typically
produces a computer program capable of behaving'', which
immediately raises the question:
Is the mind a computer program or does the mind produces a

Chapter 2. The Mind Is a Computer Program.
The chapter builds in his most part on historical
considerations. The author introduces important concepts
for understanding the computational picture of the mind
such as Turing machine, universal Turing machine and the
self-reproducing automaton of John von Neumann.

Firstly Baum stresses that the creation of the Turing
machine was the consequence of the attempt to answer one of
the problems put by Hilbert: is there an effective
procedure for solving all the problems of mathematics (a
well define class of problems as Diophantian equations, to
be more precise)? The problem was independently and
negatively solved by Turing who developed the concept of
Turing machines and by Church who developed the lambda
calculus. Turing machines, lambda calculus and Emil Post
production systems formalize what an algorithm is. But at
this point someone might ask: what is the link between a
Turing machine and the thinking process? The only answer
that Baum gives is that a mathematician mind solving a
certain problem is equivalent to the lookup table of the
Turing machine and so the states of the mind of the
mathematicians are computational states. But there are at
least two questions that should be answered:

1. Mathematical thinking is just a small part of what
can be titled as thinking. Even admitting that the concept
of computation captures the mathematical thinking how about
the rest of thinking?

2. Is mathematical thinking computational? Many authors,
notably Lucas and Penrose, believe it is not. They use in
their support the celebrated Kurt Godel's theorem, also
mentioned by Baum in this chapter.

Moreover, the account Baum gives to the Hilbert problem is
a little bit misleading. He says at page 50 that Godel
answered the problem ''(mentioned earlier in this chapter)
in the negative: there is not effective procedure that can
prove all the true theorems of mathematics''. Instead of
being concerned with this problem, Godel was concerned with
other problem posed by Hilbert, namely to give an absolute
consistency proof and also completeness proof for the
mathematics. Godel showed that a system equivalent to the
Russell-Whitehead Principia Mathematica contains
undecidable propositions and one of them is the consistency
of the system.

In the second part of chapter 2 the author describes ''the
computational process that is life'', a process that creates
and maintains us. He gives us some background from
biochemistry and argues that the program of life is
isomorphic with a Post Production system.

Chapter 3. The Turing Test, the Chinese Room, and What
Computers Can't Do
This chapter is Baum's first attempt to answer some critics
of SAI point of view. He is addressing in principal the
problems of qualia and understanding. The first problem
that Baum takes on is the important problem of experience
or qualia. How is that we can feel, smell and so on if our
minds are just computer programs? Unfortunately Baum's
answer will be accepted as a valid one only by SAI adepts.
Basically, he says that the fact that we cannot accept that
computers can have qualia is just a failure of our
imagination. Moreover, he claims that we will accept this
when we have a chat with a computer that will insist that
it can have experiences (p. 67).

Baum then skips to the other problem, the problem of
understanding. In this context he presents the Turing test
and what is considered by most people to be the most
powerful argument against SAI, Searle's Chinese room
argument (CRA). The way Baum tries to refute the CRA is not
new and it is just a variant of the ''System Reply''.
Searle's answer to the ''System Reply'' is that he can
internalize all the elements of the system, he can answer
any question an external observer asks, but he will fail
again to understand Chinese. Baum rejects Searle's
conclusion that he doesn't understand Chinese and sustains
that, by internalizing a Turing machine that can answer
Chinese questions, Searle does indeed understand Chinese.
But that is to misunderstand Searle's proof. The thought
experiment that Searle proposes has as its point to show
that someone can pass a Turing test without understanding
anything, that human understanding is different from
computer understanding in that we have intentionality,
which computers lack. Baum puzzles us further when (pp. 78)
he misidentifies intention with the intentionality: ''This
concern that intention is something that only human beings
can have is still reverberating in philosophical

Chapter 4. Occam's Razor and Understanding
The theme of this chapter is how symbols in computer
programs can mean something.

Refuting the CRA and accepting the Turing Test as a
measurement for understanding, it is clear that the answer
to the problem of how the symbols of a computer can have
meaning will be based on the capacity of programs to
explain a series of facts and make predication. With this
meaning of ''meaning'' in mind Baum shows us how the external
data can be fit by a good theory. The chapter is a plea for
Occam's razor, which is seen by the author to be at the
heart of science.

The author presents three formalizations of Occam's razor.
The first formalization uses Vapnik-Chervonenkis dimension,
the second is based on the description length principle,
and the third one is ground on Bayesian probability.

Baum says very few words about neural nets, and this is
perhaps the greatest drawback of the book. In addition, the
presentation of neural nets misguides the uniformed reader
in that it can create the false impression that neural nets
are ''complicated models of brain circuits''. In fact, brain
circuits without being their models only inspire neural
nets. The author will want perhaps to discuss this and to
correct this drawback in a next edition of the book.

Chapter 5. Optimization
If the chapter before was concerned with the relation
between compact programs and the data they are the
description of, this chapter deals with the heuristic of
finding the best program consistent with some given data.
The author rules out the prospect of searching through all
the possibilities and then finding the most compact
description consistent with the data due to the fact that
such an algorithm has an exponential complexity. He also
stresses that we don't need the smaller possible
representation in order to extract semantics, but one
sufficiently smaller than the data. Baum argues that the
solution to this problem could be a general optimization
technique known as hill climbing. The hill climbing and its
advantages are nicely exemplified with the Traveling
Salesman Problem. The author speculates that a similar
technique was used by evolution in its searching for
meaningful possibilities.

Chapter 6. Remarks on Occam's Razor
Baum starts the most substantive part of the chapter 6 with
a discussion of a critique of neural networks. The critique
is that someone who looks at the inner structure of the
neural networks cannot understand what the net is doing.
Then the author elaborates this point of view, but not
sufficiently because some things remain unclear. Because
Baum doesn't make explicit his point of view in the so-
called ''Systematicity debate'' launched in 1988 by Fodor
and Pylyshyn (1988) I cannot tell what Baum thinks about
neural networks. However, my opinion is that Baum thinks
that connectionism can account for higher cognitive
functions only but implementing the classical model. Baum
confuses me further when he writes:
'' Neural nets are not sufficiently powerful to describe
minds. One must talk instead about more powerful
programming languages.'' (pp 133)

Does Baum think that neural networks are programming
languages!? If not, what else does this mean?

Then the author compares DNA with the source code and the
mind with the executable. Baum extends the analogy by
comparing commentaries in programming languages with the
base pairs in the DNA which are not read during
development. The chapter ends with a discussion of
generalization in neural networks and with a sketch of the
proof of the lower bound theorem.

Chapter 7. Reinforcement Learning
Baum argues that a passive framework, which has as a goal
just the data classification, is not sufficient to account
for consciousness. Instead we need a more powerful model
which can account for the interaction between robots (us)
and the world. The author discusses some reinforcement
learning techniques and argues that neural nets are too
weak representations.

Chapter 8. Exploiting Structure
The author draws the difference between recognizing
structure and exploiting structure. He correctly argues
that a theory that only accounts for data classification is
fundamentally incomplete. Three problems which involve
structure exploitation are presented (Blocks World problem,
the game of Chess and Go). Baum shows the insufficiencies
of already tried computational approaches for solving these
problems. He sustains that evolution has ''trained'' the mind
on vast number of problems. Now the mind has the capacity
to generalize and to solve problems it was not trained for
such as the above-mentioned games. In these cases the
program of mind is better because he has the capacity to:
''analyze new problems such as chess into a collection of
localized objects that interact causally ... ''(pp.196).
Unfortunately the author does not elaborate this point and
I cannot understand what notion of causality he has in
mind. Moreover, he should further explain why this capacity
cannot be captured by present computer programs.

Chapter 9. Modules and Metaphors
The chapter is a plea for the modular structure of the mind
and for the metaphorical nature of thought. In support of
the former the author brings evidence from cognitive
science biology and psychology. After succinctly presenting
the ideas of Lakoff and Johnson he explains the metaphoric
nature of thinking by code reuse.

Chapter 10. Evolutionary Programming
Some computational experiments for evolving code are
discussed. The author together with his colleague tries to
solve Blocks World problem by using evolutionary
programming. They succeeded to evolve a program that
implements the same algorithm that we use when trying to
solve this problem. However the resulted code is not
superior to a code not written by this method.

Chapter 11. Intractability
A more complete and detailed analysis of the techniques
used by computer scientists to solve general classes of
problems is given, along with examples of polynomial time
mapping of instances of NP complete problems. There are
also presented some experiments with evolutionary robotics
and it is argued that constraints propagation allows for
solutions for ''intractable problems''.

Chapter 12. The Evolution of Learning
A parallel between learning and development is drawn. It is
postulated that the learning process is largely dependent
on the inductive biases that evolution produced. Moreover,
learning affects evolution by ''Baldwin effect'' (the ability
of individuals to learn can guide the evolutionary process)
and culture. However, for Baum, the concept ''culture'' has
not its usual meaning, but it means passing the information
through ''parental instruction''. Perhaps for Baum culture
means the set of acquired behaviors? An example of cultural
interaction would be the parental instructions given by
mother bear to her children (p. 335). The author believes
that most of our concepts are innate. Baum is a supporter
of Chomsky's ideas and he states that from the perspective
of evolutionary programming: ''Chomsky's proposal that there
is some universal grammar wired into genome is
tautological'' (pp. 343).

Chapter 13. Language and Evolution of Thought
Language has a purely communication function. Language is
what differentiates animals and humans. Baum proposes a
theory of language where language words are seen as labels
for computational modules. Grammar, which has a nonstandard
definition here, is seen as a mapping function from
combinations of words in a sentence to corresponding code.
It is argued that thought has nothing to do with language
because concepts predate language. Learning new words
involve attaching labels to already existing computational

Chapter 14. The Evolution of Consciousness
This chapter deals primarily with consciousness, awareness,
qualia and free will. In the author's view, evolution
produced mind and finally consciousness. Baum tries to give
an account for the notion of self. Even if he thinks that
the mind is a distributed program with many subroutines,
the fact that the subroutines are working toward the same
end build the self. The author stresses also that we are
not aware of most of our computation and that our awareness
is just a module of mind which concentrates the results of
vast amounts of unconsciousness computation. One good
argument against physicalism is that it cannot account for
sensations and feelings. Baum argues against this on the
ground that sensations are necessary for the evolution
process and are built into our program at a ''fundamental
level''. But this doesn't address the question in that he
presupposes the existence of sensations, and does not show
how the execution of a computer code gives rise to
sensations. The author thinks that free will is entirely
consistent with physicalism. He says that free will does
not exist but it is a useful concept in predicting the
behavior of others.

Baum's book raises many questions. His simple system of
explaining everything could be extensively questioned. In
what follows I will raise some possible objections.
However, not being competent in genetics, I will not
question the validity of Baum's hypotheses from this point
of view.

1. Baum states that he offers a theory of mind compatible
with our present knowledge. But it can turn out that our
current knowledge is not sufficient for explaining mind.
For instance, a theory of physics compatible with the 17-
century knowledge would be plainly false.

2. Baum does not give a theory of implementation. This
makes his construction vulnerable at Putnam's (1988) or
Searle's (1990) arguments. Putnam for example argued that
if we are allowed to consider arbitrary disjunction of
physical states as realization of the formal states of an
automaton then the result we will obtain is that any open
physical system implements any finite state automaton
(FSA). Similarly, Searle argues that because syntax is an
observer relative notion, then a wall or stomach can be
seen as implementing any computation.

3. Surprisingly for a theory that tries to explain thought,
Baum doesn't refer to propositional attitudes and doesn't
try a naturalistic reduction of Intentionality. Without
this his model has not force and cannot explain anything.

4. He claims (chapter 8) that he solved the old
philosophical problem of whether the world exists or is
just an illusion but he is just begging the question. He
presupposes the existence of physical objects and of a
code as the compact representation of the world and then
tries to prove that the world really exists.

5.His theory of language is roughly this:
a. Every concept corresponds to a piece of code (a
b. The most part of concepts is innate.
c. The meaning of expressions is compositional and it is
obtained by a module calling other modules.
d. Learning new words means to attach labels to presently
existing modules.

This raises at least the following questions: If the
most part of concepts is innate how can they fit in 10
MG of code in DNA? How can a speaker choose the right
sense of a word when the word is ambiguous (In Baum's
formulation, how a subroutine knows what subroutine to
call)? How does this model treat pragmatics? How is the
new code added and how is it compiled when we learn new

6.Baum seems to think that evolution itself is a
computational process (note that this is different from
saying that the evolution can be simulated
computationally). What algorithm does evolution implement?
For example Baum says: ''evolution effectively searched
over combinations of meaningful macros. Add long legs, and
see if that helps''. If evolution tried many possibilities
where is the evidence?

Despite these problems, Baum's book was a pleasure to read.
The author manages to explain hard concepts like self-
reproducing automata and NP completeness to those less
familiar with them. Moreover, he has a keen sense of
humor that adds joy to the reading.

Fodor, J. and Z. Pylyshyn (1988) Connectionism and
Cognitive Architecture: a Critical Analysis, Cognition: 28.

Penrose, R. (1990). The Emperor's New Mind. Oxford
University Press.

Putnam, H. (1988) Representation and Reality, The MIT
Press, Cambridge, Mass.

Searle, J.R. (1980) Minds, brains and programs, Behavioral
and Brain Sciences: 3.

Searle, J.R. (1992) The Rediscovery of the Mind, Cambridge,
MIT Press.

Sloman, A. (1992) The emperor's real mind: review of Roger
Penrose's The Emperor's New Mind, Artificial Intelligence.
Eduard Barbu is a researcher at the Romanian Institute for
Artificial Intelligence. He was involved in several
European projects. His interests are: formal and lexical
ontology, cognitive science, philosophy of language and
mind. He is presently working at a dictionary of Philosophy
of Mind.

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