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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 PUBLISHER: MIT Press YEAR: 2004
Eduard Barbu Institute for Artificial Intelligence, Romanian Academy
INTRODUCTION 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 computationally.
D. Science will never explain consciousness. This is the mystical viewpoint on the nature of consciousness.
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.
SUMMARY AND COMMENTS 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 program?
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 literature.''
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 modules.
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.
FINAL REMARKS 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 subroutine). 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 concepts?
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.
REFERENCES 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.
ABOUT THE REVIEWER:
ABOUT THE REVIEWER 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.