On the Foundations of Artificial Intelligence and

Στους γονείς µου (to my parents)

Over 33 years ago I was awarded a PhD for the following work. Because, its main Thesis and the essence of the reviews included here-in have stood well the test of time and, in addition, its relevance and impact is been steadily increasing I decided to publish it on Research Gate as a whole.∗

Your comments will be greatly appreciated and acknowledged.

Although several parts of this work have been updated and published over the years, the ‘whole’ contains important unpublished elements and details in addition to providing an integrated picture. This published version differs from the original only in the formatting and the deletion of a rather redundant figure that had been unclearly converted in the new format. ∗

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On the Foundations of Artificial Intelligence and Human Cognition

P. A. M. Gelepithis

Thesis submitted for the Degree of Doctor of Philosophy in Cybernetics in the Division of Cybernetics, Brunel University, Uxbridge, Middx., England.

May, 1984.

© Petros A. M. Gelepithis, 1984

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Preface The motivation for this research was twofold. First, a deep rooted motivation to learn something about the human nature and, in particular, the workings of the human mind. Second, a more recent motivation, to investigate the possible limitations of the human brain. The triggering occurred when I came across the working assumption: computers understand concepts. If this hypothesis was true then it would be possible for a computer to understand in particular the concept of time. This research, therefore, started as a constructive goal-oriented attempt, namely, to write a program to understand the concept of time! The change in orientation followed quickly; to explain time you need... . I felt the impossibility of the task and set myself the aim to disprove the working hypothesis: computers are able, in principle, to understand human language. In the course of this research I was variously helped by all members of the Dept. of Cybernetics, and those participating in the informal, and stimulating (inhibitorily or excitatorily does not matter) Thursday discussions. I want nevertheless to specifically express my gratefulness to Prof. Frank George and Dr. Mike Elstob. The latter for his patience to open up the blinding part of my stubbornness, and his anticipatory and revealing comments, but above all his kindness that made our sessions a rather relaxing experience. To the former for his disarming questions, the incisive and direct comments, and the financial support; but above all his friendliness and broadmindness even when I was attempting to 'build' an institute for interdisciplinary research.


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Contents

Preface....................................................................................................................................3 Abstract..................................................................................................................................5 Introduction...........................................................................................................................6 1. A backdrop to our analysis ...........................................................................................13

1.1. On 'human methodology'................................................................................13 2.2. Functionalism, identity theory, and the conceptual framework of this thesis 21 2. On the Foundations of Artificial Intelligence..............................................................30

2.1. What AI is about ............................................................................................31 2.2. Can machines think? A review.......................................................................33 2.3. AI and the quest for the criterion of the mental ............................................42 3. Understanding and Meaning: the Foundations of Human Cognition ......................47

3.1. Conceptions of human understanding ...........................................................48 3.2 Review of theories of meaning........................................................................56 3.3 Conclusions ....................................................................................................74 4. The Phenomenon of Understanding: Consequences for AI.......................................77

4.1. The nature of human understanding: human primitives.................................77 4.2. The nature of understanding: primitives .......................................................83 4.3. The only way to human automata: Evolution ...............................................85 5. Semantics in Theoretical Perspective...........................................................................91

5.1. Is a theory of meaning possible? ...................................................................92 5.2 Towards a theory for the semantic structure of the human brain...................96 Epilogue: Evaluation and Prospects ...............................................................................101 List of Abbreviations ........................................................................................................104 List of Tables and Schemata ............................................................................................105 Notes ...................................................................................................................................106 Bibliography and references ............................................................................................110


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Abstract One of the fundamental assumptions of artificial intelligence is the hypothesis that machines can, in principle, be built to understand human language. This thesis argues that this hypothesis is wrong. Concretely, the proposition we establish reads: let, an ideal robot, R, be a robot which: a) is equipped with human sensors or robot sensors functionally-equivalent to human ones, b) is able to manipulate human linguistic data, and c) is able to connect in any way a) with b). Let, furthermore, R be constructed; then, R is, in principle, unable to understand human language and we, humans, can never improve R to understand human language. In developing our argument, investigation of the foundations of human cognition, and in particular the notions of meaning and understanding have been found necessary. Our analysis of these notions has led us to propose, in rough outline, an explanatory theory of meaning, as the first step towards a unified treatment of human cognition. Our work is Cybernetically materialistic. By this we mean that: one, it is based on the materialistic tradition as this is expressed by what we believe to be its four main characteristics: (i) the materialistic assumption, (ii) the evolutionary hypothesis, (iii) the principle of causality, and (iv) the synonymity assumption. Two, it adopts the Cybernetic methodology as this was conceived by Norbert Weiner and Arturo Rosenblueth.


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Introduction "'Tis evident, that all the sciences have a relation, greater or less, to human nature; and that however wide any of them may seem to run from it, they still return back by one passage or another. Even Mathematics, Natural Philosophy, and Natural Religion, are in some measure dependent on the science of MAN; since they lie under the cognizance of men, and are judged of by their powers and faculties." John Locke, 1690. It is well known that the main goal of artificial intelligence (AI) is the construction of intelligent machines. See, for instance, how "The Handbook of Artificial Intelligence" delineates this young, multifaceted, and rapidly changing field (Barr and Feigenbaum 1981): "ARTIFICIAL INTELLIGENCE (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior-understanding language, learning, reasoning, solving problems, and so on." (ibid. vol. 1, p 3). What are less well known are the conceptual foundations of this claim. The main aim of this thesis is to investigate the validity of these conceptual foundations. Specifically we shall examine the fundamental assumptions which underlie Turing's argument for the possibility that computational mechanisms built by humans could behave intelligently. To remove any possible misunderstanding we shall explicitly say here that the possibility of constructing an automaton that could behave in a way that would be perceived as intelligent but that would achieve that effect by deceiving was not what Turing argued for (see section 2.2). Turing's primary presupposition was that machines can understand human language (MUHL)*. It may seem strange that although Turing's conclusion that machines can think met with strong opposition, his underlying hypothesis was never scrutinized. The reason for this lays perhaps in the fact that the key concept in this Some concepts used in this work cannot be adequately described by a single word. To avoid repeatedly using longish phrases for such concepts, a few abbreviations have been introduced, as is the case here. The reader is referred to the list of abbreviations on page 189 should he or she become confused at any point. *


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hypothesis -understanding- has not been elaborated by either workers in AI, philosophers or psychologists. Accordingly, I set myself the task of inductively defining the concept of understanding. Inductively, because I believe that if measurable progress is to be made in the investigation of mental phenomena, we should try to apply the scientific method, in the classical sense of the expression, to them. Of course this commitment does not imply that there exists a distinct phenomenon that our definition demarcates. It only reflects our belief that the word understanding refers to something (phenomenon, class of phenomena, mental state or whatever) and therefore it may be scientifically investigable. Another objection that may be raised is the relevance of our investigation examining the nature of human understanding- to the initial question of the validity of

the

MUHL hypothesis. The short answer to this is that since the MUHL

hypothesis refers to human language, and human beings in very many instances do understand human language, a connection between machine understanding and human understanding seems probable. Furthermore since as humans we tend to be more aware of our own understanding than a possible machine understanding it seems advisable to start investigating human understanding. Of course this reasoning does not absolve us from eventually reaching a definition of understanding that will accommodate both human and machine understanding (actually reached in 4.2). Furthermore, with respect to our main aim, our study of human understanding, (section 3.1), leads us to examine, section 3.2, the nature of meaning. So, to sum up our plan of investigation, we start with examining the nature of human understanding which in turn leads us to a double task: one, to appropriately generalize our analysis to include machine understanding; two, to examine the nature of meaning. We come now to a few points concerning the title of this thesis and, specifically, the part of it referring to the foundations of human cognition. The first has to do with the use of the expression 'human cognition' instead of, for instance, philosophy of mind. It is true that understanding and, especially, meaning lie at the core of the philosophy of mind so that a title worded in terms of it may seem more appropriate. In fact it is not. The reason is twofold. One, understanding and meaning lie at the core of various other disciplines so that a preference for the expression ‘philosophy of mind’ would not be justified, and similarly of course for any other discipline. The second reason gives direct support for our choice. Human cognition maybe thought to be comprised of four closely interrelated phenomena (or classes of phenomena): © Petros A. M. Gelepithis, 1984

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learning, memory, language and thought. Now understanding and meaning cut across all these four phenomena and therefore, in this sense, they lie at the foundations of human cognition. Our second point concerns the accuracy of the definite article 'the' when it refers to the foundations of human cognition. Since understanding and meaning are central to the study of learning, memory, etc it follows that any serious study of the latter phenomena will contain at least an implicit account of the former. It follows that a complete account of meaning and understanding should consider at least two types of account. First, a direct account of them in terms of the various disciplines that study understanding and meaning. Second, an implicit account through the reports that the various disciplines give to learning, memory, etc. In this thesis we only give an account of the first type on the assumption that the second type does not give any account of the nature of understanding and meaning that is not contained in the first one. This assumption is based on the fact that all accounts of the second type are given in terms of the various disciplines by which human cognition is studied which are the same disciplines in terms of which the first type of accounts are given. It follows that an account of the nature of meaning and understanding seems to be completely covered by an account of the first type. Of course, a further examination of meaning and understanding, for instance an investigation into the possible mechanisms, does require an account of the second type. This brings us into the last delimitation of our research. Namely, that it is only in so far as it is necessary for achieving our goal- to judge the validity of the MUHL hypothesis- that we shall investigate and analyze the question of meaning and the phenomenon of human understanding. In other words, our research of the foundations of human cognition is entirely demarcated by the kind and extent of our investigation of meaning and understanding, an analysis which, in its turn, is entirely determined by the goal of this thesis. So, to sum up the dual character of this research. The goal and conclusions of this investigation concern the foundations of AI; whereas the analysis of meaning and understanding as well as the prospects following it concern the foundations of human cognition. We come now to introduce the cluster of questions which had an influence on discussing the constructability of thinking machines, and which, incidentally, all are concerned, directly or indirectly, with human nature. © Petros A. M. Gelepithis, 1984

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Historically the first relevant question was what has come to be known as the mind-body problem. This is, of course, the philosophical trade name for the dispute between idealism and materialism. True, there is a vast philosophical literature on this issue but I frankly believe it to be of historical and strictly professional interest only. Furthermore, seeing the mind-body issue in the light of its equivalence to the materialist-idealist dispute, it loses much, if not all, of its appeal. For, suppose that there were unanimous acceptance of either the materialistic or the idealistic hypothesis. Then, there would be numerous problems concerning the workings and structure of the human brain and mind, the possibility of man-animal communication etc, but I believe there would be no-one who would raise the so called mind-body problem. The main influence that the mind-body question had in discussions of the AI problem was that those working on it could, and did, have an opinion on the materializability of the AI goal merely on grounds of their own beliefs and stance on the mind-body question (e. g., Lucas 1961; Minsky 1968; Fodor 1981). More recently a question, closely related to issues traditionally coming under the heading of the philosophy of mind and, directly addressing the constructability issue, has been raised: Could machines be made to think? This question, which we shall usually refer to by the shorthand ‘the mt question’, has stimulated a large literature and the acceptance of its realization has been probably THE theoretical cornerstone in the nearly exponential development of computer science and AI in the last few decades. Unfortunately, despite the huge literature on the mt question, (Ross Anderson, in his editorial introduction to "Minds and Machines", writes for many hundreds of papers during the 50's), AI itself was never much keen to be rigorous in investigating its foundations. The reason may have been its early successes and the fact that its opponents were far from convincing either (see section 2.2). This lack of rigour and an air of omnipotence among AI workers very soon led them to the uncritical adoption of further assumptions. One of them is that computers can, in principle, be made to understand natural language. It should be stressed here that 'understanding' has not always been used by workers in AI with its common sense meaning. It has been used, for instance, to mean recognition, but the existence of such a usage should not, as it sometimes does, conceal the fact that the common sense of understanding is also assumed to be attainable by appropriate programs. Furthermore, © Petros A. M. Gelepithis, 1984

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we should keep in mind that both machine thinking and machine understanding are just two more goal-labels for what has recently been termed "the AI problem", that is, the materialization of the goal an affirmative answer to the mt question urges one to pursue. So we see that a number of questions have been raised and assumptions taken for granted which all address in one way or another the AI problem. What all of them have in common is that they all assume, explicitly or implicitly, to be known, what the human characteristic is. It seems, therefore, reasonable to try to formulate a question in terms which would not presuppose specific human abilities as the characteristic of homo sapiens. That may well read as follows: Could humans construct something which would have the human characteristics and which would not have been made of the material human beings are made of? For brevity reasons we shall hereafter refer to this question as the man-made-mind (mmm) question. But even our new question is not sufficiently free from presuppositions which may mislead one's investigations. The mmm question is unnecessarily anthropocentric and therefore potentially misleading. For it drives one to look for the human characteristics whereas what we need is a question that would put man and machine on an equal initial footing. Now such a question can be provided in terms of communication. Since human communication is an act of sharing, it follows that human communication may as well be an act of mutual human understanding. Subsequently, a definition of communication in terms of our generalized notion of understanding will serve well as a basis for a question which would both reflect the AI problem and be independent of human characteristics. We shall therefore consider the possibility of man-machine communication (mmc) as the most appropriate question reflecting the AI problem. We come now to highlight a few particular features in the 'exploration map' we outlined so far by summing up the thesis' contents. So, in chapter one, we set the scene for our analysis. Specifically, in the first section we provide for the basic conceptions required for chapter five; whereas in section two we present the conceptual framework of this thesis in relation to the two most

successful

contemporary belief systems in the philosophy of mind, namely, functionalism and identity theory. Then, in the second chapter, we enter the main part of our research by discussing © Petros A. M. Gelepithis, 1984

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the foundations of AI. In particular, in section one we outline the basic conceptions of the scope of AI, by some of the leading workers in this field, to provide a reference framework for our subsequent work. Sections two and three are complementary. In the first of these we review the AI problem and pinpoint the main inadequacies of Turing's approach to the mt question. Then, in the third section, we conclude that the mmm question is a potentially misleading formulation to serve as basis for judging the validity of the MUHL hypothesis, and justify the choice of the mmc question as an appropriate one for a fair comparison between man and machine. Having set the scene and reviewed the mmm question we proceed to analyse the fundamental notions of meaning and understanding. So, in chapter three, we review the literature on the problem of meaning and the phenomenon of human understanding; we conclude that: (i) All answers to the question 'what is meaning or/and understanding' are inadequate though useful. (ii) Understanding and meaning are inseparably tied, and they may be best viewed as a single phenomenon- the phenomenon of human understanding- with meaning in the role of the material substratum for this and similar phenomena (e.g. creativity). These conclusions lead us to the core of our analysis. Chapter four attempts to provide an adequate answer for the phenomenon of human understanding and argues against the MUHL hypothesis. In particular, we 'inductively' define human understanding and subsequently generalize our definition to include the notion of machine understanding. Based on this approach we argue that neither man-machine communication, nor machine understanding of human language is possible. Finally, in chapter five, we look at the prospects of our investigation in human cognition. Concretely, in section 5.1 we argue that although no adequate theory of meaning, in the classical sense of the word 'theory', is available, we can see no reason against the possibility of an explanatory theory of meaning. Then, section 5.2, we give a sketchy outline for the representation of human meanings in topological terms a move that opens up the way towards an explanatory theory of meaning. Lastly, I would like to draw attention to a few conventions I have used in writing this thesis. First, I use single inverted commas to denote a linguistic expression or a symbol, and double ones in cases of quotations. Unfortunately, due to lack of a convenient alternative symbolism I have also used single quotes to indicate ambiguity. Second, in giving references, used the symbol '*' to separate the original if known, edition of a book or paper from the one I used; the use of '+' stands for a © Petros A. M. Gelepithis, 1984

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posthumous edition, whereas the use of '?' indicates that the original edition of the book or paper in consideration is not known to the author. Lists of abbreviations, tables, and schemata along with a number of notes (signified by Ni) are included as appendices.


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1. A backdrop to our analysis "This, after all, you know, really is a finger: there is no doubt about it: I know it and you all know it. And I think we may safely challenge any philosopher to bring forward any argument in favour either of the proposition that we do not know it, or of the proposition that it is not true, which does not at some point, rest upon some premise which is, beyond comparison, less certain than is the proposition which it is designed to attack." George E Moore, 1922. "Furthermore, one of the most important features of the development and the analysis of modern physics is the experience that the concepts of natural language, vaguely defined as they are, seem to be more stable in the expansion of knowledge than the precise terms of scientific language, derived as an idealization from only limited groups of phenomena." Werner Heisenberg, 1959. Unsere ganze Philosophie ist Berichtigung des Sprachgebrauchs. All our Philosophy is improvement of language. Lichtenberg, 1801, as quoted by von Mises 1951*1968 p 17. Une science n'est qu'une langue bien faite. A branch of science is nothing else than a well-constructed language. Condillac, 1780, as quoted by von Mises 1951*1968 p 17. As its title indicates this chapter provides a general framework of our work. In particular, section one briefly discusses a few questions in the philosophy of science which are pertinent to our research and succinctly states our own methodology. Then, in section 1.2, we present the basic assumptions of our analysis in juxtaposition to the two most successful contemporary frameworks in the philosophy of mind: functionalism and identity theory. 1.1. On 'human methodology' The objectives of this section are two. One, to state our own methodology in © Petros A. M. Gelepithis, 1984

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this thesis. Two, to provide for the basic conceptions on the nature of science and the fundamental concepts of the scientific method which are required in sections 5.1 and 5.2. Any human inquiry (and therefore the various disciplines too) is differentiated from others by two things: (i) The set of entities/phenomena that are, or claimed to be, investigated. (ii) The inquirer (e. g., approach or particular method he or she employs). It seems plausible to postulate that human knowledge, whether reached by inquiry or not, is not separable. This does not imply, for example, that disciplinary boundaries should not be drawn, what it does nevertheless imply is that human knowledge cannot be put into interaction-proof compartments. Disciplinary boundaries are by necessity formalistic in nature, and therefore whenever convenience suggests to draw a boundary line, we should always keep in mind two things. Firstly, the arbitrariness of the border, and secondly, and most important, whether the isolated-to-be-studied area retains its characteristics. Naturally questions of this sort are too complicated to discussed in one section of a thesis, nevertheless, we shall put forward two more questions concerning human knowledge, closely related with the ones already introduced, in order to give a taste of the problems involved in the pursuit of human knowledge and to put into a wider perspective what follows. The first question, an external one, has as its pivotal notion that of purpose; the second question, an internal one, centres around the notion of methodology. Science as part of both human knowledge and human inquiry is, expectedly, subject to all the questions introduced so far, plus questions, external or internal ones, arising out of the far greater degree to which the various features of human knowledge or/and inquiry exhibit themselves in 'human science'. Probably the first question raised concerns the meaning of the expression 'human science', mostly known, rather honorifically, as science. Philosophers and scientists have provided a number of conceptions regarding ‘human science’. Bernal (1954*1965), Braithwaite (1957*1960), Feynman, (1963), George (1981), Nagel (1961), to name but a few, may be considered as representatives of the various main conceptions, though, of course, not of the various shades within each conception. As an example of the variety of ways that human science has been conceived to © Petros A. M. Gelepithis, 1984

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be, we shall consider the differing views on the scope of human science. There are two options which people often, consciously or unconsciously confuse. In the first we understand the word science in its narrow sense as exemplified only by physics and chemistry. Then the question is whether the remaining fields of investigation can, in principle, attain scientific status. The second option is to broaden the meaning of the term science to include all the fields of human investigation. The important point then is to remember that there are methods and results of physics and chemistry which the newly baptised 'sciences' (fields of investigation) do not exhibit. In either case there is a clear distinction between physics and chemistry on the one hand, and, say, psychology or economics on the other. For a comparative view on the objects, methods, and results of human investigation, concern or action the reader may find interesting Table 1. Table 1. A comparative view of some of the basic human concerns in relation to some of their corresponding kinds of methods adopted and results obtained. Names Philosophy Astronomy Physics, Chem. Biology Psychology Linguistics Sociology Economics Mathematics Technology Art

OI or concern H. Knowledge Stars Matter Alive entities Human entities Human language Human society H. productive Rs Defined H. La. Tools Everything

OI=Object of Investigation MI=Method of Investigation RI=Result of Investigation O =Observation E=Experiment De=Deduction R=Reflection P =Prediction D=Description

O * * * * * * * * * * *

MI or action E I De * * * * * * * * * * * * * * * * * * * * * *

R * * * * * * * * * * *

RI or action Ex P D C * * * * * * * * * * * * * * * * * * * * * * * * * * * *

I =Induction Ex=Explanation C =Clarification

In favour of the second view we have two facts. First, that the "translationequivalents" (Lyons 1981 p 37) of science in other languages, (e.g. Wissenshaft in Deutch; science in French; nauka in Russian) are much wider in their coverage than the English word science is. Second, that an increasing number of people qualify the


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remaining fields of investigation or groups of such fields with the adjectivized term science. Probably the best reason for retaining the observed distinction is a relatively recent tendency to use the words science and scientific as terms connoting significance. Thayer (1970 p viii) very neatly describes the phenomenon as follows: The remarkable achievements of science are but one part of its spectacular history in the twentieth century. A by-product of perhaps equal or greater import is the phenomenon of scientism. Scientism is pseudoscience, bureaucratized science, parodized science. Scientism deifies the methods and the trappings of science. Its ideologies are its techniques; the primary if not the sole justification for inquiry is the elegance and the current popularity of the techniques employed.... Like a cancer, scientism has fed upon the growing status and prestige of science. Nowhere is this more apparent than in academe. Where mathematics used to be an adequate index of a course of study, it has become obvious that mathematical SCIENCES is politically and budgetarily a more potent term. The study of management is giving way to the management SCIENCES; the study of psychology and sociology and related fields to the behavioral SCIENCES; and the study of politics and of rhetoric to political SCIENCE and the speech SCIENCES; or communication SCIENCES. For us, and for the purpose of this thesis, we take 'human science' S to be the conscious attempt to remove imprecision from that part of human language used to deal with problems in S, as well as to improve any other tool used to serve human ends. In this way we consciously try to encompass all uses of 'human science' though, of course, we do not agree with a number of these (e. g., weapon construction), and furthermore believe that the aim of human science should be the betterment of human life, a goal to be reached peacefully and cooperatively by the human species. The main external questions of a particular science S1 concern its scope and purpose; the main internal ones deal with its empirical foundation and internal organization (collectively known as philosophy of S1). We shall say a few things in turn. Kybourg (1968) summarizes four answers to the question of purpose, (that is, what do we do science for) in the human scientific endeavour: A1: control of environment A2: explanation of individual happenings A3: prediction of future events A4: understanding of a general kind of phenomena.


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At the other end of the spectrum, an increasing number of scientists ask for a moratorium concerning scientific research on the basis of the scientists' social responsibilities. The main goal of such a requirement is to counterbalance the increasing number of misuses that science has been put to. After this very brief excursion into the aims of science we return now to the main theme of this section that is, human methodology and in particular the scientific method. For my part, scientific method and the common sense methods of human inquiry differ only in degree. In other words, they lie on the same continualum, that is, the discrete counterpart of continuum. For us the scientific method is an abstraction, at times useful, out of the very many scientific methods which, in turn, stem from the combination of two factors: one, human experience; two, human language. It is quite easy for someone to see the basic elements of the scientific method operating in the common sense method of inquiry. A very simple example will suffice. Consider the statement 'fire burns'. It contains all the characteristics of the, so called, scientific method. It is a generalization out of particular instances; it can be used to predict future happenings and explain certain phenomena or events. So, although everybody concerned accepts the two characteristics of the scientific method, namely, (a) its empirical foundations and (b) its internal organization, philosophers have succeeded in dividing themselves between those, so called empiricists, who consider only (a), those rationalists, who consider only (b) and those, compromisists, who try to find a middle way between the two, so called, "extremes". For rationalism, which can be traced back to Descartes and Plato and more recently to Carnap, Reichenbach and Hempel, "the intellectual content of any natural science can be expressed in a formal propositional system" (Toulmin 1974). Such a claim differs only slightly, if at all, from the goal set by the Unity of Science movement, (the high point of Viennese Positivism), to provide a fully comprehensive account of nature on a single axiomatic pattern. Empiricists, on the other hand, apart from claiming primacy for the raw facts of reality, also claimed that general theoretical principles have authentic scientific content only when interpreted as empirical generalizations about directly grasped empirical data and, furthermore, they came to think that abstract theoretical entities, (i.e., theoretical terms), must be understood as logical constructions from

more

fundamental elements that can be directly identified in human experience; the step © Petros A. M. Gelepithis, 1984

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over to claiming that logical constructs can not possibly have a referent was an easy one to be taken, and indeed was taken by Mach in claiming that atoms are merely intellectual fictions. (see Toulmin 1974). In this context we shall say a few things about the celebrated difference between empirical laws and theoretical laws (or theories). It is true the word theory is used in many different senses (Lacey 1976 p 110; Saugstad 1980), from being regarded as mere speculative activity, through being considered as a field of study, to a unified system of laws or/and hypotheses with explanatory force, and as an attempt to make explicit what we presuppose. Putting aside the Empiricists-Rationalists debate and the semantic differences concerning theories we come to what may rightly be called the core of the scientific method, namely, the relation of the empirical laws to the theories, and the structure of the theoretical laws. To start with we shall state what are, in general, held to be the basic differences between empirical laws, (or laws in brief), and theories. According to the Britannica there are two such differences: (a) the difference between observable and theoretical terms; (b theories cannot ordinarily be tested and accepted on the same grounds as laws. The reasons usually given to support the second difference are two: one, that the nature of theories is different from that of the laws since the former purport to explain a number of laws and predict others, whereas the latter express relations among a small selection of observables; two, that theories are sometimes accepted in terms of considerations like internal coherence, plausibility and simplicity, which of course have no place in the acceptability of laws, and some others on the basis of the laws that the theories explain or predict. To be able to reach a reasonable decision on the laws-theories debate one should look carefully at the alleged differences between observables and theoretical terms. Observables are terms which all refer to things or properties of these things that can be observed; theoretical terms (logical constructs or theoretical entities), are terms referring to things or their properties that are not necessarily observables. The central question of the laws-theories debate is whether the distinction between them is fundamental or not. Views, naturally, exist on either side. Nagel (1961), on the side of those who claim the difference to be an important one, bases his arguments mainly on the fact that laws are always associated with experimental procedures, whereas the same does not always hold true for theories. Opponents of © Petros A. M. Gelepithis, 1984

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this view on the other hand, (e.g. Feyerabend), base their arguments on the fact that there is no criterion in whatever terms (e.g. "observable" or "sense data") which can provide us with a sharp distinction between laws and theories; the difference between the two notions are differences of degree. In the next few paragraphs we shall give our reasons for a more or less compromising view. One reason for retaining the distinction between laws and theories on the one hand; and reasons for supporting the belief that a fundamental, that is pertaining to their nature, difference does not exist, on the other hand. To start with it is clear, I believe, that distinguishing between laws and theories is useful. This is because laws and theories can provide two 'methodologically separable' platforms for furthering human knowledge. The former can provide a stable basis on which one can safely build or experiment; the latter provides a flexibility which laws lack, to the same degree and therefore provides the means for a multiplicity of interpretations and hints for new directions of search. On the other hand to debate the possibility of a fundamental difference between empirical and theoretical laws, semantic confusion aside, would be to debate about the possibility of a fundamental difference between observables and theoretical terms. Now in my brain, the fact that the boundary between observables and theoretical terms is shifting is an adequate reason for someone to believe that there cannot be a fundamental difference between the two notions. For what account can possibly be given for the diachronic shifting of words like 'air' or the synchronic shift of terms like 'sulphuric acid'? Can the shifting of the word air from the status of a debatable theoretical construct for the pre-Socratics to the status of an observable for most, if not all, contemporary laypersons be explained in terms of a fundamental difference between observables and theoretical terms? Or to consider a synchronic example, how can it possibly be explained in terms of the observable/logical construct distinction, the fact that 'sulphuric acid' or a 'dwarf star' are considered to be observables for a chemist or an astronomer whereas at the same time they may only be accepted as observables on grounds of authority. Clearly, the only answer, compatible with the findings of the various established human disciplines, is in terms of the differing brains, either in a diachronic or in a synchronic fashion. But such an answer would imply that the claimed fundamental differences between observables and theoretical entities are groundless. © Petros A. M. Gelepithis, 1984

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Finally, we come to the axiomatic view of theories. It is the view that theories can be considered as postulational (axiomatic) systems from which empirical laws may be deduced. The best known analogy for this view is Euclidean Geometry considered as a postulational system wherein the role of empirical laws may be taken up by the Euclidean theorems. The main objection against this view is that such a deductive scheme has never been actually fully realized and is debatable whether it is, in principle, realizable. Nevertheless for reasons that were sketched in the introduction to this section we shall include here the basic components of a scientific theory T (see for instance Nagel 1961; Hesse 1967). They are three: 1. The (formal or abstract) calculus C of T, 2. The intended interpretation (or model) M for T, and, 3. The correspondence rules R of T. The calculus of a theory is the set of the abstract relations holding between the terms of that theory. Probably the best known example of such a calculus is Euclidean Geometry. A model for a theory is a set of more or less familiar concepts in terms of which the calculus of a theory may be presented. The intended interpretation of a theory, accordingly, is the primary set of such concepts. Should a differential equation of the second order be considered as a calculus then Newton's second law could well be presented as a familiar model for it. The correspondence rules of a theory are a set of rules that relates at least some of the postulates of C to experimental or observational notions. As an example one may refer to the relation of the theoretical notion of ‘electron transition’ to the empirical notion of ‘temperature changes’ which is established through Plank's law of radiation. Against this background we shall paint now our own criteria for accepting a proposed analysis, and introduce the methodology we shall follow. We have only two, complementary, criteria for accepting a proposed analysis: C1: The proposed analysis must be consistent with one's fundamental and particular beliefs. If it contradicts either or both of them then it must be shown that either C2: at least one of the beliefs is mistaken or, if none of the beliefs is mistaken, and none of the premises of the proposed analysis is mistaken, that C3: the results reached through the proposed analysis are 'better' than the results © Petros A. M. Gelepithis, 1984

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reached through the initially-held analysis. I would like to notice, though it may be obvious, that in case of conceptual ambiguity this must be firstly raised before the above criteria can be meaningfully applied. Secondly, conviction may rely, equally well, on experiential (e.g. experimental) or conceptual (e.g. philosophical analysis) grounds, or, of course, on both and therefore the question above can be restated in these terms instead of "attain scientific status". We come now to the methodology we shall use in pursuing our central question, (the mmc question). It will simply be the axiomatic approach. That is, we shall define the key words involved in the mmc question in terms of primitives and subsequent words in terms of the key ones; finally, we shall try to make explicit the fundamental beliefs on which our working hypotheses will be based. To this latter end the second part of the next section is offered. 2.2. Functionalism, identity theory, and the conceptual framework of this thesis The aim of this section is to state the basic hypotheses on which our subsequent analysis is based and juxtapose functionalism with "identity theory". According to J.A.Fodor, probably the leading proponent of this doctrine, functionalism seeks to provide a philosophical account of the level of abstraction characterizing the cognitive sciences; whereas "cognitive sciences" is, for Fodor, a collective term covering the fields of artificial intelligence, computational theory, linguistics, cybernetics, and psychology (Fodor 1981 p 124). In what follows we shall not deal with functionalism's aims, nor with Fodor's conception of what comprises the field of cognitive science (for a substantive argument though, see Norman 1980), we shall instead comment on Fodor's claim for functionalism. The root of this claim seems to be the observation that SIMILAR functions occur quite frequently in nature. Consider, for instance, the functional similarity provided by the analogous wings of a bird and a butterfly. Although these structures are similar in function, they are entirely different in structure and in origin. So, whereas a bird's wings are supported by an internal skeletal framework, the wings of a butterfly are essentially made up of a stiffened membrane. (Nason and Dehaan 1973). This rather well established observation seems to have provided the springboard © Petros A. M. Gelepithis, 1984

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for the functionalist claim that the SAME function may be materialized in a wide, or even indeterminate, variety of ways. Or, the even more sweeping generalization that the same function may be realized, (in contrast to "materialized", to allow for immaterial entities), in a variety of ways. Or still, to use Fodor's words, functionalism "recognizes the possibility that systems as diverse as human beings, calculating machines and disembodied spirits could all have mental states." (ibid). But, from SIMILARITY to SAMENESS, and from MATERIAL to IMMATERIAL, there is a long way that, to our opinion, can not be 'walked over' in terms of unsupported claims. The core of such claims is Fodor's decision to state that "the psychology of a system depends not on the stuff it is made of (living cells, metal or spiritual energy) but on how the stuff is put together." (ibid). Symbolizing with 'Sp' "the psychology of a system"; we take Fodor's claim on the following symbolic form: (i) Sp=f(o); (ii) Sp≠f(m). Now there are two possibilities: either 'organization', that is, the ways the stuff is put together, is a function of the stuff or vice versa. And in symbolic form either (iii) o=f(m); or (iv) m=f(o). But from these two possibilities (iii) has never been invalidated and (iv) has never been observed. On these grounds it is justified to choose (iii) which will imply that claiming (ii) is against EVIDENCE. Functionalism's essence, at least as conceived by Fodor, can be seen from another angle when considering its basis. "Functionalism is the philosophy of mind based on the distinction that computer science draws between a system's hardware, or physical composition, and its software, or program." Of course functionalism's basis is not this distinction itself but what this distinction presupposes, namely the proclamation of logic (or spirit, or soul) to a status independent of the actual world and indeed the universe. To quote: "Nevertheless, the software description of a Coke machine does not LOGICALLY [my emphasis] require wheels, levers and diodes for its concrete realization. By the same token, the software description of the mind does not LOGICALLY [my emphasis] require neurons." That's true; neither the Coke machine nor the human mind LOGICALLY requires some matter to operate on; they only actually do require so. Functionalism is the most recent form of idealism, the major other ones being © Petros A. M. Gelepithis, 1984

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dualism, immaterialism and neutral theories concerning the human mind. Tables 2 and 3 show the major versions of materialism and idealism in accordance with their stance on the mind-body problem. Table 2. Main types and subtypes of Idealism. Dualism

I D E A L I S M

Functionalism: Mental states can be states of any system (material, mental, etc) Neutral theories: Both matter and mind aspects of one underlying fundamental reality. Immaterialism: Everything that exists is mental.

Interactionism: Mind and matter affect each other causally. Epiphenomenalism: Only matter causally affects the mind Psychophysiological parallelism: Matter and mind are causally unrelated, like synchronous clocks.

Absolutism Subjectivism Phenomenalism

We come now to the identity theory and our fundamental hypotheses on which we base our analysis. To start with the identity theory for the human mind is not, strictly speaking, a theory, that is, a hypothetical deductive system. It is a hypothesis which happens to be compatible with the scientific, (i.e., sophisticated common sense), outlook in general and the recent scientific developments in biochemistry and neurophysiology in particular. Furthermore, there is a variety of different forms (or versions) of the materialistic hypothesis (or psychophysical monism) concerning the mind. Table 3 lists and summarizes the main versions of the identity theory; here we shall present a short account of these forms. Probably the best short exposition of the psychoneural identity thesis is provided by Smart (1959*1970), in his "Sensations and brain processes". His account is strictly speaking restricted to sensations without an explicit reference to the totality of the cognitive processes but this restriction can be straightforwardly removed and that did


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actually happen with Armstrong' (1968) exposition. We therefore confine ourselves to presenting Smart's version only. "Let me first try to state more accurately the thesis that sensations are brain processes. It is not the thesis that, for example, 'after-image' or 'ache' means the same as 'brain process of sort X' (where It is that in so far as 'after-image' or 'ache' is a report of a process, it is a report of a process that HAPPENS TO BE a brain process. It follows that the thesis does not claim that sensation statements can be TRANSLATED into statements about brain processes. Nor does it claim that the logic of a sensation statement is the same as that of a brain process statement. All it claims is that in so far as a sensation statement is a report of something, that something is in fact a brain process. Sensations are nothing over and above brain processes." (Smart 1959*1970 pp 55-56). Table 3. Main types and subtypes of materialism.

M A T E R I A L I S M

Behaviourism

Central State

Radical: psychological terms have no reference. De facto: psychological and Behavioural terms have the same referent and different meaning. Logical (Analytic): Psychological and behavioural terms will eventually become synonymous. Methodological: the only suitable working level is the behavioural. Disappearance: No recourse to ordinary lang. Translation: Mental and neural terms have the same reference and different meaning. Logical: Mental and neural terms will eventually become synonymous. Methodological: the only suitable working level is the neural. Emergentist: There are mental terms which are not reducible to neural terms.

On this basis we shall now discuss the alternative versions of the identity hypothesis (see Table 3). As a point of fact logical and methodological central state materialism do not really compete with the other versions of the identity theory. The reason is that there are no proponents for these versions. Let me try to justify this fact briefly. Were methodological materialism true it would imply that psychology (except from physiological psychology and linguistics (save neurolinguistics), for instance, could not in principle contribute anything to our knowledge for the structure and workings of the human mind. On the other hand should the logical identity version is adopted, the essence of it © Petros A. M. Gelepithis, 1984

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would also have to be adopted, that is, that mental terms should be redefined neurologically. So, anger, for example, instead of being commonly defined as: (d1): "a fierce feeling of displeasure, usually leading to a desire to hurt or stop the person or thing causing it" (Longman Dictionary of Contemporary English 1978). should be redefined to something like: (d2): a neural state n1 of no less than x 'neurodynes' usually leading to a neural state n2 of 'neuropotential' y. Furthermore, the two descriptions will be synonymous. It is this sort of claim that makes the logical identity version virtually devoid of proponents. We shall not here try to point out the reasons for this fact. We shall nevertheless notice that such acclaim brings in not only the central problem of the nature of meaning but other fundamental ontological assumptions as well. Let us try to clarify the latter clause. Assume that a referential theory of meaning has been adopted (see section 3.2); that would not be an adequate premise for accepting the synonymity of the mental and neural descriptions d1 and d2. For it may well be the case that both a dualist and an identity theorist accept the referential theory of meaning. It would then follow that d1 is different in meaning from d2 by the very fact that d1 and d2 refer to different entities. Let us consider now what is generally accepted (see for instance Smart 1959*1970; Feyerabend 1963*1970; Shaffer 1963*1970), to be the most powerful objection to the translation form of central state materialism. Namely, mental properties are not reducible to physical properties even if we accept the identity of mental processes to brain ones. Shaffer, (1963*1970 p 130), has given, essentially, the following form to this objection: a person reports the having of some mental event (e.g. thought or sensation) and notices that something has occurred and that this something has some properties. Now, according to Shaffer, this person does not notice, in many cases at least, any physical features (i.e. properties), although he or she does notice some feature. It follows that this person must notice something other than a physical property, that is, he or she must notice a mental feature of the reported event. To meet this objection the disappearance form of the identity theory has been developed. As it may have already become clear the key fault of the identity hypothesis seems to be its logical inconsistency. For at least as it is usually stated it


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implies the existence of non-physical features. Feyerabend (1963*1970) attacked the identity hypothesis itself; to be exact the statement of it, say S. His argument is very simple. Although stating the identity hypothesis implies dualist there is no reason to have S as a premise. Moreover if commitment to materialism is taken as a premise then one is forced to conclude that S itself is false! Generalizing, he further concludes that "any recourse to existent terminology" should be avoided. Expectedly his indirect attack against our ordinary speech raised numerous objections. They all focus on the following point: should no reference to our ordinary language be made, no connection of our findings to it is possible and we shall therefore be unable to understand what we are talking about. Feyerabend's rejoinder is again simple: "This objection assumes that the terms of a general point of view and of a corresponding language can obtain meaning only by being related to the terms of some other point of view that is familiar and known by all. Now if that is indeed the case, then how did the latter point of view and the latter language ever obtain its familiarity? And if it could obtain its familiarity without help 'from outside', as it obviously did, then there is no reason to assume that a different point of view cannot do equally well." (Feyerabend, 1963*1970 p 141). It may be interesting to notice here that apart from direct proponents of the disappearance form (e.g., Rorty 1965*1970), well known proponents of alternative forms of the central state materialism have also been attracted to it (e.g., Smart 1963; Feigl 1967). On the other hand the dualistic implication of the identity hypothesis seems to have contributed to the strengthening of the emergent materialism. Let there be two successive events A and B and further assume that event B is inexplicable in current physical terms. The question is why and the answer to it may equally well lead to emergent materialism, interactionist dualism or a quest for recasting our physical concepts. Emergentism (or emergent materialism) claims, plausibly, that B belongs to a class of events which cannot be accounted for in terms of the physical laws. They (and their corresponding processes) can be accounted for only in terms of laws (emergent laws) which can not be deduced, even in principle, to those of physics. Notice that such a view seems to be materialistic since it does not postulate the existence of either emergent properties or emergent stuff. Of course whether or not this emergent-laws materialism can be defended without eventually referring to either © Petros A. M. Gelepithis, 1984

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emergent features or processes is an open question but we shall not deal with it here. Furthermore we shall provisionally, at least, accept that emergent processes, events or features do not contradict the term materialism. So one of the well known current emetgentists, Bunge, gives the following account of emergentist (Bunge, 1980p. 6). "Emergent materialism (M5) holds that the CNS, far from being a physical entity - in particular a machine - is a biosystem, i.e. a complex thing endowed with properties and laws peculiar to living things and, moreover, VERY peculiar ones, i.e. not shared by all bio-systems." and a bit further, "Mental functions would thus be CNS functions and, far from being purely physical processes, they would be emergent relative to the physical level." Finally Bunge qualifies his assumption as follows: "The emergence claimed for the mental is double: the properties of a CNS are not possessed by its cellular components but are SYSTEMIC PROPERTIES and, moreover, non-resultant ones; and they have emerged AT SOME POINT IN TIME in the course of a long biotic evolutionary process." I wish to conclude this short presentation of the emergentist ideas with a similarly short comment on the notion of "systemic properties". I think is fair to say that the concept "systemic properties" is meant to imply that there are properties which are exclusively properties of a system, that is, there are properties which the constituent parts of a system can not possibly possess. This claim is based on the implicit assumption that any property that the constituent parts may possibly possess must be specifiable while the parts are in isolation for otherwise any property that exists and is not so specifiable must necessarily belong to the system. But this implication is false. For it may well be that the so called 'systemic property' is not specifiable in isolation (either because we lack the appropriate technology or sufficient knowledge, or even because it is only specifiable 'when', (that is, at a time and during a certain period following that time), the constituent parts are not isolated)), but that certainly does not imply that the 'systemic property' can not exist in isolation. I believe it to be clear by now that the identity hypothesis is, essentially, an extension of materialism to account for the structure and workings of the human © Petros A. M. Gelepithis, 1984

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mind. Given the complexity of such an undertaking it is only natural therefore that a variety of versions have been 'emerged'. Nevertheless I am far from being convinced that all these different shades of opinion are necessary for the scientific study of the human nature. Furthermore, there is a common premise to all the different versions of the psychoneural hypothesis which we believe to be an adequate basis for studying the workings of the human mind. This common premise is the assumption that every mental state is a state of the central nervous system (CNS). In what follows we shall not argue for this adequacy-of-common-premise belief; we shall instead take it as one of our fundamental assumptions on which our subsequent analysis is based. As such it is adequate and Ockham's razor dissuades us from adopting a particular version of the identity hypothesis instead of its common premise. So, in what follows we shall lay bare our basic hypotheses. These are: 1. The materialistic hypothesis is quite simply a name for the assumption that 'the universe', that is, what exists, is matter. Matter, in turn, is used in what follows, as an undefined word (N1). 2.

The evolutionary hypothesis is a name for the assumption that the universe

changes. Change is used as an undefined word. In particular ‘the earthian evolution’ is the assumption that the current earthian ‘entities’ (matters) have developed from previous earthian entities. 3. Causality is a name for the assumption that the changes of the matter are linked. Link is used as an undefined word. 4. The synonymity hypothesis is a name for the assumption that 'mental process', (similarly 'mental state', 'mental event'), is another name for 'brain process', (similarly 'brain state'). I wish to finish this section with a short comment on the reluctance of many people to accept a materialistic account of the human nature. Consider the following passage: "Is not the theme [the mind-body problem] under consideration the most important for man, reaching to his fundamental nature? If Feigl is right, then man is no more than a superior animal, entirely a product of the chance and necessity of evolution. His conscious experiences, even those of the most transcendent creative and artistic character, are NOTHING BUT the products of special states of the neural machinery of his brain, itself a product of evolution. If Polten is right, man has in addition a supernatural component, his conscious self that is centered on his pure ego. Thus with his spiritual nature he transcends the evolutionary origin of his body and brain, and in so far could participate in © Petros A. M. Gelepithis, 1984

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immortality." (Eccles 1973 p xii). This is certainly one of the clearest expositions for the reasons behind the reluctance to accept a materialistic account of ourselves. It is to be noticed that Eccles, despite his eminence in neurobiology, does not base his reasoning at all on findings or indications from the neurosciences. His point is focused on the worthiness of human life and in so far as it is a genuine stance, that is, it is generally defended and widely applied to all walks of life against the mistreatment and degradation of human life is a position, at least, worth of consideration. So, let us assume, for the sake of argument, that this is actually the case. Namely, idealism is a viewpoint making human life worth living and defending human dignity against infrahuman actions. The question then is: is materialism by its nature incapable of providing humans with such a perspective? It should be clear to all, I believe, that logically the answer is no. There is nothing in the materialist claims to imply a 'low profile' for humans and consequently a degrading treatment of them. True, Eccles, and most opponents of materialism, describe "man" to be "no more than a superior animal"; but is that what materialism implies for human beings? Let us contrast this view with the following description: the human species is the conscious link in the awesome evolutionary process; a process lost in time in either of its directions. Is this a degrading picture for women and men? I believe not.


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2. On the Foundations of Artificial Intelligence "A number of laboratories around the world are investigating how to program computers to be a little more 'intelligent' than they are. Such studies soon came up against a fundamental problem concerned with Otherwise, attempts to get computers to do things normally requiring human intelligence, are likely to remain superficial and, in the long-run, unproductive." Donald Michie, ?*1974. "If we believe that we really and truly understand Euclid, or cookery for that matter, there is an acid test. We can be asked to convert our understanding into program, and so cause the machine to do geometry of compose recipes as the case may be. We must certainly own, from the present level of achievement in computer programming for complex tasks, that we do NOT yet understand either Euclid or cookery: we may possess kind of instinctual 'understanding' of such tasks, analogous to that by which a high-jumper gets himself over the bar or an acrobat balances on a wire, but we have not achieved understanding. If we had, we could program it. If we cannot, then although, as HOMO SAPIENS we may display this or that capability, we cannot claim truly to understand, in the given respect, what it is to be human." Donald Michie, 1974. As we wrote in the introduction the initial aim of this thesis is to investigate the materializability of a fundamental assumption of AI, that is, the hypothesis that machines can be constructed to understand human language. So, in the first part of this chapter we present the various views of what AI is about, so that we have a clear idea of the place of our research in the map of the AI field and its potential reference to subdisciplines of AI. Then, section 2.2, we review the AI problem and pinpoint a few characteristics of the research work in this area. This opens the way for discussing, section 2.3, the merits of the mt, (Can machines think?), mmm, (man-made-mind), and mmc, (man-machine communication), questions. (see also introduction).


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2.1. What AI is about There are many views of what artificial intelligence (AI) is about; Professor Donald Michie, of the Machine Intelligence Research Unit at Edinburgh University, wrote in 1974: "The scientific goal of research work in artificial intelligence is the development of a systematic theory of intelligent processes, wherever they may be found; thus the term ‘artificial intelligence' is not an entirely happy one." (Michie 1974 p 156). A more committed presentation of practically the same view is readily revealed by the following excerpt which also outlines an alternative view concerning the goal of AI. "For many, AI is identified as a narrowly focused field directed toward the goal of programming computers in such a fashion that they acquire the appearance of intelligence. Thus it may seem paradoxical that researchers in the field have anything to say about the structure of human language or related issues in education. However, the above description is misleading. It correctly delineates the major METHODOLOGY of the science, that is, the use of computers to build precise models of cognitive theories. But it mistakenly identifies this as the only purpose of the field. Although there is much practical good that can come of more intelligent machines, the fundamental theoretical goal of the discipline is understanding intelligent processes INDEPENDENT of their particular physical realization." (Goldstein and Papert 1977 p 84-85). A rather personal and much more cautious attitude concerning the goals of AI has been expressed by Professor Yorick Wilks of Essex University. "I take Artificial Intelligence to be the enterprise of causing automata to perform peculiarly human tasks, and by appropriate methods, though I do not want to discuss that difficult word "appropriate" here,..." (Wilks 1973 p 114). Nevertheless this multiplicity of goals stems from a single fundamental assumption. This assumption in turn takes two forms. The first version, expounded by the founders of the field, Marvin Minsky and John McCarthy, takes for granted that a thinking machine can be made and attempts are made to accomplish it. "The only reason we have not yet succeeded in formalizing every aspect of the real world is that we have been lacking a sufficiently powerful logical calculus. I am currently working on that problem." (Prof. J. McCarthy, Stanford University; as quoted by Prof. J. Weizenbaum, MIT). "This book presents a group of experiments directed toward making intelligent © Petros A. M. Gelepithis, 1984

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machines. Each of the computer programs described here demonstrates some aspects of behavior that everyone could agree require some intelligence.... Thus Bobrow's STUDENT program... rivals, within its algebraic scope, the average high-school student. Evan's ANALOGY program ... works also at this respectable level." (Minsky 1968 p 1). And more recently N.J. Nilsson, a leading figure in AI writes: "AI has also embraced the larger scientific goal of constructing an informationprocessing theory of intelligence. If such a SCIENCE OF INTELLIGENCE could be developed, it could guide the design of intelligent machines as well as explicate intelligent behavior as it occurs in humans and other animals. Since the development of such a theory is still very much a goal, rather than an accomplishment of AI, we limit our attention here to those principles that are relevant to the engineering goal of building intelligent machines." (Nilsson 1980*1982 p 2). The second version, which may be termed the weak approach to AI tends to be less ambitious and more cautious. Workers adopting the latter viewpoint formulate their views in a way such that they neither reject nor take for granted what the first group tends to term the AI problem (i.e. how to make a thinking machine). In the words of Marr (1977 p 37) "The goal of Artificial Intelligence is to identify and solve tractable information processing problems." Or more fully: "The central goals of Artificial Intelligence are to make computers more useful and to understand the principles which make intelligence possible." (Winston 1977 p 1). In sum, there are two main goals of AI. The one, which may be called the engineering goal of AI, is to construct intelligent computer systems; the other to develop a theory of intelligence based primarily, if not exclusively, on the information processing approach. As outlined in the introduction, part of our work is concerned with the engineering goal of AI and, specifically, questions its attainability. In so doing it addresses the philosophical underpinnings of AI and, therefore, its relevance to subdisciplines in this field may only concern their scope. On the other hand our work on semantics (ch. 5), may be of direct interest in AI. Concretely, our representation model (section 5.2), may be seen as a promising technique in knowledge representation, and therefore, of some value in areas like expert systems, natural language processing, and robotics.


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2.2. Can machines think? A review The section is divided in three parts. In the first two we will survey the arguments for the AI problem and the contributions against the belief that machines could be built to think. In addition, in part 2.2.B, we offer an explanation why the 'against-arguments' were not so far convincing. Finally in the third subsection we shortly present and comment on the ‘no-reason-why-not’ belief. A. Turing's test and reasoning It is interesting to notice that despite the large number of people who take for granted that machines could be made to think only Turing (1950) attempted to produce an argument to support such a belief. An adequate reason may be the plausibility of Turing's claims coupled with the, currently, widely held view of an omnipotent science. Here it is, for instance, how McCarthy one of the founders of the AI field, supports his conviction for the eventual realization of the AI problem. "The alternative is to say that there is an area of nature that is not reachable by science. And nothing in the history of science supports that hypothesis." (McCarthy, as cited by Kolata 1982 p 1238). This view itself may be challenged on, say, semantic grounds, but this is not our purpose here. Our point is that whatever this view may be considered to be, it cannot claim to have the status of an argument. Similarly, just claiming that "the brain is a computer", (Hayes 1978), cannot seriously be considered as arguing in favour of the attainability of the engineering goal of AI, and can hardly count as a discussion of "the foundations of Artificial Intelligence" (ibid). On the other hand there were scientists who although they did not argue for the possibility of constructing a thinking machine they nevertheless refuted existing claims against such a possibility (Turing 1950; George 1956) and explicitly asserted that "we can think of no reason to doubt the possibility" (George 1979). (see 2.2.C for

a comment on the latter view). In what follows we shall concentrate on

pinpointing the inadequacies of Turing's reasoning. "Can machines think?" Turing proposed to consider this question, (the mt question), in his 1950 paper "Computing machinery and intelligence". He furthermore


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stated that this "should begin with definitions of the meaning of the terms "machine" and "think"." Despite this methodologically sound contention Turing rather surprisingly, chose "to replace the question by another". The reason was that "The definitions might be framed so as to reflect so far as possible the normal use of the words", which attitude, Turing goes on, is dangerous. With this wording I may agree depending on the circumstances what I dissent with is Turing's methodology on the matter. Concretely, Turing, facing the possibility of providing statistical or dictionary definitions, (which it seems he considered absurd in the circumstances), preferred to replace his fundamental question without considering alternative ways of defining the question's key words "machine" and "think". One of the alternative ways to define the terms 'machine' and 'think' would be so that they reflect as far as possible the characteristics of the referents of the words instead of their "normal use of the words". In other words he could try to give characteristic or, at least, stipulative definitions of the terms involved instead of dictionary or statistical ones. We may notice in passing, that stipulative definitions is a common practice among scientists without this necessarily making their disciplines less scientific. In any case Turing eventually decided to replace the mt question with another one expressed in terms of what he called imitation game. In general, the objective of this game is for an investigator A to distinguish between two things B1 and B2. It follows, according to Turing, that the mt question can be legitimately replaced by the question: can an interrogator A distinguish between a human H and a machine M? Turing's answer to the new question, the distinguishability question, was in the negative and by extrapolation, (considering the AI problem and the distinguishability question equivalent), he answered affirmatively the mt question. There are, therefore, two main points to be considered. First, whether Turing's test is an equivalent formulation of the mt question. Second, and this regardless of our answer to the first question, to consider the soundness of his reasoning in favour of realizing the AI problem. With respect to the equivalence issue views vary from those expressed by committed proponents of the Turing test, through to views characterizing it as the least worst option, to counterexamples aiming at ridiculing it. Feigenbaum and Feldman, for instance, although they accept the inadequacy of the Turing test, they nevertheless seem to be satisfied with ‘the degree of equivalence’ it provides. In their words: "Though the test has flaws, it is the best that has been © Petros A. M. Gelepithis, 1984

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proposed to date." (Feigenbaum and Feldman 1963 p 10). As an example against the imitation game one may consider Ziff's comment on it. His remark is based on the observation that ‘thinking’ cannot be equated with performance, and as an example he notices the readiness with which a person may be deceived by a performing actor. (Ziff 1959*1964). Nonetheless, ‘Behaviourism', which may be viewed as the main cause explaining Turing's decision to consider his imitation game equivalent to the mt question, has been taken up by the majority of workers in AI. To avoid a possible misunderstanding let me qualify this statement. ‘Behaviourism' has taken several forms from the time it first stated by J.B. Watson in 1913. The sense we understand ‘Behaviourism', in this chapter, is that of methodological Behaviourism, (see Table 3), implying that behavioural tests are the sole criterion for accepting a viewpoint or judging a situation. And it is this particular version of Behaviourism as the sole arbiter for the feasibility of the engineering goal of AI that has been adopted by the majority of the workers in AI. To see methodological Behaviourism in contemporary action consider the following claim by workers at, or around, Yale as quoted by Prof. Searle: "How do you know that other people understand Chinese or anything else? Only by their behaviour." (Searle 1980 p. 421). Of course it is not "only by their behaviour". Behaviour is neither necessary nor sufficient requirement for understanding. It would have been nice and promising were behaviour sufficient for understanding, not the least, for it would have provided an effective shield against liars! On the other hand I find it hard to believe that a lot of people would act differently from what they do, on the event that they had achieved understanding of, say, Einstein's gravitation theory. What is it then that makes Yale's view sounding sound? It is the fact that by implicitly referring to other persons brains it brings in interaction which in its turn implies behaviour. Now, although behaviour is not necessary for personal understanding it may be necessary for interpersonal understanding. The latter possibility carries all the force of Yale's reply, though inadequately. The reason is that interpersonal communication does not actually rely on behaviour for its realization; it relies on the partly common past history of the participants. This last point becomes fatal for the Yale's view when the analogy from interhuman to human-machine © Petros A. M. Gelepithis, 1984

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interaction is pursued. (For an elaboration of this point see section 4.3). We may come now to our second point, namely the soundness of Turing's reasoning. His basic claim, presented under the section title "Learning Machines", can be summarized as follows: for a discrete state machine to think i.e., to imitate an adult human mind, we have to appropriately teach a child programme. In his words: "Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain”. And a bit further Turing concludes: “We have thus divided our problem into two parts. The child programme, and the education process”. Before going on to see the kind of support that Turing gave for this claim it may be revealing to read the opening paragraph of the relevant section (last section of his 1950 paper): "The reader will have anticipated that I have no very convincing arguments of a positive nature to support my views. If I had I should not have taken such pains to point out the fallacies in contrary views. Such evidence as I have I shall now give." Turing's evidence is a combination of two expressed beliefs of his: one, the close analogy between evolution and "the education process"; the other, that programming (that is, programmers) is (are) almighty. In support of the former, Turing states three "identifications" between what he calls "education process" and "evolution". These, for Turing, are: (1) "Structure of the child machine=hereditary process", (2) "Changes of the child machine=mutations", (3) "Natural selection=judgement of the experimenter". Although reasoning by analogy is one of the useful modes of constructive thinking, it becomes a two-edged sword if used unwisely. Since Turing's goal was to establish the possibility of constructing a thinking machine he could not claim that his third identification above supported his aim for, the latter presupposes the existence of a thinking machine. To see our point from another angle. It would have been as if Darwin had claimed that natural selection would have brought about humans! With respect to the omnipotence of formal reasoning Turing is less explicit yet © Petros A. M. Gelepithis, 1984

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still clear. According to Turing the problem is one of programming. Since there are mental functions that we can program there seems to be no reason to assume that there are mental functions which are not mechanical. It seems to follow that all mental functions are programmable, and Turing, accordingly, goes into some length to indicate ways that could help us in constructing the child machine. But, although one may accept the hypothesis that the human mind is deterministic, as we do, it does not imply that the human mind is programmable. Turing failed to notice this inconsequence and this is a second flaw in his reasoning. So, to sum up: Turing tried to establish two things in his 1950 paper. First, he replaced the mt question with his imitation game in the belief that the two were equivalent; and second, he tried to argue in favour of the AI problem. As we saw, Turing's test has been accepted to be inadequate even by proponents of his views for machine intelligence. On the other hand his reasoning for the possibility of thinking machines is nothing more than statements of his beliefs concerning the scope of programming and education. There is a final remark I wish to make before proceeding to consider the againstarguments. It concerns what I believe to have been Turing's root cause of his beliefs. It seems to me that Turing's fundamental assumptions concerning the mt question stemmed from a dualistic conception. The very nature of the imitation game is a clear indication supporting this view. As Turing himself puts it: "The new problem has the advantage of drawing a fairly sharp line between the physical and the intellectual capacities of a man." (1950 p 12). B. The against arguments The aim of this section is to summarize and discuss the various views against the possibility of intelligent machines. As we saw in section 2.2.A Turing attempted to establish two things. First, equivalence of his imitation game with the mt question; second, his reasoning for machine thinking. On the other hand, as we saw in the introduction, the mt question is closely related to both the mmm question and the mind-body debate. As a result, the opinions against Turing's argumentation have taken a number of forms and they have addressed more issues than the ones Turing himself did. In Table 4. we summarize all arguments, so far, against the belief for machine intelligence along with the issues their advocates actually addressed. © Petros A. M. Gelepithis, 1984

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To make Table 4. most clear we proceed now to elaborate or comment on some of the views summarized in it. We start with the refutation of the mathematical objection. To accept that discrete-state machines have limitations does not imply either that such machines are not intelligent or that humans are more intelligent than computers, (i.e., humans know things that machines can not). Table 4. Coded views opposing Turing's claim for machine intelligence. Name

T.t=mt Q

mt Q

Theological

No

Mathematical

No

Neurological

No

ESP Lovelace

No

Jefferson

No

Possible

Scriven

No

No

Polanyi Ziff

mmm Q

No

Possible

No No

Lucas

Possible

Dreyfus

m-b P

Qualified No No

Searle T. t = Turing test M = Mechanism Mt Q = Can machines think? Mmm Q = man-made-mind question m-b P = mind-body problem

Yes

M ‘refuted’

Argument Premises Immortal soul; God given Limitations of D-s Ms NS continuous; TM discrete ESP does exist Only what know how Selfconsciousness Semantic analysis Consciousness “Tacit knowing” Accepted Linguistic Cvs. Gödel’s theorem

Refutation Impossible

Human nature

No need

Counterexample. D-s = discrete state M(s) = Machine(s) TM = Turing machine Cvs = Conventions NS = Nervous system

Not applicable to all Ms Sufficiently approximable Tighten test up Programming open-ended No need No need Not specified Cvs change No need

No need

For, what a certain machine at a certain time may not know, another machine at some other time is possible to know. Or, in Turing's words: "In short, then, there might be men cleverer that any given machine, but then again there might be other machines again, and so on." (Turing 1950 p 22). Turing was also the first to consider what we have termed the neurological argument, that is, the belief that computers cannot be made to think because they are discrete-state machines whereas the human nervous system is continuous. This claim is both inadequate and misplaced. Inadequate, at least for those accepting Turing's test criterion of intelligence, because discrete-state machines can be sufficiently © Petros A. M. Gelepithis, 1984

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approximated by analog machines (e.g., differential analyzer), (Turing 1950 p 27). Misplaced, because proponents of the engineering goal of AI do not have to commit themselves to a position excluding the use of a certain type of machines, (e.g., analog), to achieve intelligence. For further comments and relevant references the reader is referred to Boden (1977). Probably the earliest remark against the feasibility of the AI project is Lovelace's (1842) conviction that: "The Analytical Engine has no pretensions to ORIGINATE anything. It can do whatever we KNOW HOW TO ORDER IT to perform." Considerable attention has been given to this remark but most of it focuses on sufficiently different variants of Lovelace's statement. For instance, Turing, (1950 p 27), argues against the "view that machines cannot give rise to surprises"; and Winston, (1977 p 252), finds uninteresting the fact that computers' "abilities descend from human programmers", because "it is equally true that humans are indebted to the genetic code." (ibid). There is still a number of arguments which do not need to be refuted as their proponents eventually accept, (e.g., Searle 1980), or do not exclude, (e.g., Jefferson 1949), the possibility of achieving machine intelligence. In this category Lucas's argument is probably best known in attempting to refute mechanism while, at the same time, embracing the possibility of man-made-mind. I wish now to conclude with a few general remarks. Probably the most striking feature revealed by this review is the fact that although all researchers of the AI problem, presumably addressed the same question, their answers show that they had indeed addressed different questions. (Compare, for instance, Jefferson, Polanyi and Lucas Table 4). Another point that should be made clear is that some answers in the negative are based on the PRESENT attempts to program intelligent computers or the CURRENT abilities

of computers. So, Dreyfus (1972 p 202) for instance, writes in his

"Conclusion": "Could we then program computers to behave like children and bootstrap their way to intelligence? This question takes us beyond present psychological understanding and present computer techniques. In this book I have only been concerned to argue that the current attempt to program computers with fully formed Athene-like intelligence runs into empirical difficulties and fundamental © Petros A. M. Gelepithis, 1984

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conceptual inconsistencies." And Searle, (1980 p 422), qualifies the type of machines we could not give the human characteristic, whatever it is. "I see no reason in principle why we couldn't give a machine the capacity to understand English or Chinese, since in an important sense our bodies with our brains are precisely such machines. But I do see very strong arguments for saying that we could not give such a thing to a machine where the operation of the machine is defined solely in terms of computational processes over formally defined elements; that is, where the operations of the machine is defined as an instantiation of a computer program." Finally, we should stress the fact that regardless of the differences distinguishing the various answers offered to the mt question they all have something in common: their quest, or taking for granted, for the human characteristic(s). Section 2.3 addresses the relevance of this quest to AI and takes issue on what should be the more appropriate question describing the AI problem. C. The `no-reason-why-not' belief In view of the inconclusive reasoning both for and against the possibility of constructing intelligent computers, (sections 2.2.A and 2.2.B), it seems that the most sincere and open-minded viewpoint to be adopted on the issue is that of neutrality. A view approximating neutrality has indeed been adopted by few scientists (e.g., Boden 1977; George 1979). We call this view ‘wishful optimism' and it may be summarized as the view allowing someone to act as though something is true or possible to happen because one can see no reason why not. In what follows we shall comment on the reasons which support the basis for wishful optimism, that is, the ‘no- reason-why-not' belief. As stimulus for our comments we take a skilfully written paper by George (1956). He made two main points: The first is a remark concerning the semantic difficulties the question is open to. The second is a powerful mixture of a claim and a remark both centered around the key issue of human constructability. In relation to the first point George writes (1956): “Now from our semantic point of view it is clear that the basic question that we set ourselves: "could machines think or not?" depends upon how broadly or how narrowly the two relevant terms "machines" and "thinking" are taken.” The second point is made explicit as he remarks: Our trouble intuitively is that we are © Petros A. M. Gelepithis, 1984

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continually being made aware that machines are constructed but forget that organisms are constructed also". And he furthermore claims that "the problem is not one that merits much discussion as the problem is empirical." Firstly on the semantic issue. Although I wholeheartingly agree that the question “Could machines think or not?” depends on how broadly the terms in this question are interpreted I nevertheless want to remark that the involved are not fictional their interpretation is bounded by the nature of their referents. To say it less vaguely, the number and kind of their possible interpretations are constrained by the possibilityboundaries set by the nature of their referents. On his second point concerning human constructability George remarks that: (i) what once has been done could be done again, and claims limited, if any, merit in discussing it on the grounds of being empirical. What is powerful about this remark is that it is partly correct. The false consequences may appear only after one makes the mistake of understanding different ways to obtain the implicit object of both "done" appeared in it. It may be argued that no-one can be so superficial as to overlook this left with the theoretical and empirical task of considering the possible differences between machine- and organism- construction. Our last comment concerns the allegedly ‘purely empirical' nature of the AI problem. I write ‘purely empirical’ for no one would ever claim, I believe, that the AI problem is not partly empirical. Actually most, if not all, problems are partly empirical; but, equally well, most, if not all problems have a theoretical part. And, the AI problem, in particular, admittedly does have a theoretical part since we: "must be careful to make it absolutely clear what we mean by a "machine" and what we mean by "think."" (George 1956 p 244). Of course, the problem may not merit much, or even any, discussion; but, it is doubtful that the reason for it is its empirical nature. On the other hand one may claim that the problem is worth of much discussion on the grounds of the amount of attention paid so far to it, or the consequences which its solution may have. For, in case the answer is yes: why have so many people have felt that machines cannot possess intelligence of their own? Is that feeling a remnant only of bygone beliefs, something analogous to the Ptolemaic belief to geocentricism? Or, the expression of that feeling is the overt manifestation of a deeper fear concerning the future of the human kind? A fear that, rather recently, has been nicely summarized by © Petros A. M. Gelepithis, 1984

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a scientist who agrees that machine intelligence is, in principle, attainable: "There have been many debates on "Computers and Mind." What I conclude here is that the relevant issues are neither technological nor even mathematical; they are ethical." (Weizenbaum 1976*1984 p 227). And, of course, there is always the case that the answer is, eventually, no. What, then, of the senior workers who had wholeheartingly believed in the attainability of the engineering goal of AI? Were they the victims of, merely, their own misjudgement? Or, were they misled by the plausibility of a paradigm -information processing approach- bloomed in a society that rates, first and utmost, the making of profit? And, finally, would such a possible glide justify the use of a language degrading humans to themselves and leading, eventually, to language -and therefore society- corruption? To avoid a possible misunderstanding we should say that these questions do not intend to mark beliefs or attitudes; I only made them to support the view that the AI problem is rather worth of discussion. 2.3. AI and the quest for the criterion of the mental Stemming from Turing's methodological shift in addressing his initial question "Can machines think?", a discussion of the philosophical notion of the criterion of the mental is outlined. As a result we point out that human language may be considered as the characteristic of the human species. We conclude that investigation into the nature of human understanding is a prerequisite to facing the mmc, (man-machine communication), question. After Wittgenstein's (1953*1976) conception of the existence of family resemblances among phenomena and Ryle's (1949*1983) attack of the "concept of mind" on similar lines, the number of philosophers looking for what is called, the criterion of the mental, i.e., whether there is something that all mental phenomena have in common, was reduced. Nevertheless the search whether there is something that could distinguish human beings from the rest of the nature in general and animals in particular never really stopped. Traditionally the mind is divided into three faculties: the cognitive, concerned with knowledge, the affective, concerned with feeling; and the volitional concerned with action. This division although no longer believed to reflect "the three so called


43

basic faculties that comprise the mind" (Shaffer 1974); it still plays a major role in determining the current philosophical conceptions in the West. Furthermore it serves as the philosophical background for the development of theories, models or just claims in other disciplines as for instance in AI. Currently the concepts most widely used as candidates fulfilling the criterion of the mental are thinking, (or/and intelligence), emotion, and intentionality. Concepts like purposeful behaviour, or consciousness seem to give way to more sophisticated conceptions as, for instance, the traditional concept of consciousness to Sperry's modified notion of it. Consciousness despite its lack of definiteness, or rather because of its blurred conception, conflating cognitive and affective aspects, gains impetus among its rivals. In AI the darling candidate is intelligence (e.g., Hayes 1978 p 295; Lenat 1978 p 259). Turing though he himself was not, at least explicitly, after the philosophers' quest for the human characteristic(s), he nevertheless took side on the dispute. In his 1950 paper one can easily recognize Turing's favourite: intelligent thinking. Now we can see clearly the relation of the strong AI claim to the quest for the criterion of the mental. Namely, its reliance on the assumption that thinking, (or intelligent thinking or equivalently still intelligence), is actually the human characteristic. The reason for choosing thinking among the several candidates, is mainly due, I believe, to its few, comparatively speaking, dualistic connotations. The question therefore raised is: are any of the proposed candidates adequate for being considered as the human characteristic? There are at least two reasons, each by itself sufficient, for rejecting all of the mentioned candidates: (i) Rudiments of the mental phenomena can be found in other animals besides human beings. (ii) Mental phenomena are inextricable from each other. We should emphasize at this point that we do not reject the existence of the mental phenomena; what we do reject is the passing over of single human abilities in isolation from each other as what could possibly characterize the human beings. Let me try to put it in another way. All of the human features that philosophers have put forward as candidates for the human characteristic are characterized by a more or less evident 'individualism'. That is, they all refer to some individual activity that is claimed to characterize the human mind. We can safely conclude that the question “Can machines THINK?” does miss the © Petros A. M. Gelepithis, 1984

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point. For, a necessary condition that a human feature has to fulfil in order to be considered as a candidate for being the human characteristic is to take into account, in some way, the influence that society has exerted on the nature of the human species. There is, I believe, no philosopher, scientist, or layperson that would sensibly deny that humans, whatever they are, they are "social animals" too. It follows that if one wishes to keep pursuing the quest for the human characteristic the societal component of the human nature have to be included somehow. In particular the strong AI claim has to be recast in similar terms if one wishes to claim an essential similarity between machines and humans. As a point of fact such a recasting did not happen. Moreover, a second assumption was taken for granted by workers in AI and a lot of work was started aiming to implement its implicit goals. It is interesting to note that Turing (1950) had already made, even if not entirely explicitly, the same assumption. It concerns one of his fundamental beliefs namely, his hypothesis that machines can be made to understand human language. This hypothesis is clearly manifested in the nature of the imitation game as a question-answering game as well as in his further assumption of the possibility of teaching a discrete state machine. It will be illuminating at this point to make a digression to see whether philosophers had considered understanding or human language as candidates for the human characteristic. Although a number of mental phenomena and particularly those more closely related with what is called thought had considered to be strong candidates for the sought characteristic, human language and/or understanding have not been considered as possible candidates by philosophers. This is even more surprising in view of the close connections of both language and understanding to thought. In the case of language this fact is particularly acknowledged by the special concern people have assigned to the language-thought relationship from the very beginning of their reflections on language; a deep concern which is still going on in the form of the so called linguistic relativity debate. So far what is more akin to a claim that human language is the human characteristic is the Whorfian hypothesis i.e., the thesis that language determines perception and thought. (Whorf 1940*1979). Moreover, recent biological (e.g., Lennenberg 1967) and linguistic research (e.g., Chomsky and his followers in many books and papers), emphatically claim the central and deep rooted character of human © Petros A. M. Gelepithis, 1984

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language. Assertions like: "Language is the manifestation of species-specific cognitive propensities." (Lennenberg 1967), are currently accepted by almost all workers in Linguistics and ‘influenced-by’ disciplines. Nevertheless what we are concerned with here is not the establishment of either human language or human understanding as the human characteristic although we do believe that human language would be a very promising candidate indeed. I want to notice in passing that human language being a successful candidate would not imply analytic philosophy's position, namely, human language is the yardstick for measuring all things. Our concern here is to justify the focussing of our attention on investigating the validity of the assumption that machines can, in principle, understand humans instead of arguing on the possibility of (intelligent) machine thinking.

We have two reasons. The first is that the assumption of

'understanding machines' is stronger than the assumption of 'thinking machines'. This is so because understanding, whatever it may be, implies thinking but not vice versa. That is, whenever one understands something or somebody thinking is always involved; whereas, while a person thinks this does not (necessarily) imply that he or she understands too. Obviously this reasoning is entirely based on the implicit definitions of the words understanding and thinking and therefore we should stress that they were used above in their common sense of everyday English. (see also section 4.1). Secondly, as we remarked a few paragraphs above in this section, all candidates for the criterion of the mental belong to the 'separating class' type, a fact that makes them unsuitable for being considered as the human characteristic. Understanding on the other hand has a feature, (understanding somebody), that no other candidate shares. In other words ‘understanding somebody' brings in a social element that all other candidates lack.To avoid misunderstanding we are not saying that no other candidate (e.g. thinking) is not influenced by social factors or even more that philosophers believe so. All we say is that though thinking, for instance, is societally influenced it is as a matter of its nature a strictly private process whereas understanding may well require interaction in order to be reached. Still we should stress here that we do not consider understanding as THE human characteristic. We only wish to make the point that understanding possesses a societal feature which any candidate for the criterion of the mental must possess and no candidate so far proposed has had. © Petros A. M. Gelepithis, 1984

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We believe that these two reasons form an adequate justification for focusing our research on the possibility and nature of machine understanding instead of merely questioning the possibility of machine intelligence. Chapter three provides the specific basis, (for the general framework see chapter one), which our analysis and prospects, in chapters four and five respectively, draw upon.


47

3. Understanding and Meaning: the Foundations of Human Cognition Usually, understanding and meaning are not treated under the same heading. Moreover, however central or significant their studies are considered to be in many disciplines (e.g., philosophy of mind, psychology, etc.), they are usually not referred to as the foundations of human cognition. It seems in order, therefore, to give our reasons for this chapter's title. It reads as follows: Since, it may be reasonably claimed that human cognition consists of the interrelated phenomena of learning, thinking, language, and memory; and since, furthermore, understanding or meaning play a role in each and every of them, we think we are justified to conclude that through investigating understanding and meaning one investigates the foundations of human cognition. We saw in the previous chapter that to investigate the validity of one of the fundamental assumptions of AI –that machines can in principle understand human language (MUHL)- one is led to investigate the nature of human understanding and, consequently, the nature of meaning. To pave the way for our analysis of the phenomenon of human understanding we review, here, the necessary theoretical literature on meaning and human understanding. What we mean is that as there are many theoretical questions which may be asked in either the study of meaning or human understanding, we shall confine our attention to those two which are necessary for the investigation of our aim as outlined in the introduction. Namely, what is human understanding? And, what is the nature of meaning? One thing that may strike the reader in our review of these two questions is the considerable difference in the type and extent of work that has been done on each one of them. This difference reflects the state of the theoretical research on meaning and understanding. The respective amount of research work done on them may well be said to be at loggerheads in the two-dimensional continuum of theoretical and experimental work. Schema 1. may serve best to illustrate this point.


48

T HE

*M

OR

M= Work on meaning

Y

U- work on understanding

Y

*U

EXPERIMENT

Schema 1: Comparison of theoretical and experimental work on meaning and understanding. The result is that whereas in the study of meaning there are many well established ‘theories' of meaning, the theoretical study of understanding has not reached similar achievements. Positively speaking, what we have in the theoretical study of understanding are rather isolated attempts, stemming from particular schools of thought, (e.g., behaviourism), which have not reached the status of ‘theories' in the sense of those in the study of meaning. A plausible reason to explain the slim experimental concern for meaning, compared to the almost stunning interest for its theoretical aspects, may well be explained by the fact that it has been found rather difficult to related meaning to the overt human behaviour, save, of course, committed theoretical frameworks like behaviourism. Whereas in the case of the, by far, dominance of experimental work in understanding, the reason may be that human understanding is rather easily related to phenomena concerning overt human behaviour. 3.1. Conceptions of human understanding The philosophical underpinnings of human understanding can be traced back at least as far as the times of Plato and Aristotle and their attempts to provide an account for the human mind. More recently, Locke (1690*1975) and Hume (1758*1977) have written treatises on human understanding but their views on the scope of understanding was much broader than that of today’s workers in the field of human cognition, and more in tune with Plato and Aristotle. Actually, for both of them human understanding was essentially taken to be coextensive with the functioning of the human mind. However significant their views may be, and in many occasions they are, we shall not be concerned here with their analyses of the human mind. We shall © Petros A. M. Gelepithis, 1984

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focus instead on the contemporary view of understanding as one of the phenomena of the human mind. In other words, our concern here is not with the human mind as a whole but only with one of its functions; namely, understanding. Questions, both theoretical and experimental ones, are always discussed, consciously or unconsciously, in some conceptual framework. In the study of understanding one may distinguish two schools of thought: the analytic (or propositional), and the hermeneutic (or non-propositional).

A.

The Analytic Tradition There are, currently, three approaches to understanding in the analytic tradition (see Luria 1982). First, the semasiosyntactic approach, that is, the belief that to understand a sentence in a language it is adequate to understand the meanings of the words comprising the sentence and know the grammatical rules of that language. Second, what we may call the contextual approach to understanding a sentence, that is, the belief that the semasiosyntactic approach though necessary is not adequate for understanding, and what is needed besides word meaning and grammatical rules is the context in which a sentence occurs; this context being either linguistic or extralinguistic (situational). Finally, the motivational approach goes even further demanding knowledge of the motivation behind the utterance of a sentence, besides its context, for real understanding to occur. As it may be seen readily from the above juxtaposition the three approaches do not contradict each other; actually the one is contained, so to speak, in the other, starting from the semasiosyntactic and moving up to the motivational. It follows that a choice between them can only be made in terms of methodology since in principle everyone would agree that, according to the description given above, the motivational approach is, as we understand the workings of the human mind today, the least incomplete approach to human ‘understanding’. So, it comes as no surprise that the majority of workers prefer to adopt the contextual approach to human ‘understanding’ which is both less complicated than the motivational, so that they can reasonably hope to tackle the problems involved, and at the same time it is not so clearly inadequate as the semasiosyntactic one. Although this account does summarize the current state of the art in the Analytic tradition, it does not show the underlying trends in it, which eventually shape the aforementioned approaches. In what follows we shall just give instances characteristic of these underlying trends. They are two: the first, which we shall call 'the formal', places emphasis on knowing the rules; in other words, on know-how or syntax. The second, which we shall call 'the semantic' emphasizes the role of meaning in the © Petros A. M. Gelepithis, 1984

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treatment of understanding. Probably the clearest, and certainly the most influential, currently, formalistic approach to understanding is exemplified by workers in AI. Their response towards the phenomenon of human understanding can be best seen through their attempts to build computers to ‘understand’ human language. Although understanding proved to be a necessary condition to machine translation (Michie 1974 p 159), the nature of understanding was not tackled. Furthermore although most of the workers in AI accept or consider the necessity of understanding in intelligent behaviour, they either restrict themselves to some of the end results of this activity, what is commonly called knowledge, or consider it in passing (McCarthy and Hayes 1969; McCarthy 1979). In the first case they create a variety of knowledge-based systems (Winograd 1972; Buchanan et al 1969) in the second they may create wrong impressions and certainly contribute nothing to the phenomenon of human understanding. On the positive side of the research on the understanding of Natural/spoken language, the various knowledge-based systems and their corresponding worlds (e.g., children-stories world) on which they are based on, clearly showed on thing: the dependence of understanding on context (reference frame). Of course, this fact although a necessary requirement for someone to understand something fails to characterize understanding. In summary, we may say that this approach of AI workers in the area of human understanding has been ad hoc rather than systematic and comprehensive. Nevertheless, they have greatly contributed to the widespread adoption of what may be called formal approach to human understanding. A clear example of this approach can be seen in one of the best introductory textbooks in the field of AI. In "The Thinking Computer: mind inside matter" we read: "Note that, as we come to understand a process, its informality disappears. In fact, we might define understanding as the ability to reexpress some concept in terms of concrete mathematical expressions or computer algorithms." (Raphael 1976 pp 140-141). To be sure the formal approach to human understanding has not come with the advent of AI. Wittgenstein, for one, had explicitly propounded such a view before. In his "Philosophical Investigations" he writes: "To understand a sentence means to understand a language. To understand a language means to be a master of a technique." (Wittgenstein 1953 par 199). His approach to understanding is more concretely presented a few pages before in the same book. © Petros A. M. Gelepithis, 1984

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"Let us imagine the following example: A writes series of numbers down; B watches him and tries to find a law for the sequence of numbers; if he succeeds he exclaims: "Now I can go on!"---So this capacity, this understanding, is something that makes its appearance in a moment". (ibid p 59e). This conception of understanding, as a mastery of ways to do things, becomes even more striking in Ryle's philosophy of mind; He writes: "Understanding is a part of knowing HOW. The knowledge that is required for understanding intelligent performances of a specific kind is some degree of competence in performances of that kind." (Ryle 1949 p 53). Finally a, rather unexpected, forerunner of the formal approach to the human mind can be seen in the writings of one of the leading physicists of our days. In considering the meaning of 'fundamental physics', Feynman adopts essentially the same approach to understanding though in slightly more cautious terms. “What do we mean by "understanding" something? We can imagine that this complicated array of moving things constitutes the "world" is something like a great chess game being played by the gods, and we are observers of the game. We do not know what the rules of the game are; all we are allowed to do is to watch the playing. Of course, if we watch long enough, we may eventually catch on to a few of the rules. The rules of the game are what we mean by Fundamental Physics. Even if we knew every rule, however, we might not be able to understand why a particular move is made in the game, merely because it is too complicated and our minds are limited. If you play chess you must know that it is easy to learn all the rules, and yet it is often very hard to select the best move or to understand why a player moves as he does. So it is in nature, only much more so; but we may be able tat least to find all the rules. Actually, we do not have all the rules now. (Every once in a while something like castling is going on that we still do not understand.) Aside from not knowing all of the rules, what we really can explain in terms of those rules is very limited, because almost all situations are so enormously complicated that we cannot follow the plays of the game using the rules, much less what is going to happen next. We must, therefore, limit ourselves to the more basic question of the rules of the game. If we know the rules, we consider that we "understand" the world." (Feynman et al. 1963 vol. I, p 2-1). The heavy emphasis on meaning, which characterizes the semantic trend in the Analytic tradition, becomes at times so great as to eclipse nearly all discussion of understanding as such in favour of meaning (see, for instance, Bransford and McCarell 1972). A second characteristic of the semantic approach is that the majority of workers, even when they do concentrate on understanding rather than on meaning or knowledge, they do investigate human understanding in a particular domain, e.g., © Petros A. M. Gelepithis, 1984

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problem solving, rather than as a phenomenon of its own. (see, for instance, Greeno 1977; Anderson JR 1980). The result seems to be, more or less, influenced by the particular domain under consideration as is the case with Anderson in writing on "Language comprehension". Comprehension can be analyzed into three stages: PERCEPTION, PARSING, and UTILIZATION. Parsing is translation from the word representation to a meaning representation. Utilization is the use to which the comprehender puts the meaning of the message." (Anderson JR 1980 p 400). Of course there are always attempts to account for understanding as a phenomenon of its own. Franks, for one, writes in his "Toward Understanding Understanding": “"Understanding" is a function of the extent to which adequate (coherent, complete) meanings have been generated in a particular context." Whereas, ""Meaning" refers to relations activated or generated as a function of knowledge relations and particular environmental context."; and ""Knowledge" refers essentially to static, semi-permanent long-term memory relationships.” (Franks JJ 1972 p 232). Obviously the main inadequacy of this conception of understanding is its lack of providing specific attributes to its key terms 'meaning' and 'adequate'. An apparently more elaborate but essentially equally inadequate view of understanding is developed by Greeno (1977). In an attempt to give central importance to understanding in the domain of problem solving he develops a conception of (human) understanding closely related to that of Schank and Winograd. In general, Greeno views understanding as the process of constructing a pattern of conceptual relations for those concepts mentioned in the object to be understood. In his words: "When a sentence is understood, its internal representation shows what the sentence means. The meaning corresponds to a pattern of relations among concepts that are mentioned in the sentence, and understanding is the act of constructing such a pattern." (Greeno JG 1977 p 44). There are a number of further refinements and remarks on this view but no elaboration on his central position concerning understanding. As a result we shall not further be concerned with these in the present work. Finally, I want to shortly comment on two particular conceptions concerning understanding which do not neatly fall in either of the formal or semantic trends. The first is due to Gordon Pask, (1976), the second to Paul Ziff (1972). Pask's view concerns a particular kind of understanding, the one that may take place when a person A1 converses with a person A2 and is a small part of a lengthy,


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both in time and volume of cognitive processes. The view in rough outline is as follows. Two participants A1 and A2 will be said to understand a particular topic T, in a given domain, whenever the following conditions are met: (i) Either participant explains T; (ii) Either participant explains how he constructed and reconstructed the concept responsible for T; (iii) The explanations in (i) and (ii) above are agreed between A1 and A2; (iv) An explanation is agreed between A1 and A2, iff, it reproduces in one of the two participants a concept equivalent to that initially produced by the other participant. We think the conditions are correct but inadequate. Specifically: (i) to having reached a consensus (agreed explanations) does not imply to having reached understanding. Unforced and well intentioned persuasion, for instance, has hardly been conceived as either implying or presupposing understanding. (ii) the scope of understanding is broader than what these conditions demarcate it to be. Individual understanding of a theory, for instance, let alone understanding of its wrongness, although it involves and presupposes reconstruction, it does also demand conceptual reference, well beyond agreed explanations, to common beliefs and experiences. According to Ziff (1972), the fundamental problem in understanding is how one understands what is said. To answer this Ziff, first, slightly shifts focus to consider other views and then refocuses to state his own viewpoint. The first question Ziff considers, springs out of the following situation: let there be two persons, A and B, both who have heard something but only A has understood it. The question is: what is the difference between A and B such that only A has understood? The three different types of answer Ziff considers, can be summarized as follows: Answer-1: The difference is in actual or potential overt behaviour. Answer-2: The difference is in the ability of making an inference from what understood. Answer-3: The difference lies in the possibility of providing paraphrases. Having refuted all three answers, Ziff finally claims that "understanding is essentially a matter of analytical data processing of some sort." There are three points concerning Ziff's analysis of understanding; the one is in favour of his analysis, the other two rather against it. In favour of Ziff's refutation of answers A1-A3, is the fact he cleared some of the way towards understanding understanding from a considerable amount of irrelevant information. The second and third remarks refer to Ziff's claim and belief, supporting this


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claim, respectively. The point against his claim is that it is too general to be useful. He rightly points to the analytical nature of understanding but fails to pursue further his investigation in the direction pinpointed by analyticity itself. The barrier to his research seems to have been set by his own beliefs: "only that which is composite, complex, and thus capable of analysis, is capable of being understood." (Ziff, p 19). It would have come as a surprise to me to find out that Ziff is not able to understand the statement 'it is raining' without analysing it. B. The Hermeneutic School of Thought Strictly speaking the contents of this subsection should be confined to the review of hermeneutics in its relation to the nature of understanding; (see introduction to this chapter). Nevertheless, and in anticipation of an assessment of our work in the epilogue to this thesis, we shall briefly outline two more things: one, the scope of hermeneutics; two, its influence on the human (social) sciences. We start, therefore, with a short note on its historical development. Hermeneutics was originally developed to answer the question of authenticity of ancient texts which could only be available, at the times before Gutenberg, through the work, with their inevitable mistakes, of a long series of copyists. This aim -to recover the true meaning of a text- was carried out, in early hermeneutics, by employing merely philological methods. This coupling nevertheless, broke down by the end of the 18th century. In the Romantic and Kantian climate of the late 18th and early 19th century the hermeneutical methodology changed dramatically. Philology ceased to be the sole methodological framework for hermeneutics. The text in isolation from its cultural and individual cradle ceased to be seen as an adequate source for its interpretation. As Bauman, more generally, remarks: "A written text, a work of art in the town square on in the museum, a code of law or a ritual - unlike direct speech - do present the problem of understanding-through-interpretation, because they have lost the original link with life which accorded them meaning." (Bauman 1978 p 30). The Kantian insistence on the priority of cognition and the Romantic emphasis on particularity and subjectivity open the way up for psychological considerations in the quest for the authentic meaning of a text. The second important change in the conception of hermeneutics concerned its scope. It enlarged in two stages: The first occurred with Dilthey, the main founder of hermeneutics, in the early 19th century. According to him, the main task of hermeneutics is: © Petros A. M. Gelepithis, 1984

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"to counteract the constant irruption of romantic whim and sceptical subjectivity into the realm of history by laying the historical foundations of valid interpretation on which all certainty in history rests." (Dilthey 1900 p 260). The second came naturally as the result of developing and establishing the field. Hermeneutics now, claims as, or does not exclude from, its subject matter the whole gamut of products of human thought. As an illustration we can contradistinguish the 'specificity' of a well-known worker in the field, "hermeneutics is the theory of the operations of understanding in their relation to the interpretation of texts." (Ricoeur ?1981 p 43). Against the loose descriptions of workers in affected disciplines: "[hermeneutics is] the theory, art or skill of interpreting and understanding the products of human consciousness". (Checkland 1981 p 276). "[hermeneutics] has come to encompass not only the interpretation of language, but also the larger understanding of how we interpret the world in which we live." (Winograd 1980 p 223). The influence of hermeneutics on the human sciences was due to the fact that it established the claim that the social sciences should necessarily take into account the subjective factor in the study of the social phenomena. To be sure such a claim had been indicated quite some time before the Romantic movement by thinkers like the Italian Vico (1668-1744). But it was with hermeneutics that such indications got established. So, Dilthey's work reflects most clearly the opposition between the natural and human sciences. He, furthermore, codifies this opposition in the meaning of the words ‘explanation' and 'understanding' respectively. It was, therefore, this introduction of understanding, and consequently meaning, in the interpretation of human history that constitutes the main challenge against the program of a united science. Within this framework it is probably easier for one not to be surprised by the very little concern of hermeneutics to the question of the nature of understanding. Understanding is seen either as a primitive notion, see previous quotations on the scope of hermeneutics, or at most as a methodological tool: "Understanding is the process of recognizing a mental state from a sense-given sign by which it expressed." (Dilthey 1900*1976 p 248). © Petros A. M. Gelepithis, 1984

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Actually the only work, to our knowledge, that deals with understanding itself, in the hermeneutic tradition, is that of Moravcsik (1979). In a well written paper he tries to establish that certain kind of understanding, namely, "understanding proofs", involves a non-propositional ingredient which is necessary for the complete understanding of a proof. This non-propositional element which is postulated to be a mental state- "the state of understanding"- is according to Moravcsik, "the underlying factor responsible for the varieties of know-how and propositional knowledge". (ibid p 207). The nature of this underlying factor is sketched more fully in the definition of understanding that follows: "Understanding, then, is the state of mind in which a conception of the proof ... yields the insight that unites our knowledge of the relevant rules, our abilities of application, etc." (ibid). Probably the weakest point in Moravcsik's analysis is his postulation of understanding as a state. The strongest support for this postulation comes from his assumption, (ibid pp 202-203), that understanding can be described in terms of knowledge, though, of course, not of the kind that the Analytic tradition has attempted to describe. The first argument one may think of, against such an assumption, is that it violates our established linguistic conventions concerning understanding. According to these conventions understanding is conceived as a process rather than as a state. Consider, for instance, what "The Oxford English Dictionary on Historical Principles" writes on understanding. "(Without article). Power or ability to understand; intellect, intelligence. With THE: The faculty of understanding and reasoning; the intellect." (ibid vol. X, part I, p 148). But the key point against such an analysis is that it is very little informative and "short-sighted". What I mean is that such an analysis does not tell us how, for example, the claimed insight is obtained or why understanding should be described in terms of knowledge despite our established views. 3.2 Review of theories of meaning © Petros A. M. Gelepithis, 1984

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In the introduction to this chapter we noticed that investigation of the theoretical question of the nature of understanding leads one to investigate the corresponding question of meaning. To initiate proceedings our survey in this section is offered. It covers eight theories of meaning (TsM) which have been classified in four categories: philosophical, linguistic, formal, and biological (Table 5 p 57). A word of justification for this classification scheme is in order here. In general classifications of theories of meaning either exclude certain views (Britannica for instance, does not include the AI approach to meaning), or arbitrarily subsume some theoretical conceptions of meaning under hardly related headings. On the other hand it is a fact that there is no unique placement (i.e., classification) of semantics on a general map of human knowledge in terms of the ontological commitments underlying each theory. This is of course entirely justified in view of the further fact that the question 'what is meaning?' is equally unsettled. Table 5. Summary of key advantages and disadvantages of the main theories of meaning. Name RTM

Nature of Meaning The L-W link.

ITM UTM

Intended audience Use

LTM

Purely linguistic

TTM

Knowledge of truth conditions

AITM

Procedure

IdTM

Encodes

BTM

Function of responses

effect

TM = ‘Theory’ of meaning. RTM = Referential TM ITM = Intentional TM UTM = Use TM LTM = Linguistic TM TTM = Truth TM AITM= Artificial Intelligence TM IDTM= Ideational TM BTM = Behavioural TM

Main advantage Pinpointed the relation of L to W Distinguishes between linguistic and non-linguistic meaning Recognizes the effect of context on meaning. Inclusion of syntax to account for meaning (i) Pinpoints the relation of T to W (ii)Succinct representations (i) as with UTM (ii)Succinct representations Considers the close connection of L to T. Brings in our relations to things

Main disadvantage Too narrow; cannot account for, e.g., indexicals. Inability to combine personal with public meanings. Ignores underlying mechanisms. Excludes context and the world Cannot account for non-truth expressions. Cannot account for nonexecutable expressions. Cannot account for the abstraction probability Does not recognise perceptual effect on meaning. W = World T = Thought L = Language

On these grounds I think that a classification scheme which considers all different


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theories of meaning and classifies them according to their answer to the fundamental ontological question concerning the nature of meaning may well be found useful and convenient. Another point I want here to notice is that certain conceptions concerning meaning have not been included in this survey. Concretely we have excluded physicalism and Peirce's and Quine's views. These omissions may be justified on the following grounds. Physicalism was felt to add nothing in our survey since the truth conditions theory of meaning is included. Peirce's (1839-1914) views on meaning are not included simply because we believe that they are more coherently presented by other investigators in theories of meaning that we certainly have included (e.g. behavioural). Finally the reason for the exclusion of Quine's views on meaning is that either his arguments are actually for a reference theory itself rather than a referential theory of meaning, or against the possibility of constructing a TM in the first place. (See, for instance, Quine 1951*1980; Quine 1975). Concluding I want to notice two points concerning the kind of our survey. First, the presentation of each theory is essentially composed of: (a) the fundamental thesis or theses of a 'theory', (b) the fundamental objections to it, and (c) the essential, if any, rejoinders

to

each

theory. We subsequently exclude all ramifications or

technical additions to a TM and include only, if necessary or illuminating, specific examples of a theory. Second, that questions external to the theories reviewed will not be addressed in this section. In particular our survey will presuppose what philosophers do namely that: (i) A theory of meaning is needed. (ii) Questions philosophers have formulated do reflect problems which are not necessarily imaginary constructs. 3.2.1. Philosophical theories of meaning

A. The referential theory of meaning (RTM) The fundamental conception of the RTM is the alleged tie between words and things. This basic idea has taken two forms. According to the first one the meaning of a word is to be identified with what the word points to (Alston 1967; Rosenberg and Travis 1971). In the second variation the meaning of a word is to be identified with the relation between the word and its referent (Alston 1967). © Petros A. M. Gelepithis, 1984

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There are two sorts of linguistic expressions which are particularly suitable for exemplifying the fundamental idea of the RTM: the proper name and the singular (known also as definite) description. They both refer to individuals and are collectively known as singular terms. So the meaning of the expression 'my sister' is what it refers to, my sister, and the meaning of Peking is what it refers to, that is, the city of Peking. Of course the RTM is not restricted to singular terms (words), general terms, that is, words denoting groups of things provide a second source of examples. So for instance, the meaning of 'longer' is the set of all pairs of which the first element is longer than the second, and the meaning of 'red' is the set of red objects. There are three main, and interrelated, objections to the RTM: a) lack of reference; b) same denotation yet different meaning; and c) ignoring intension. We shall very briefly exemplify objection a), for b) is an extension of a) and c) an equivalent way of stating objection b) (Richards 1978 p 74). In identifying the meaning of words (singular or general) with what they refer to, or denote, the RTM runs into two sorts of troubles. In the first case, identifying singular words with their referents, one is led to accept that words like Kerveros (the ancient dog-guardian of Athis) are meaningless. In the second case, identifying words with their extension (N2) one is forced to conclude that words like 'goblin’ and 'nymph' have the same meaning since their denotation is the empty set. There is a number of reasons why workers in semantics have not abandoned the RTM in face of such difficulties.

Richards (1978 p 76) neatly states two such

reasons: "[o]ne reason is that they find the RTM as symbolic logicians have developed it, so much more "scientifically" acceptable due to its scope and mathematical rigour. ... Another reason is that meaning understood as intension, is identified with the properties a thing must have for the word to apply to it. And they deny there are, in the world, any such things as properties. They claim, reasonably enough, that the world consists of just individuals: green individuals, spherical individuals, plastic individuals. It does not also contain the properties greeness, sphericity, plasticity. So, there are no properties, hence no intensions. And if our theories of language suggest there are intensions, well we had better recast our theories." Probably the best way to meet objections against denotation is Russell's theory of singular descriptions (Russell 1905*1969; 1919*1971). Consider for instance the expression, l, 'the present king of Greece'. This expression lacks a referent yet contrary to the RTM it is meaningful. Russell circumvents this and similar problems © Petros A. M. Gelepithis, 1984

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(e.g. Frege's classical example) with his conception of existential propositions, that is, propositions without definite reference. So in the above instance the expression l is equivalent to 'there is at least, and at most, one person that reigns over Greece, and whoever reigns over Greece is king'. Furthermore to tackle questions like: (i) What is the meaning of a sentence (statemental or not), and (ii) What is the meaning of words like "I"

"this",

"yesterday" etc, variously known as indexicals, indicators, egocentric particulars or token reflexives (N3), RTM developed a highly sophisticated calculus. The methods involved are usually of two sorts predication (linking general terms (words) to singular ones to give a simple sentence) or/and quantification. The basic idea behind the referential calculus is the common sense idea that the meaning of a sentence is concocted out of the meaning of its constituent words. We may conclude now with the fundamental pros and cons of the RTM. The fundamental point for a RTM is based on the observation that language is used to talk about the things around us. Unfortunately, the implications of this valid point are extended beyond their valid range as the difficulties with indexicals, syncategorematic words and non-statemental sentences show. B. The intentional theory of meaning (ITM) One of the most recent conceptual schemes intended to give an account of what meaning is is that proposed by H.P. Grice. There are considered to be at least two reasons for bringing in intention: (i) Sentence ambiguity (I went to the bank yesterday) (ii) Desire to distinguish linguistic acts from non-linguistic acts. It is usually the case that an analysis of the concept of linguistic meaning is carried through in terms of the notion of communication whereas, furthermore, the concept of rule (semantic or linguistic) plays a fundamental role (Harrison 1979). Nevertheless this tendency is not the only defensible thesis and Grice made it explicit by analysing the concept of linguistic meaning (what he calls meaningNN) in terms of the speaker and the particular utterances he or she utters. In a series of articles, (1957, 1968, 1969), Grice developed a theory aiming at showing that what an utterance means can be explained in terms of what effect the speaker intends to create in his/her audience. To this end Grice developed a calculus along the following lines. He essentially equates M(u) with Ei whereas, Ei stands for © Petros A. M. Gelepithis, 1984

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the effect the speaker of an utterance u intends to produce on his/her audience A, and M(u) stands for the meaning of u; (See for instance Fodor JD 1977; Harrison 1979: Richards 1978). In one of his most simply worded definitions Grice writes: ""U meant something by uttering x" is true iff, for some audience A, U uttered x intending (1) A to produce a particular response r (2) A to think (recognize) that U intends (1) (3) A to fulfil (1) on the basis of his fulfilment of (2)." (Grice 1969). The objections against ITM can be easily distinguished into two sorts: fundamental and state-of-the-art ones. In the latter case we have a particular type of inadequacy and numerous counterexamples. ITM's inadequacy concerns the meaning of words. It seems for instance, difficult to specify the intention of the word mountain and the difficulty is not raised by considering words exclusively as parts of sentences (or utterances). For what could, for example, be the intention of a speaker uttering 'my village is far from the sea'? Concerning counterexamples on the other hand one notices that many of them (e.g. Searle 1969; Ziff 1967*1971) work because what the utterer may mean, and what his or her utterance means, can vary independently of each other. This observation moreover makes explicit one of the fundamental objections to ITM. As George (1981 p 219) puts it: "the difficulty remaining at the heart of the Gricean conditions is that A knowing B's intentions is not necessarily the same as knowing what is meant by x." The second fundamental, and actually external, objection to ITM concerns the theory's basic notion of intention. It can be argued by materialists (e.g. behaviorists) that even if ITM is true it must follow, in some way or another, from a materialistic account of meaning for otherwise it would have been at loggerheads with the whole scientific edifice of our days. To summarize it is widely accepted that Grice's most characteristic advantage is that it allows one to distinguish between natural and non-natural meaning (see nevertheless 3.2.2). On the negative side the problems of Grice's theory stem from its inability to combine personal meanings with public ones. As Harrison (1979 p. 186) expresses the root cause for this inability: "it [ITM] connects the concept of meaning ESSENTIALLY with the concept of 'intention to communicate', abandoning the Fregian principle that the meaning of an expression in a language must be independent of the psychological states of particular speakers." © Petros A. M. Gelepithis, 1984

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C. The use theory of meaning (UTM) Wittgenstein may be arguably claimed to be the originator of the UTM. Although it is true that it was the latter Wittgenstein, that is the Wittgenstein who wrote the "Philosophical Investigations", who identified the meaning of a word with its use one can nevertheless see the sperm of such a conception in his "Tractatus Logicus Philosophicus": "Frege says that any legitimately constructed proposition must have a sense. And I say that any possible proposition is legitimately constructed, and, if it has no sense, that can only be because we have failed to give a MEANING to some of its constituents." (ibid p 97). A further point concerning Wittgenstein's conception of the meaning of a word as identical with its use is his supported belief that such an identification does not hold true for all cases. In his definition of meaning (Wittgenstein 1953*1976 p 20e) this is explicitly stated: "For a LARGE class of cases- though not for all- on which we employ the word "meaning" it can be defined thus: the meaning of a word is its use in the language". As an example justifying the 'non-absuluteness' of the above term one may quote: "when longing makes me cry "Oh, if only he would come!" the feeling gives the words "meaning". But does it give the individual words their meanings?" (Wittgenstein 1953*1976 in par. 544). There are three conceptions which give to UTM its characteristic flavour. The preeminent role of the rules of use of an expression; the abstract character of the bearer of meaning; and the conception of the sentence as the unit of meaning. A recent clear exposition of the first conception can be seen in Quinton (1977). "For a person to know the meaning of a word is for him to know the rules of its use; for a word to have a meaning is for these to be, among some groups of speakers, a practice of using it in accordance with a set of rules." And again more succinctly: "The meaning of an expression is the rules which determine its use in discourse." These rules, for Quinton, are of two sorts: semantic and syntactic rules. The former connect meaning to things, properties, states of affairs etc; the latter govern its possibilities of combination with, and its logical relations to, other expressions. The formalistic element of the UTM is explicitly stated by a leading contemporary proponent of this theory. "It should be remembered that the bearer of meaning, be it a word, phrase, or sentence, is a relatively abstract entity." (Alston 1967 p. 238). This conception is subsequently justified as follows: "A word is a © Petros A. M. Gelepithis, 1984

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pattern of temporally ordered sound types to which particular soundings may more or less approximate. Hence, meaning does not attach to particular activities, sounds, marks on paper, or anything else with a definite spatiotemporal locus." Finally the question of the semantic unit for a language is answered remarkably baldly in the context of the UTM. "the sentence is the smallest linguistic unit with which a complete action can be performed." (ibid). On the three fundamental conceptions outlined above the current UTM is being built. The main particular building block is the notion of illocutionary act (or illocution) (N4). Alston again gives a tentative account of the sentence meaning in terms of Austin's conception of "speech acts": "As a first step we can say that for a sentence to have a certain meaning is for that sentence to be used to perform a certain illocutionary act." (ibid). Objections to UTM vary from those pinpointed by the proponents of the theory themselves (e.g. inability of UTM to give an account of word meaning) to those raised as a result of UTM notions (e.g. sentence as the unit of meaning) coming at loggerheads with established results in linguistics

(word and more precisely

morpheme is the unit of meaning). A second disadvantage of UTM is the lack of convincing examples to illustrate the theory in clear contradistinction with the powerful slogans used to support it. ("Don't look for the meaning, look for the use" or "Look at the sentence as an instrument, and at its sense as its employment."). Probably the most compelling reason for UTM is an alloy of correct observation concerning human language and its admittance of some of the deficiencies of rival TsM. This reason is succinctly stated in Vendler's article in the Encyclopaedia Britannica. "[UTM] admits that not all words refer to something, and not all utterances are true or false. What is common to all words and all sentences, without exception, is that people use them in speech. Consequently their meaning may be nothing more than the restrictions, rules, and regularities that govern their employment." It is this reasoning behind the next move of the proponents of the UTM. Namely, the focusing of their attention on the behaviour of the speaker as the place to look for the use of language. This latter move seems to lead one inexorably to relate the meaning, of words say to the use of language. The difficulties arise when one tries to specify this relation by, for instance, identifying word meaning with its use in language. For use is too vague a word to be of scientific value and when replaced by more specific © Petros A. M. Gelepithis, 1984

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words like rule, semantic analysis becomes beset with all the difficulties linguistic theory has been encountered with so far. Put in current linguistic or biological terms: UTM is solely concerned with behaviour or performance ignoring entirely whatever mechanisms may -and in a materialistic framework must- underlie behaviour or realize- materialize- competence. 3.2.2. Linguistic theories of meaning

Linguists concern for the nature of meaning never figured first in their agenda (Rosenberg and Travis1971; Vendler 1974; Fodor JD

1977). Nevertheless, the

problem of meaning and more specifically the sound-meaning relationship has been recognized and given primary position in the study of language by all linguists (compare for instance Saussure +1916*1974; Bloomfield 1933*; Chomsky various books; McCawley ?*1971; Lakoff 1971; Vygotsky +1934*1962). The reason for that is, perhaps, the youngness of their science, as Fillmore (1971) summarizes the justification for the few activities of linguists in the area of semantics until very recently. The situation started changing slowly in the early sixties with the appearance of the work of Katz and Fodor (1963), Katz and Postal (1964), and Chomsky (1965). Chomsky's "Aspects of the theory of Syntax" was the last major work in generative linguistics before the appearance of a fundamental split in transformational generative grammar. The split concerns the relationship between syntax and semantics. The result was the appearance of two diametrically opposed conceptions concerning the nature of semantics: interpretive semantics and generative semantics. Proponents of the former approach believe that basic syntactic structures can be specified independently of semantic considerations. Adherents to the second conception claim the inverse to be true. As the reader may have already noticed both approaches fall in the framework of generative linguistics. This of course is not the only school in linguistics; structural linguistics, stratificational grammar, the Prague school, to name but a few, are rather well known alternatives in linguistics (Lyons 1974; Simpson 1979). The reason for confining ourselves to this framework is that transformational generative grammar is the dominant linguistic framework for the study of human language in general and human languages in particular, and furthermore the one which can plausibly claim to © Petros A. M. Gelepithis, 1984

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be the most adequate, to date, in dealing with linguistic problems and questions (Fodor 1977; Babiniotis 1980; Lyons 1981). In what follows we shall present the dominant generative linguistics approaches to meaning.

Namely, the interpretive semantics view of meaning (IS), and the

generative semantics conception of it (GS). The presentation of IS will be relatively complete, that is, adequately complete for the purposes of this thesis. Concerning GS our presentation will be confined to the fundamental points of disagreement between the two approaches. The reasons for this unbalanced presentation will become apparent by presenting three different and rather complementary views on the status of GS. First, Paivio and Begg (1981), admit that GS is in a state of turmoil which does not allow for a reasonable presentation of this approach but only of its very general characteristics. Second, as even believers in the eventual prevalence of GS put it "the semantic based grammar view has been inadequately developed." (Steinberg 1982 p 62). Finally, I think that one cannot dismiss lightly clear claims of distinguished rivals: "As for "generative semantics," it is difficult to discuss it because nobody, to my knowledge, now advocates an explicit theoretical position under that name. It is now nothing but a rather loose characterization covering the work of a number of people. Insofar as a theory had been clearly formulated, it seems to have generally been abandoned- at least as far as I know- by those who formulated it." (Chomsky 1979 p 149). Starting our presentation I want to notice that, in what follows, if an explicit mention of either of the two conceptions is not given that is to be understood that what is said applies to both of them. The semantic analysis of natural language is essentially based on the assumption that two relations hold true. First, that sentence meaning is a function of both syntax and word meaning (N5); Two, that word meaning is a function of semantic features (N6). We shall call this approach to the semantic analysis of natural language the semasiosyntactic approach to meaning. In what follows we shall confine ourselves to the Katz and Fodor conception (1963) which exemplifies it. The principal concepts in Katz and Fodor's approach are two: semantic dictionary and projection rules. The former consists of a lexicon, syntactic markers, semantic markers (i.e. features), and distinguishers. The latter specifies which of the semantic entries for different words (termed dictionary entries) can be combined in


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order to arrive at an acceptable interpretation of a given sentence. Schema 2 illustrates the internal structure of the semantic theory as well as the relations of it to its input and output. INPUT (A semantic and its grammatical description)

SEMANTIC THEORY Dictionary component

Projection rules

SPI

OUTPUT (Semantic interpretation) Schema 2. Adapted from Katz and Fodor 1963 p 195. SPI=Set of Possible Interpretations To make clear the Katz-Fodor (KF)conception we give an example they used to illustrate "how a semantic theory of English might interpret a sentence". The sentence is 'The man hits the colorful ball'. According to KF (1963 pp 196-197), "the derived constituent structure" of this sentence is given by the following tree diagram (Schema 3). Sentence NPc

VP

T

Nc

V

NPc

The

man

Vtr

T

Nc

the

A

Nc

colorful

ball

hits

Schema 3.: From Katz and Fodor 1961 p 197.

Now the semantic interpretation of the sentence is reached through a series of amalgamations operating on the terminal nodes of the tree diagram. At this point the


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componential analysis of word meaning, or in KF terms the structure of the dictionary entries, come to play. For instance, starting with 'colorful' and 'ball' we have their respective tree diagrams in the following equivalent, form of linguistic rules: So, for 'colorful': c1: 'colorful' -> (Color) -> [Abounding in contrast or variety of bright colors] {(Physical Object) or (Social Activity)} (N7). c2: 'colorful' -> Adjective -> (Evaluative) -> [Having distinctive character, vividness, or picturesqueness] {(Aesthetic Object) or (Social Activity)} For 'ball': b1: 'ball' -> Noun concrete ->(Social activity) -> (Large) -> (Assembly) -> [For the purpose of social dancing] b2: 'ball' -> Noun concrete -> (Physical Object) -> [Having globular shape] b3: 'ball' -> Noun concrete -> Physical Object) -> [Solid missile for projection by an engine of war] Where: () enclose semantic markers, [] enclose distinguishers, and {} enclose functions of syntactic or semantic markers. Now permissible amalgamations depend on the existence of common semantic markers for the dictionary entries involved.

In our case (Social Activity) and

(Physical Object) are the common semantic markers for 'colorful' and 'ball'. Thus the projection rules, in our case, would produce the following set of trees as the amalgamations of 'colorful' and 'ball': c1b1: 'colorful' + 'ball' -> Noun concrete -> (Social Activity) -> (Large) -> (Assembly) -> (Color) -> [[Abounding in contrast or variety of bright colors] [For the purpose of social dancing]] c2b2: 'colorful' + 'ball' -> Noun concrete ->

(Physical

Object) -> (Color) ->

[[Abounding in contrast or variety of bright colors] [Having globular shape]] c3b3: 'colorful' + 'ball' -> Noun concrete ->

(Physical

Object) -> (Color) ->

[[Abounding in contrast or variety of bright colors] [Solid missile for projection by an engine of war]] c4b4: 'colorful' + 'ball' -> Noun concrete -> (Social Activity) -> (Large) -> (Assembly) -> (Evaluative) -> [[Having distinctive character, vividness, or picturesqueness] [For the purpose of social dancing]] Notice that the combinations c2b2, c3b3 have been eliminated because they do not satisfy the selection restriction {(Aesthetic Object or (Social Activity)} (ibid. p. 199). © Petros A. M. Gelepithis, 1984

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To reach the final semantic interpretation of our sentence the rest of the required amalgamations are similarly produced. 3.2.3. Formal theories of meaning

A. The truth conditions theory of meaning (TTM) Stemming from both the objections against the RTM and the need to maintain a connection between language and the physical environment the truth condition TM (TTM) was developed. It seems that Tarski (1944*1969) was the first modern investigator of semantics that provided the basic ingredients for such an attempt. The key notion in the TTsM is to be found in the reasonable claim that "to understand a sentence is to know what state of affairs would make it true or false." (Vendler 1974). One of the key notions in this claim is the concept of truth. Tarski's aim was to give a "satisfactory definition" of it. The basis of his definition of truth in a deductive system is captured in his idea of an "equivalence of the form (T)", that is, in the following formula: (T) N is true if, and only if, S whereas: S is a variable representing any sentence, and N is a variable representing the name of S. So for instance 'that dog is running' is true iff that dog is running. This idea of developing a semantic theory in terms of the notion of truth was recently modified by Davidson (1967, 1973, 1974) to apply also to a natural language. In his words: "such a theory yields, for every utterance of every sentence of the language, a theorem of the form: 'An utterance of sentence s by a speaker x at time t is true if and only if ____.' Here ‘s’ is to be replaced by a description of a sentence, and the blank by a statement of the conditions under which an utterance of the sentence is true relative to the parameters of speaker and time." (Davidson 1975 p 13). The essence of Davidson's idea is this. Let P be a procedure such that P enables a human h to specify the conditions under which a sentence is true for all sentences in a language L. Knowing then P is an adequate condition for understanding L and subsequently such a P is a TM for L. Actually what Davidson's conception tells (explains) is how we can determine the conditions under which sentences are true and how this can be done by knowing:


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(i) the syntax of the sentences, (ii) the denotations of the words (terms) (Richards 1978). The essence of Davidson's procedure can be seen by working out a particular example. Consider for instance the sentence "All humans are mortal" (1); to work out the truth conditions of (1) we identify (1) with: for all (x) [Hx ==> Mx] (2); H for human, M for mortal. We ask now: When is (2) true? And the answer is: Just when [Hx ==> Mx] (3) is true; (without bothering to what x refers to (by definition of the universal quantifier). Finally we ask: When is (3) true? And we conclude that: (3) is true iff '==>' is true! (you can find out when '==>' is true from the truth tables). To conclude it is clear, I believe, that Davidson's TTM is both plausible and powerful. Its plausibility stems from the adequacy of the procedure P involved in it for understanding language. As Richards (1978 p 106) puts it: "[f]or what is it for a person to understand language? Well, when a person can listen to sentences in that language, sentences which he or she has not heard before, and figure out the conditions under which they are true; surely then we grant him or her a good understanding of the language." Its powerfulness on the other hand stems from the fact that P is cast in terms of classical symbolic logic which in turn can be very easily used in computation. On the negative side now the most strong argument against Davidson's theory is the objection Tarski's conception had to face too. Namely that whereas statements, testimonies, reports are true or false; order, promises, proposals, prayers, etc can not be assessed, or even accounted, in terms of truth and falsehood. B. The AI theory of meaning (AITM) From, at least, the early seventies there were attempts in AI to produce a theory of meaning based on "the basic insights of the field rather than being imported from outside." (Wilks 1980). These attempts are currently accommodated under the name "procedural semantics" (PS). Probably the best way to introduce PS is through the notion of an Machine) C equipped with sensors and effectors able to recognize all possible actions. To avoid potential confusions it is worth noticing that the internal state S of the ideal robot is richer than the corresponding state of a computer. This fact ensures that the symbolreferent relation can be represented in R's S-structure whereas it cannot be represented in C. For instance an 'apple' can be internally be represented in C as well as, for example, the GO TO command but the actual apple cannot. Let further call P any of


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the programs or procedures (small programs that know how to do things, how to proceed in well-specified situations), (Barr and Feigenbaum 1981 p 155). Now the fundamental claim of PS can be expressed very neatly and comprehensively as the belief that meaning can be defined in terms of either S or P in R. Such a conception can take either of the following three forms: (a) m=f(S), (b) m=f(P), (c) m=(S,P). Of these three alternatives only (b) has been given attention. The main proponent of this form of procedural semantics is Johnson-Laird. It should be stressed at this point that he is neither the only proponent of PS nor even the originator of the idea of using procedures to represent meanings of words or sentences. Among the first who explicitly used the idea of representing knowledge about the world as procedures were Woods and Winograd in LUNAR and SHRDLU respectively. We come now to Johnson-Laird's account (1977). His conception is based on the compile-execute dichotomy. He suggests that an utterance, u, would be firstly compiled into procedures into the brain-language (corresponding to R's machine level language) which subsequently would be executed. The execution of the brainlanguage-procedures would, according to Johnson-Laird, constitute the 'semantic interpretation' of u. More specifically the intension of words, Johnson-Laird goes on, are identified with Ps during execution, whereas the extension of them are identified with the results of Ps. So the meanings of words or sentences are the procedures they call or/and are called by. As Winston (1977 p. 165) puts it in discussing SHRDLU: ""Pick up a big red block," ultimately becomes a simple call to the PICK-UP program". So to summarize. What seems to be the main advantage of PS is its ability to include contextual information concerning words and sentences in the form of procedures. As a more recent statement of PS reads: "The overall thesis [of PS] may be stated thus. Apart from context words have no meaning and without dynamic processes, context can neither be discerned nor utilized. An explanation of how words have meaning, therefore, must include an account of how human beings detect and inject context into their writing, discourse, and other linguistic activities." (Ringle 1982). On the negative side, one of the first and probably most powerful objections that © Petros A. M. Gelepithis, 1984

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come to one's mind in relation to Johnson-Laird's conception is the fact that not all sentences or utterances are executable. For instance there are questions which can not be definitely answered; Wilk's example in this connection is illuminating: the question "Is Fermat's last theorem true?" can hardly be said to have no meaning in view of it not having been solved ("executed") yet. 3.3.4. Biological theories of meaning

A. The ideational theory of meaning (IdTM) The inseparability of human language from human thought seems to have been the motivation for what is usually called an IdTM. As Vendler (1974 p 510) puts it: "the same thought -the same proposition, as some philosophers prefer to call itcan be expressed by using various linguistic media. From this point of view, it appears that saying something involves encoding a thought and that understanding what are said involves decoding and recovering the same thought. The meaning of a sentence will consist in its relation to the thought it is used to encode. This may be viewed as the fundamental thesis of the psychological theory of meaning." Of the most comprehensive, though not the earliest, formulations of the IdTM was that made by Locke in the late 17th. century. In his treatise an "Essay concerning Human Understanding" he writes: "The use then of Words, is to be sensible marks of Ideas; and the Ideas they stand for, are their proper and immediate Signification." (Locke 1690*1976). His example to support this claim is based in the various uses the word 'gold' is equated to by different people. For one 'gold' is only the bright shining yellow colour and therefore calls the same colour in a peacock's tail 'gold'. For another 'gold' is the bright shining yellow colour plus its weight. For a third 'gold' is the same sort of colour plus its weight plus its fusibility, and the list can be increased to a considerable length to match the various conceptions of different people. And Locke concludes: "Each of these uses equally the Word Gold, when they have Occasion to express the Idea, which they have appy'd it [the word] to: But it is evident, that each can apply it only to his own Idea; nor can he make it stand, as a Sign of such a complex Idea, as he has not." (Locke ibid). More recently the IdTm was held by philosophers and linguists alike (e.g., © Petros A. M. Gelepithis, 1984

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Saussure +1916*1959; Ogden and Richards 1923*1956). In what follows we shall summarize a version of the IdTM as held by contemporary cognitive psychologists. Paivio and Begg (1981) describe what they call a dual-coding approach to meaning, whose essentials originally spelled out by Paivio (1971 ch. 3). They distinguish three "levels" or "kinds of processes" which are related to the concept of meaning. It should be stressed that these levels are theoretical constructs which, according to the authors "are also linked explicitly to particular empirical indicators or defining operations. The three are referred to as REPRESENTATIONAL, REFERENTIAL, and ASSOCIATIVE levels of symbolic representation or, in the present context, meaning." (Paivio et al 1981 p 115). Concretely : "representational meaning refers to the availability, in the long-term memory, of representations corresponding to things and to linguistic units" (respectively called imagens and logogens). (b) "referential meaning corresponds to reference or denotation in traditional theories of meaning." (c) "associative meaning refers to associations or higher-order structures within each system. These are composed of intraverbal associations on the one hand and compound images on the other." The objections to the IdTM are of two kinds: external and internal ones (N8). The former questions the necessity of 'concepts' as theoretical terms (see for instance Palmer 1981 p 27). The latter is focused on what has come to be known as the abstraction (or universals-particulars) problem (Harrison 1979 pp 26-42; Richards 1978). As Steinberg (1982 pp 87-88) put the abstraction problem: "For him, [Locke] the meaning of the generic DOG consists of one general abstract, i.e. a universal, idea that is applicable to all particular dogs. The particularist's objection to this doctrine is that any such concept is inconceivable. If, for example, a dog has a nose and eyes, what is the shape of a of a 'universal' eye which must cover all possible eye colors?" Another form that such objections take stem from the answers one gives to the question what is an idea. So, (i) if "ideas" are "images in the mind", then the theory fails when there are no images for instance "incect", whereas (ii) if "ideas" are "intensions" then the theory fails when there no intensions, for example, logical words like "if", "all", etc. B. Behavioural theories of meaning (BTsM) The last set of TsM comes under the heading of behavioural TsM (BTsM). As


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their collective name indicates they try to explain meaning in terms favoured by behaviourism. Their fundamental assumption is that Mu=f(Rs), where Rs is the response, (or pattern of responses), the utterance, u, evokes in a given situation, s. The early favourite model in this context is the Pavlovian model of conditioned responses. In this framework the most recent and comprehensive theory is that propounded by Osgood. Osgood calls his theory "representational mediation theory of meaning" and derives it from Hull's theory of learning (see Osgood 1971). In particular, Osgood translated Hull's concept of the "fractional anticipatory goal response", rg, to his concept of mediational or meaning response RM. The primary postulate of his theory of meaning has been verbalized by Osgood himself as follows: "a stimulus pattern (N9) which is not the same physical event as the thing signified (S) will become a sign of that significate when it becomes conditioned to a mediation process, this process (a) being some distinctive representation of the total behavior (RT) produced by the significate and (b) serving to mediate overt behaviors (RX) to the sign which are appropriate to ("take account of") the significate." (Osgood 1973 p 451). To illustrate Osgood's theory let us consider a concrete example of his own. Schema 4 may be of some help to this end. Stimulus Pattern ---------(S) rM

RT

significate or referent, or thing signified (total behaviour)=(R1, …, Rn).

sM RX (set of overt behaviour) rM=the total representational mediation process elicited by a sign. sM=automatic-consequent of rM.

Schema 4. Adapted from Osgood 1973 p 452. Early in infant life, the presence of the nipple of a bottle in the infant's mouth (that is the unconditioned stimulus (S)), produces a number of behavioral responses, for instance, sucking movements, milk ingestion etc (that is RT). Later on as the sight of the bottle (that is the conditioned stimulus ), regularly antedates the presence of (S), eventually produces mediation process (essentially rMsM), conditioned to it, which serves to mediate overt behaviours like salivation or "anticipatory sucking movements" (that is RX).


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There are four particular points about rM's we should emphasize. First, according to Osgood, meaning is derived historically from behaviours to things and not perceptions of things. Second, rM's may be peripheral, autonomic or just cortical reactions though Osgood himself is inclined towards a centralist position. Third, rM is not a single response, but a pattern of responses (or mediator components, or semantic features), which for brevity reasons we shall symbolize by a vector (rM=rm1, ..., rmk); Finally, rM is a theoretical term, or as Osgood prefers to put it: "rM's (both caps and lowers) have the theoretical status of HYPOTHETICAL CONSTRUCTS (with presumed existential properties) rather than intervening variables (convenient summarizing fictions)." (Osgood 1973 p 452). So, to Osgood things like a mountain and a river, for instance, have distinct meanings because of our distinctive patterns of behaviour towards them, or as Paivio and Begg (1981) have put it: "an apple, for instance, becomes meaningful because of our handling, biting, chewing, and tasting responses to it." There are two main objections to Osgood's version for an S-RTM. Firstly it is argued that meaning should at least partly be based on our perception of things and not exclusively on our responses to them. Secondly it has been argued (Fodor 1965) that rM does not yield any predictive advantage over 'single' S-RTM. The formal equivalence of the latter with Osgood's theory is claimed to be due to accepting the hypothesis that a representational theory must assume that rM is in a one-to-one correspondence with RT. Osgood rejoins as follows: “Since rm's are hypothetical constructs, they bear a part-to-whole relation to RT's only in the sense of being 'derived from' and being 'distinctively representational of' RT's. They are not 'parts of' in the literal sense of being a sub-set of the overt R's making up RT.... In exactly the same sense that one cannot substitute some particular phone (which one?) for a phoneme in linguistic theory, one cannot substitute any particular overt R (which one?) for an rm in behavior theory. Thus representational mediation theory does NOT reduce to single-stage S-R theory, as Fodor claims. (Osgood 1971 p 524). 3.3 Conclusions Probably the least controversial conclusion our survey may offer seems to be the inadequacy of the proposed 'theories' of meaning or/and understanding to account for the phenomena under consideration. Let us make it clear, that this remark is not


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intended to be reprehensive of the 'theories' proposed; it should rather be taken to be indicative of the great difficulties besetting these phenomena. As Prof. Ziff writes in his study of understanding: "When one broaches such topics as these, one can no longer avoid the dismal conclusion that to understand understanding is a task to be attempted and not to be achieved today, or even tomorrow." (Ziff 1972 p 20). Could, then, definitive conclusions be drawn upon the foundations of such a wide and controversial area of investigation as human cognition? On first sight the answer seems to be no, and on closer inspection this suspicion becomes almost certainty, save a few very general conclusions, remarks rather, which investigators of human cognition seem to take for granted, if not for commonplaces worthless of further consideration. This stance, as it may be for human cognition, it is certainly not the case as far as its consequences for AI is concerned. In what follows we shall state exactly those most general conclusions, concerning meaning and human understanding, which all investigators agree, and which we shall use in our argument against the possibility of machine intelligence, section 4.3, and as a basis for the first part of a proposal, chapter five, for a unified treatment of meaning and understanding. Our first conclusion concerns the relation of understanding to meaning. It should have been clear by now, mainly through our review of the main approaches to understanding, the close dependence of understanding to meaning. Indeed, the fact that human understanding is inseparably tied to human meanings. This remark coupled with the majority view conceiving meaning as a thing rather than as a process, while understanding the other way around, can not help us to suggest that the phenomenon of human understanding- with meaning providing the material substratum that is necessary for the occurrence of human understanding. Chapter five accepts this view of meaning, actually we assume there that meaning provides the material substratum for the whole of the human cognition, and provides a rough outline of a theory of meaning as the first step towards a unified treatment of meaning and understanding. We shall, nevertheless, remind the reader that our main goal in this thesis is to investigate the validity of the engineering goal of AI. To this end our next conclusion, concerning meaning, plays a vital role. This conclusion is such a commonplace that one may wonder whether it is worth


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mentioning it at all. We believe it is; for reasons that will become fully clear when stating our argument against the possibility of machine intelligence in section 4.3. This is the fact that (human) meaning, whatever it is, is human dependent. This is a fact that all 'theories' of meaning, explicitly or implicitly, recognize, section 3.2, regardless of their specific stance on the nature of meaning.


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4. The Phenomenon of Understanding: Consequences for AI "[On being asked to define New Orleans jazz]: Man, when you got to ask what it is, you'll never get to know." Louis Armstrong, as quoted in Hayakawa 1978 p 48. We have seen in chapter three that, whatever view of understanding is in particular held, the question of the nature of understanding is generally recognized to lead into questioning the nature of meaning. We have also seen, section 3.3, that meaning is generally recognized to be human dependent, and that the question: 'what is understanding?' has not been adequately answered. The first section of this chapter aims exactly at providing a less inadequate answer to this question. However adequate the results of such an attempt may be, they can not be adequate enough to frame a satisfactory answer to our primary question of manmachine communication (mmc). The reason is that human understanding is but one part in the mmc problem; the other is machine understanding. To cope with this requirement we present, section two, a definition of understanding covering both human and machine understanding. Finally, section 4.3, we develop our argument that we, humans, can never construct an apparatus able to understand human language. 4.1. The nature of human understanding: human primitives In this section we shall present, discuss and justify our definition concerning the phenomenon of human understanding. As we have seen, chapter three, understanding has been conceived in many different ways. As with virtually all phenomena, and particularly the human-related ones which become the object of scholarly investigation, understanding has been endowed with a number of characteristics depending on the particular scholar investigating it, or the specific school created as a result of the concerted effort of similarly thinking scholars. It follows that even to hope to reach an objective definition of we should start with such properties which either everybody agrees the phenomenon of understanding possesses, or it can be justified that it does. Then, by analyzing the notion of


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misunderstanding we shall specify the crucial property which, we believe, characterizes the phenomenon of human understanding. Finally, assuming that the characteristics of any phenomenon are precisely the properties/facts present in any (and all) manifestations of the phenomenon under investigation, we shall postulate the properties specified that far to be the only ones present in all manifestations of human understanding. The first property, accepted by all workers concerned with understanding, to characterize the phenomenon of human understanding, is that human understanding always involves the grasping of 'meaning' whatever this word means. To help the reader accept this property we shall recall that both schools of understanding accept it; and furthermore notice its common acceptance as it has been crystallized in, for instance, the English dictionaries (e.g., The Oxford English Dictionary on Historical Principles). Parenthetically a simple consequence of this characteristic may be noticed. As we saw in section 3.1.A the majority of workers in AI identify 'understanding' with recognition. Now, although there are cases in which this identification may be said to hold true, e.g., in recognizing a face, it does not hold true in general. The reason is simply because recognition of a symbol, for instance, that is, its shape, does not imply grasping of its meaning, as experts, for instance, are painfully aware of when they move into an alien domain of expertise. The second characteristic of human understanding can again rather easily be accepted universally: Human understanding is a process taking place in human brains. For the alternative view considering human understanding as a state of the human mind, and our reasons for rejecting such a view the reader is referred to section 3.1.B. Now it is obvious that human understanding, viewed as a process, is required to be terminated for human understanding to be possible. But although it may be accepted as obvious that the process of understanding should be terminated, it seems to be not obvious at all WHERE it should be terminated. Naturally, thinking processes may, and do, terminate somewhere; but, after all, not all thinking is called, or can be, understanding. One may, for instance, be interrupted while trying to understand something and subsequently fail to catch again the thread of that particular reasoning. To be sure, there may be future recall of the interrupted process but our point here is only that at the time of interruption no understanding may be said to have occurred. So, we conclude that not all terminated © Petros A. M. Gelepithis, 1984

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thinking processes are processes of understanding. But let us assume that we witness a case of an undisturbed thinking process. When shall we say that one has achieved understanding? We have seen that just termination of the process is not adequate. What, then, additional conditions should be fulfilled in order to say that one has understood something? There are two particular thinking processes which are said to be related with, or comprise part of, human understanding, which will help us to specify where human understanding is terminated. They are called alleged and partial understanding respectively. ‘Alleged understanding’ (or misunderstanding) may be said to be the case whenever people think they have understood something only later to discover that what they had concluded was not the case. The key point in a case of misunderstanding is the fact that one's own predictions or explanations, according to his or her model of a situation, phenomenon, etc., turn out to be not the case. Assuming there was not any fault in the inference process we conclude that there must have been mistaken premises. We see, therefore, that whenever misunderstanding is reached there must have been some mistaken premise involved. We take this fact to be a strong suggestion that to reach understanding one's own premises- concerning the phenomenon, situation, etc. to be understood- must hold true. It is tempting, therefore to postulate human understanding to be that thinking process at the end of which a description, in terms of a set of reference beliefs, of whatever is attempted to be understood, is reached. Whereas, a 'set of reference beliefs', for a human being, is a set of beliefs according to which that human lives. The question now is: what does that 'set of reference beliefs', Br, consist of? Though, of course, there is no need to specify Br we shall make a distinction which we believe to be convenient and useful. Our distinction will actually give rise to two types of understanding corresponding to the notion of partial understanding and what may be called complete human understanding respectively. We shall call ‘complete human understanding’, Uc, that type of human understanding where its Br cannot be further reduced. In other words we call Uc the thinking process which ends in describing, whatever is to be understood, in terms of human primitives, that is, self-explainable or undefined concepts. We, furthermore, call 'partial understanding', Up, all understanding that is not complete. As the reader may already have noticed our definitions of human understanding © Petros A. M. Gelepithis, 1984

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and its two main types have been cast in mentalistic terms. It would have been desirable, therefore, should we be able to restate our definitions so far in neurophysiological terms. Unfortunately this is not only far beyond our purposes in this thesis, but also a mammoth task that a lot of investigators, perhaps rightly, believe to be beyond the current, if not any future, possibilities of neurophysiology. Nevertheless, despite the discouraging picture just painted, we shall go on to provide the reader with a pseudo-neurophysiological definition of Uc. The reason is very simple. Even pseudo-neuronal definitions of mental phenomena force the investigator to view them in a light not provided by their description in mentalistic terms. Before proceeding to our definition a terminological point. We call ‘terminal neural formation’ (tnf) the neurophysiological end of a thinking process. It follows, in accordance with our mentalistic definition above, that we shall call complete human understanding, Uc, the neurophysiological process ending at primitive terminal neural formations (tnf's) (N10). There is, now, a peculiar and apparently contradictory characteristic in this definition of human understanding. Namely, it seems to be cast both in materialistic and idealistic terms. Indeed the key word of it, the adjective ‘primitive’, seems to be torn apart by referential ambiguity. For, on the one hand, ‘primitive’ may be taken to qualify the expression ‘terminal neural formation’, in which case it refers to a neurophysiological entity; and on the other hand, when explicated, its implicit reference to concepts clearly emerges. Of course this ambiguity is justifiably risen when one identifies (a move entirely in accordance with our conceptual framework (sec. 1.2)) human meanings with human neural formations. Moreover, this identification is overall justifiable, that is, justifiable in view, not only of a general materialistic framework, but also in view of the alternative approaches to meaning. Indeed, none of the various theories of meaning, section 3.2, is explicitly against such an identification, at least as far as, by 'meaning' we mean human meaning. We are, therefore, on rather firm grounds when making this identification. We come now to a conceptual analysis of the term ‘primitive’, on which (analysis) we shall subsequently apply our identification of meaning with neural formations. Firstly we observe that 'primitive' exhibits a temporal ambiguity. On the one © Petros A. M. Gelepithis, 1984

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hand, it may refer to, in a more or less timeless manner, undefined or self-explainable concepts; on the other hand, it may refer to the earliest stage of a development in which case it clearly indicates a time dependence of concepts. Now, in the latter case, and bearing in mind our identification of meaning to neural traces, a 'primitive neural formation' has to be identified with a pre-linguistic neural trace, since human language is admittedly of relatively recent origin. It follows that such primitives, we may call them pre-linguistic or sense primitives, are inseparable from the corresponding organism concerned. Furthermore, since human linguistic primitives are related to human sense primitives, linguistic primitives- save purely linguistic primitives, see N11- are also inseparable from the corresponding organism concerned. To see this more clearly consider the following game. Suppose we are given the words set, electron, water, pain and we are asked to classify them in either of the two mutually exclusive sets: either in the set of primitives P, or its complementary P'. It seems that set falls in P electron in P'. What about water and pain? It might seen obvious that H2O being analyzable to its constituents parts and these in their turn to electrons should be placed in P'. Still I can think of no-one who would not place water in P. But even now we have problems, for set and water are two quite different "primitives". Set can be a primitive for a mathematician whereas water is a "primitive" to anyone. What about pain then? It might seems obvious to everybody that pain should be put in P, BUT Dennett (1978) has recently given a nice, although sketchy, model of pain; it follows we should put pain in P'. So what do we conclude? We infer that three out of the four concepts can be placed either in P or in P'. Electron is the only one of the four which cannot be placed in P. Nevertheless the class of concepts like electrons which can be placed in P' but not in P is not a singleton. Furthermore I want to notice that if we were able to distinguish electrons we could place it in P but that requires sense experience which at present we do not possess. What are the consequences of this inference then? It follows that we must distinguish two classes of stages in the development of human concepts: the first, let us call it the prelinguistic (pre-l) stage, occurred before the appearance of human language. The second, let us call it linguistic, after its appearance. In what follows I will give two examples. The first will clarify the relation between primitives; the second will show the ubiquity of our remark. The first example points to the distinction between primitives so we have: © Petros A. M. Gelepithis, 1984

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(a) prelinguistic primitives like pain, water (b) object primitives like change, time etc. Generalizing our conclusions for primitives to include all human concepts we may distinguish three different kinds of meaning: pre-linguistic (or sense) meaning, object meaning, and meta-meaning. (see N12). Applying now our identification of meaning with neurophysiological traces we end up with three distinct neurophysiological levels which for convenience reasons we shall also call pre-linguistic, object and meta. To illustrate these three kinds or levels we shall now give one or two examples for each kind or level: (a) prelinguistic (or sense) information like 'site of a rivulet', 'the shapes of trees' etc. (b) object information like 'John is running', 'Give me some bread', or the object information a theory may include. (c) meta information like traditional grammars, the sentence "colourless green ideas sleep furiously" etc. The reader may already have noticed that we have not provided examples of primitives for the meta level (or kind). This is because by the definition of 'meta' we cannot have meta-primitives. It follows that understanding in meta theories is impossible. What can happen is, what we may call 'ruled understanding', that is, understanding following reducibility to arbitrary imposed rules. Of course, this ruled understanding can also happen in manipulating object information, as for instance, in following proofs in mathematics; the difference is that in the latter case understanding is still possible for those interested, whereas in the former case such a possibility has been ruled out by definition. To put it in other words, the distinction between the concept of primitives in the pre-linguistic and linguistic categories is carried over to understanding, resulting in two sorts of understanding: a pre-linguistic and a linguistic one. Probably the first question we should ask reads: is there any difference of kind between pre-linguistic and linguistic understanding? The short answer is that there can be. It depends on whether linguistic understanding is reduced to pre-linguistic understanding or confines itself exclusively in the linguistic level. In the latter case, i.e., whenever understanding is reduced to linguistic primitives which either do not correspond to material entities or they are not reducible to pre-linguistic primitives, a difference of kind exists. The difference is that pre-linguistic understanding, or corresponding © Petros A. M. Gelepithis, 1984

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linguistic understanding, are verifiable, whereas any other kind of understanding is only 'neurophysiologically true' (n-true). A few examples may help to illustrate the notions introduced. As a clear example of verifiable understanding one may notice any case of inductively reached knowledge (e.g., fire burns; heavy objects fall; etc). Further examples of this type may be provided by formal systems. The formal systems of Euclidean Geometry and Quantum Mechanics can provide two such examples in the case where they are used as physical models. It should be noticed that neither Euclidean Geometry nor Quantum Mechanics are necessarily free of n-true understanding. Let us try now to justify our overall approach to understanding. Probably the strongest reason for our definition is its utility as a criterion for checking whether someone has understood something or not. This can be done by asking, for example, someone to explain what he or she has understood. A complete explanation (that is, an explanation entirely in terms of primitives), would be both an adequate criterion that he or she has understood and it would facilitate communication since primitives either are shared by all humans (sense primitives), or can be shared by all humans (linguistic primitives). It follows that such a criterion makes the study of human understanding empirically investigable at the conceptual and possibly behavioural levels. On the neurophysiological level though the investigability of human understanding, as we have defined it, is much less easy. Nevertheless, there seems to be no reason why appropriate neural formations could not be identified with concepts like time. A second reason in favour of our definition is of course that it does not violate the common sense of understanding as described by most dictionaries and encyclopaediae (e.g. Oxford, Longman, Britannica). Furthermore it is in accordance with contemporary views on the nature of understanding (e.g. Brewer 1972*1974; Greeno 1977), and it is more or less directly supported by workers like Nagel (1961), and Russell (1948). Finally, being defined in terms of the notion of reducibility (i.e., the ability of tracing back) it allows a theoretical explanation of the fact that understanding may be complete or not. 4.2. The nature of understanding: primitives As is pointed out in the introduction the central aim of this thesis is to © Petros A. M. Gelepithis, 1984

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investigate the possibility and nature of machine understanding. Since our investigation so far was exclusively confined to human understanding one might think that we are well off our target. Actually we are exactly on the spot and for a very good reason. We humans, presumably because of our limited nature, always try to delineate whatever phenomenon we have to investigate. In the case of ‘machine understanding' that attitude would destroy the phenomenon under investigation. For ‘machine understanding' is, at least at its starting phase, a question of human constructability and as such it cannot be specified. What one may nevertheless be able to find out is whether there are limits in human constructability itself. It follows that an investigation of the human nature is not merely justifiable but necessary if one wants to investigate the nature of at least some of the human constructions. We need to define 'machine understanding' so that we know what are we talking about. On the other hand our definition is required to be cast in such terms that it does not confine human constructability in pre-set moulds. Now this requirement will be satisfied in case the sought definition is cast in terms independent of human constructability itself. To this end we observe that our definition for human understanding does satisfy this requirement. Nonetheless that definition has a characteristic that makes it unsuitable for our purposes. Namely, is cast in neurophysiological terms. Fortunately this drawback can be overcome too. We can generalize our definition for human understanding by substituting a more general condition for its specific neurophysiological characteristic. The most general such condition, in accordance with our materialistic hypothesis, is a requirement for material primitives. Notice that we do not require 'electronic' or any other specific kind of primitives, we just ask for a material basis for them. So, on these grounds we shall say that a computer C understands something, S, iff C reduces S to its electronic primitives. And, most generally, an entity E understands S iff E reduces S to its material primitives (N13). According to this reasoning it follows that machine understanding is possible given, of course, that human beings are, or will be, adequately capable of providing the initially required impetus; a capability that we can see no reason that humans do not or will not possess. Nevertheless, granting machines the possibility of machine understanding, does not imply an affirmative answer to the man-machine communica© Petros A. M. Gelepithis, 1984

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tion question too. This is the question we investigate next. 4.3. The only way to human automata: Evolution Until recently, relatively speaking, there were three prevalent alternatives to explain the appearance of human beings: divine will, extraterrestrial activity and evolution. At the beginning of the second half of the 20th century a conception dating back to Goethe's times started being seriously considered as a fourth possibility of causing humans to exist. Namely the construction of humans by humans (N14). The conception was further abstracted to include the construction of entities made by nonhuman material which would/could equal or surpass their creators. Such entities are variously known as intelligent machines, robots (humanoid or not) or simply computers. The creationist approach to the problem of the origin of life, although prevailing for thousand of years, has ceased to be attractive nowadays. The main reasons seem to be its lack of supporting evidence as well as its lack of explanatory power. Of course both reasons are judged to be inadequate in the light of the prevailing paradigm of our times- science. If we do not accept science as the only way to explain happenings the creationist approach becomes quite plausible, especially in view of the fact that questions like the origin of the universe are equally insufficiently answered by both, and actually all, approaches. There are two versions of the extraterrestrial approach. According to the first one humans were created on planet earth after the emergence of life on this planet. On the second version life itself was brought to planet earth. The latter, although seemingly a novel conception, really just shifts the dispute over the origin of life from concrete and 'down to earth' evidence to mere speculation. The former contradicts the continuity exhibited between humans and the rest of the primates. It may be said, nevertheless, and has indeed been the case, Crick (1981*1982), that this problem of transfer is only partly true. What of course is aimed at, in this case, is to distinguish between the questions of the origin of life on earth and the origin of life itself. Crick's arguments concern the question of the origin of life on earth; whereas our point concerns the question of the origin of life itself. So, based on common sense and the generally accepted view in favour of the evolutionary process approach, we may safely disregard the divine and extraterrestrial © Petros A. M. Gelepithis, 1984

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approaches on the grounds of lacking evidence. In what follows we shall concentrate on refuting what we call the anthropomorphic approach to the problem of the appearance of human beings, that is, the belief, and the subsequent attempts, that the AI problem (section 2.1) can be solved. For a human-made construction to be able to understand human language either one of two things should happen. One, we should construct the human neuroprimitives themselves i.e., construct the human brain. Or two, we should construct an apparatus able to understand the human primitives. I shall consider the two options in turn. Could humans construct themselves? The question has been deficiently posed for, of course, we want to exclude answers of the type 'through insemination'. So, the question really is: Could humans by 'direct synthesis' construct themselves? There are one or two points I want to make here. First, a point of word use. By 'direct synthesis' I mean a kind of procedure which would rest upon complete knowledge of the entity concerned. But it is well known that our knowability of both the mental and physical phenomena is restricted at least as far as the limitations posed by Heisenberg's principle go. Of course there is still the possibility of us constructing a human being in, more or less, the way we construct a bridge or a spaceship, that is, not by direct synthesis but merely by a know-how knowledge. This is a possibility for which I, currently, cannot see any fundamental objections, but which nevertheless I tend to think as less attractive than bridge construction solely on grounds that the risk element involved in the former may not be of the same kind as that involved in the latter. Let me write one or two paragraphs on this belief to make sure there is no misunderstanding. Since our knowability for anything is limited, that is not complete, it follows that we cannot ever be absolutely sure that our control over something will be complete either, and similarly our predictability will suffer. Of course failure, operational or whatever, may well result in a disaster with a bridge or spaceship; but the consequences of the unhappy incident will be definitely confined in space and number. Unfortunately, the same cannot be definitely asserted in the case of a genetic mishappening. On these grounds I would think that human construction by, say, molecular engineering ought to be avoided at least till the time that humans will have overcome present-day dangers of self-extinction or misery (e.g. © Petros A. M. Gelepithis, 1984

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by nuclear war). We come now to the second of our two initial possibilities, namely to construct an apparatus able to understand human language. Essentially our problem is one of communication. Can a computer and a human communicate, say, in language? More specifically can a human and an ideal robot (section 3.2.3.B) communicate on a topic, subject, or something whatever, in human language? I think it is preferable at this point to make our discussion more precise. We start from human communication. It is generally accepted that (human) communication has to do with making something common (see for instance Cherry 1957*1980; Ogden and Richards 1923*1956): "COMMUNICATION IS ALWAYS AN ACT OF SHARING" (Cherry ibid p 306; his emphasis). "Thus a language transaction or a communication may be defined as a use of symbols in such a way [t]hat acts of reference occur in a hearer which are similar in all relevant respects to those which are symbolised by them in the speaker." (Ogden and Richards ibid pp 205-206). It will therefore be justifiable to introduce the notion of communication in similar terms, concretely in terms of mutual understanding. So, we shall say that a human h1, communicates with another human, h2, on something expressed in a language L, call it S, iff h1 and h2 understand each other on S. We shall then, naturally, say that h1 and h2 understand each other on S iff h1 understands S, h2 understands S, and the two understandings are the same. To illustrate our definition and simplify further presentation we introduce Schema 5 below. n1

…

np1

h1: s

L(np1)

s

L(np2)

n = neural formations np = neuroprimitives s = something expressed in a language L.

h2: n2

…

np2

Schema 5. The two S's connected by a straight line stand for the idea that there is no distortion of S during the process which may lead to communication, that is, we are not concerned here with the accuracy of transmission. Accuracy of transmission is a technical problem which, for our purposes, we shall assume does not exist.


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Returning to our definition of communication we shall say that h1 communicates with h2 on S iff U1(S)=U2(S). Now, since the results of the function U are internal to the human concerned, the question is how we can test the sameness condition in our definition. The answer is rather simple: U1(S)=U2(S) iff it is possible that L(np1)=L(np2) for all nps, whereas L stands for a human language. To be able to tackle our original question concerning the possibility of manmachine communication we must, obviously, have a definition of communication not cast in exclusively human terms. In accordance with the above we shall, now, say that a computer C and a human H understand each other iff it is possible that L(ep)=L(np) for all primitives. It becomes clear therefore that the sharing of primitives is a necessary prerequisite for a man-machine communication. Is that possible? There are two ways that a computer C can share with a human H the human neuroprimitives, that is, the human primitive meanings: one by acquisition; two by being given. Granted that acquisition, in the robot case, presupposes a minimum set of meanings to be initially provided by humans, we shall only examine the latter case. Human primitives are of two kinds: sense primitives and linguistic ones (section 4.1). Our first question is whether we can give linguistic primitives to computers. There is a rather trivial sense in which a H can certainly give a theoretical primitive to a C. Just give the name of it. There is another sense, rather non-trivial and certainly useful, according to which we can give to C not only the names of theoretical primitives but also a set of rules for their combination. In other words we are able to give a C a formal uninterpreted system. Again this is not adequate for our purposes. What we do want to give to a C in addition to a calculus is an interpretation of the latter. Now, a human interpretation of a formal system always include human sense primitives (see section 1.2). We are led therefore to consider the possibility of giving non-linguistic human primitives to a C. Notice that we are not concerned here with the possibility of providing C with robot-sense primitives. The latter is, at least in principle, rather easy. For, particular problems of relating the computer sense data with linguistic names and rules are in principle solvable (see 3.2.3.B). So to sum up: we can have, in principle, an ideal robot able to move around act on the environment and process information in any formal system whatever. Our © Petros A. M. Gelepithis, 1984

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initial question then becomes: can an ideal robot R, in principle, communicate with a human H? And according to our approach this will be possible iff it is possible that: L(ep)=L(np) for all primitives. Now, in an attempt of settling the question quickly, it may be claimed that, there are two categories of neural primitives for which the corresponding computer categories are empty. The first one concerns self-preservation; the second reproduction. It follows, of course, that there are primitives for which the equation L(ep)=L(np) can not hold, and therefore the man-machine communication breaks down at this point. Actually the situation is slightly worse, for there is a number of other categories that are reducible to the two aforementioned ones which makes the man-machine communication break down at a few more points. Naturally the question is raised: is any sort of man-machine communication possible, since it is debatable whether there are any categories, apart from purely linguistic ones, that are not reducible either to the categories of self-preservation and reproduction, or to a third category that is directly demonstrable that its corresponding computer category is empty? Interesting as this question maybe, rather for its own sake, it shall not concern us here. We shall, instead, comment on a possible rejoinder. It may be counter-argued that neither of the self preservation and reproduction categories are empty for a computer. That, the argument may go, is only a question of definition and as soon as we include electricity supply in the former and computer network in the latter the two computer categories cease to be empty. The argument sounds powerful but it really evades the point. For the point is not whether a C can, in principle, have its own primitives in these categories BUT whether a C can, in principle, share primitives. The difference is essential because it is necessary for a computer to have both its own and corresponding human primitives for otherwise C will not be able to compare the two and therefore will be, in principle, unable to draw any analogy for humans that would also be possible to be understood by it and subsequently unable to understand human beings. So, can R share human primitives? We have already seen (see our analysis of primitives, section 4.1) that human primitives can be distinguished in linguistic and sense human primitives, and that, furthermore, linguistic primitives save purely linguistic ones (N11)- are eventually reducible to sense primitives. Finally, we have seen that computers must have their ‘starting off’ set of human primitives being given © Petros A. M. Gelepithis, 1984

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by humans. It follows that our question becomes: Can humans, even in principle, give human sense primitives to robots? The key word, now, in this question, is the verb 'give' and it is best to clearly distinguish the various alternatives. What is possible to be given are two things: a) the carriers of the human sense primitives, that is, the human sense organs; b) the description of the human sense organs. Now, to describe (the meaning of) the human sense primitives we need some human language, say L. Furthermore, for an R to understand the 'described in L' human sense primitives, (SPs), it needs to understand L. But, to understand L is equivalent to understand human linguistic primitives AND human sense primitives, since human linguistic primitives, (LPs), are reducible to human SPs (save purely linguistic ones; see N11). In other words we find ourselves in the vicious circle of needing human SPs to understand human LPs and human LPs to describe the human SPs. Put even simpler, we need language to describe the senses (sense meanings) and senses to understand language. What then about the first alternative? Let us assume that we are in principle able to give to R the human SPs. In other words we are able, in principle, to construct a symbiont possessing both human and robot sense organs, (SOs). Of course human SOs on their own, that is, apart of a human being, do not record human sense meaning; they only record human sense data. Obviously, this assertion is equivalent to claim that the following hypothesis holds true: human SOs, apart from a human being, provide only human sense data

and not human sense meaning. We take this

assumption for granted; it follows that the only way to overcome this problem is by describing the human sense meaning to a symbiont, a task we saw to be impossible.


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5. Semantics in Theoretical Perspective "Unlike such relatively mature areas of linguistic as phonology and syntax, semantics exists not as a field of scientific investigation but rather as a heterogeneous collection of proposals for the creation of such a field." (Fodor and Katz, 1964). "much of the discussion [in semantics] is more philosophical than scientific." (Palmer, 1981). "[On the situation in Lebanon:] officially there is no war, but that is semantics." (BBC 9 o' clock news; 6/2/1984). As we saw in section 3.3, meaning, or more accurately human meanings, may be reasonably identified to neural formations. This identification makes semantics part of human neuropsychology. Now, there are a few points I wish to make. Firstly, we should understand that human meanings, (and, therefore, semantics), are not identified, (referred to), to the ephemeral, individualistic meanings which each of us assigns to his or her speech but to the endurable meanings that human society forms in its course. This distinction is analogous to the distinction between langue and parole made by Saussure, except for the fact that human meanings cover both linguistic and pre-linguistic human meanings. In other words, though the influence of human society, and the world at large, continually influence the shaping of human meanings, their PERMANENT influence on human meanings can only be seen after the lapsing of a long time surpassing that of an individual's human life span. Secondly, we believe that although societal and, more generally, environmental influences are the main factors shaping human meanings, their corresponding notions are least useful in the scientific study of semantics. The reason is that meaning is, eventually, part of a human being, and not part of human society or the world; however much both human society and the world influence human meanings. We may conclude therefore that by having, justifiably, identified human meanings to neural formations, semantics can be reasonably claimed to have been put on an empirical basis. Nevertheless, an empirical basis, though necessary, is not adequate to make semantics a scientific discipline. What is, furthermore, required are legitimate GENERALIZATIONS which would enable workers in this field to construct adequate explanations and, probably, to some degree, predictive theories. © Petros A. M. Gelepithis, 1984

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The reason for which we qualified the construction of predictive theories is that we believe it to be impossible to completely control both the genetic and environmental components which make up the human mind. On this basis, we try, in this chapter, to contribute in putting semantics on a scientific basis. Thus, in section 5.1 we examine the possibility of constructing a theory of meaning. In particular, in section 5.1, the view is developed that although current conceptions of meaning cannot be called theories in the established sense of the word theory (see also section 1.1), there is no reason against the possibility of a TM in that sense. Then, in section 5.2, we offer an outline for an explanatory theory of human meanings. 5.1. Is a theory of meaning possible? Reading the literature on 'meaning' one gets the feeling that all discussions about, and 'theories' of, 'meaning' are made in a sort of vacuum. All seem to know what a 'theory' of 'meaning' is for and of course what the object of their investigation is. Naturally the newcomer is, at first, lost and subsequently, either adopts one of the numerous working frameworks or, usually, leaves the field with mixed feelings of admiration and contempt. Having spotted the lack of goals and even of a 'clear-cut' object of investigation he or she is subsequently not far away from asking what a theory of 'meaning' is for and whether a theory of 'meaning' is possible in the first place. We should make entirely clear here a peculiar characteristic of our question. When the stress is on 'theory' the investigation is oriented towards the feasibility of a theory of meaning; on the other hand, when we stress 'meaning' our investigation is invariably led to request to specify the word meaning at least to the extent that it would make 'meaning' an acceptable term for attempting to construct a theory in the classical sense of the word. A little reflection, I hope, reveals that the two questions feedback to each other; we have nonetheless to start from somewhere so we shall start from considering the possibility of a THEORY of meaning. Sometimes this question is indirectly answered to the affirmative by considering the related question: is a theory of meaning needed? So, for instance, Alston (1967) in a well written article in the Encyclopaedia of Philosophy creates the impression that a theory of meaning is needed by stating seven different senses of the word 'mean'. This © Petros A. M. Gelepithis, 1984

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leaves the reader with the impression that the ambiguity of the word 'meaning' and its derivatives is one of the essential characteristics of the word ‘meaning’. Of course this is not so for the word spring has NINE different meanings without having been considered for this reason worthy of philosophical scrutiny. So, is a 'theory' of 'meaning' possible? It is true that any conception about something if combined with other conceptions or beliefs will have a number of consequences. There is an increasing tendency nowadays to call such a system a 'theory'. Naturally I would have had no objection to such a usage of the word 'theory' provided that it is always and clearly contradistinguished from the established notion of the word 'theory' (see sec. 1.1). In what follows I will call the first notion of 'theory' a belief system, (BS), retaining the word 'theory' for its established notion. Returning to our initial question "is a theory of 'meaning' possible?", we now have two questions to answer: Q1) Is a BS concerning 'meaning' possible? Q2) Is a theory of 'meaning' possible? Clearly the answer to Q1) is yes. We already have quite a lot of BS's concerning 'meaning' (e.g. Davidson 1967, 1974; Grice 1957, 1969). Coming to Q2) we are led to investigate a few more points. The first question we have to face is what are the phenomena which a 'possible' theory of 'meaning' has to explain. We should make once more clear that we do not question the ‘legitimacy’ of BS’s concerning ‘meaning’. This is, for us, beyond dispute; personal or local interest or usefulness is, to us, an adequate reason for 'legitimizing' relevant concerns. What we are after is to find out whether or not there is a class of phenomena which can, in principle, be characterized by a set of parameters and which therefore, are liable to serious (scientific) investigation. Put in yet another way: are there genuine problems in what has recently come to be known as semantics? It is generally accepted that problems precede whatever theories humans put forward to explain them. It is rather unusual to have explanatory theories looking for problems instead of having problems first and then -should we be concerned with them- looking for explanations. In the case of 'meaning' the situation is not entirely so; of course this does not imply that workers in 'meaning' are 'problems-creators'. It is in the nature of 'meaning' that the problems an investigator is going to consider are bound to depend on the answer he or she would give to the question what is 'meaning'? © Petros A. M. Gelepithis, 1984

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Usually facing problems lead us to try to find ways to tackle or explain them and, should we feel the need or find it more convenient, we give the class of problems we are concerned with a name, and the corresponding field of methods, explanations, hypotheses and beliefs -that is theories- another. This is not the case with 'meaning'. We do not have problems of 'meaning' as we have problems of balance; we only have a question "what is meaning?" and a number of different classes of questions depending on our answers to it. It seems therefore imperative to investigate our two new questions. First whether problems of 'meaning' in the sense described above do exist; second which of the available semantic paradigms, if any, gives an adequate theoretical account of 'meaning'. Before expressing our personal view on this question let us briefly present a few representative views on it. To start with there are a lot of researchers, for instance, philosophers or workers in AI, who have tried to give an implicit answer to our question; (see, for instance, Loar 1981; Ringle 1982; Rosenberg and Travis 1971). Although such answers may be turned to be highly motivating they actually beg our question by not providing any specific answer to it. To my knowledge there were only Chomsky (1957 and elsewhere), and Katz (1972) who explicitly described the goals or the problems of a semantic theory. Chomsky’s statement of the goals of linguistic theory is very clear: "We have a collection of data regarding sound-meaning correspondence, the form and interpretation of linguistic expressions, in various languages. We try to determine, for each language, a system of rules that will account for such data. More deeply we try to establish the principles that govern the formation of such systems of rules for any language." Katz on the other hand was meticulous in trying to delineate the range of phenomena that comprise semantics. In his "Semantic theory" he tried to demarcate the domain for a theory of semantics by specifying fifteen "subquestions" of the basic question 'what is meaning?', each of which corresponds to a class of phenomena a semantic theory has to explain. Finally we should notice that since our question was an existential one we are entirely justify to confine our presentation to only one class of investigators, namely linguists, as far as their answers prove that a positive answer is the case. Nevertheless, © Petros A. M. Gelepithis, 1984

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workers in other disciplines, for instance psychology, have also pointed to different problems in the area of semantics. This diversity of semantic problems within disciplinary boundaries, though effectively sealing semantics against ‘no-object’ of investigation criticisms, indicates, at the same time, the difficulties for a comprehensive account of meaning not confined in one particular discipline. This remark promptly brings us to the consideration of our second question concerning the adequacy of existing semantic frameworks and the requirements for a unified semantic framework. It is generally agreed that there is no adequate theory of meaning; (see, for instance, Katz and Fodor 1964; Palmer 1981; Quinton 1977; Weinreich 1961*1966). Furthermore, it should be made explicit that it is not only a unified, (that is, interdisciplinary), semantic framework that is lacking; theoretical inadequacy is the case for each and every disciplinary framework (see, for example, the competing approaches in linguistics or philosophy as outlined in section 3.2). It follows that it should be expected that the requirements for a theory of meaning will be as diverse as the problems specified and the approaches chosen. This is indeed the case. In what follows we shall give a few examples of such varying requirements concluding that, despite the difficulties, and they are many and great, a theory of meaning in the established sense of the word theory can not be ruled out. Our examples have been drawn upon the disciplines of philosophy and neurobiology; it was felt that these two disciplines may illustrate best the variety and divergence of the requirements sought. So, Dummett (1975), makes the remark that a theory of meaning for an entire language would require specifying the meaning of everything spoken or written in that language- a remark that is indeed traceable back to Bloomfield (1933) with the further remark that such an understanding is not yet feasible. A less incomplete requirement has been, more recently, put forward

by

Davidson. In "Reality without reference" he states: "I propose to call a theory a theory of meaning for a natural language L if it is such that (a) knowledge of the theory suffices for understanding the utterances of speakers of L and (b) the theory can be given empirical application by appeal to evidence described without using linguistic concepts, or at least without using linguistic concepts specific to the sentences and words of L." A justification for the slightly unfavourable way of presenting the philosophical © Petros A. M. Gelepithis, 1984

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requirements, may be granted, in view of the minimal criteria for a theory to account for higher brain functions proposed by Edelman. He writes: "1. The theory must be consistent with neuroanatomical, embryological, and neurophysiological information. 2. It must account for the distributive properties of memory and learning, for associative recall, as well as for the temporal properties and temporal "scale" of recall. 3. It must permit updating of memory to accord with current inputs. 4. It must reflect the main functions of higher brain systems as mediators between action and experience. 5. It must provide the necessary, if not the sufficient, conditions for awareness.” (Edelman 1978 p 52). Obviously the requirements stated are anything but incompatible. The difficulty arises only when workers concerned with meaning in the various disciplines are not broadminded enough to, at least, try to consider and incorporate such varying requirements in their own particular approaches. Naturally one could have been even more rigorous demanding more, or more stringent, requirements. For instance one may ask for specification of correspondence rules for any proposed theory, or for delineating the range of semantic phenomena in such a way that it would be accepted by all workers in semantics. Reasonable as these requirements are, they are also, I believe, beyond present realization and therefore not in need of presently being pursued. To sum up: having distinguished between belief systems and theories we come to think that there is no reason against the possibility for a unified theory of meaning; furthermore it seems advisable to try to develop a theory of meaning inductively. To this end the next section is devoted. 5.2 Towards a theory for the semantic structure of the human brain We saw, in the previous section, that a theory of meaning, in the classical sense of the word theory, is not impossible. Our primary concern here will be to consider whether a concrete mathematical model can be justified as a representation for human meanings. It should be stressed, once more, that human meanings lie in the core of many © Petros A. M. Gelepithis, 1984

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phenomena such as thought, memory, understanding etc. It follows that a discussion confined only to meaning will be, by necessity, impaired (N15). Before going on to develop our ideas I want to make clear two points. One, the word `linguistic' always refers, in this study, to human language; two, it is intentionally used to exclude non-linguistic events. I take the first point to be a justifiable restriction of our field of investigation and I, therefore, confine myself to one or two remarks concerning our second point. To be sure by excluding all nonlinguistic events we exclude one of the uses of the verb mean, namely, to be a sign of. Furthermore there are workers in the field of semantics (most notably Grice) who have based their views on such a distinction. I believe nevertheless that such a distinction is not important if one is not restricted to the English language alone. For one thing the distinction is not present in all languages (e.g. Greek). This remark of course is not to imply that non-linguistic events are not important or 'consequencefull'; on the contrary. It was only made to justifiably delineate our field of investigation. And with these remarks we come to our theme. To try to develop a theory inductively the best place to start, I think, is from generally accepted facts. In our case such a fact is the existence of meaningful linguistic expressions. Our fundamental question is: 'What are the meanings of linguistic expressions?' or to put in what Alston (1964 p 10) calls the canonical form of the meaning problem "what are we saying about a linguistic expression when we specify its meaning?". It might have been interesting if we were able to recall the processes through which we reached our answer to our original question. That not being possible I prefer to first state our answer and then justify it. So we define: the meaning m of a linguistic expression l in the context Cl for the human h at time t- symbol m(l, Cl, h, t)- is the prevailed neural formation of h, corresponding to l in Cl, at t- symbol Cbp. Let us start with explaining two key words in our definition: The term `neural formation' refers to a neural formation, Cb, in the human brain, and the latter, in turn, has been conceived to correspond to the context, Cl, of a linguistic expression l. On the other hand, `prevailed' has been conceived to: (a) symbolize the particular neural formation, (the prevailed neural formation), which has, eventually, gained control over its neighbours (neighbouring neural formations); and (b) indicate the neural (not necessarily only neuronal) processes which are going on to result in, eventually, the prevailed neural formation Cbp. © Petros A. M. Gelepithis, 1984

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We are now faced with two questions. First, what mathematical notion, if any, could represent Cbp? Second, assuming that our first question is answered to the affirmative, could the neural processes indicated in (b) above be also represented mathematically? With respect to our first question there are two observations which rather strongly suggest an answer to the affirmative. One, since meaning has been identified with some psychological (brain) state it seems plausible that linguistic context should be also identified with a neural grouping and, therefore, the mathematical notion of neighbourhood seems to me to become almost a must as its theoretical construct. The second observation concerns the structure of the human brain. It was felt that its hierarchical organization and the complexity of its neural interconnections would be adequately represented by the topological notion of a neighbourhood structure. Based on these observations we shall go on to postulate that Cbp can be represented by the topological notion of neighbourhood, and proceed to consider whether the semantic structure of the human brain can be represented by a neighbourhood structure. We should make clear at this point that the linguistic context Cl of a linguistic expression l includes l, and similarly that the neural formation Cb of a brain unit b includes b. Furthermore we should make clear our assumptions on the structure of human brain B. Human brain may be described either as (1) B1={si, cj, mk}; or as (2) B2={si} where si stands for the set of the subcellular units of B; cj stands for the cells of B; and mk stands for the neuronal microsystems of B. The indices i, j, k take values in the set of natural numbers. Now if m(l, Cl, h, t)=Cbp; what does b correspond to? I think that perhaps the most justifiable correspondence of b is to the form of l. That form being possibly, for instance, the sound or the ink mark of l. The question now raised is what b is identified with? As it was remarked in the paragraph above the question is empirical; we nevertheless may make a tentative guess. Tentatively, therefore, we assume that b belongs to B1. Returning to our question we believe that since human brain is hierarchically organized and its cells are highly intermingled we are justified to assume that the set © Petros A. M. Gelepithis, 1984

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of

the semantic interconnections of the human brain, let us call it E, can be

represented by a neighbourhood system, say G. Our reasoning is as follows. Let B* be that subset of the human brain B which is anatomically identified with its semantic components. Then P(B*) is the first complexity level of the working B*, and P(P(B*)) is the second complexity level of it. In other words P(B*) and P(P(B*)) are identified with the functioning of B*. Following these assumptions we identify P(P(B*)) with E and we shall say that a neural configuration Cb, that is, a neural formation including b, belongs to P(B*), whereas the family of all neural configurations including b, (symbol Cb), belongs to P(P(B*)), that is, E. Now, as it was remarked in the introduction to chapter five, we are not concerned with the ways that E was (or is) created, but only with the possibility of its topological representation. Therefore, we assume that a creating process, let us call it D, has given rise to E (operated on B*). And the crucial question now is: can D, E, Cb, etc. satisfy the requirements for a neighbourhood system G? To start with let us state a definition from general topology (see, for instance, Thron 1966). Def. 1: Let X be a given set and let F be a function from X to P(P(X))=G. If F satisfies the following requirements, (a) for every x that belongs to X, F(x) is not empty, and x belongs to Ux for every Ux that belongs to F(x); (b) if A is a subset of X, A superset of Ux, and Ux belongs to F(x), then A belongs to F(x); (c) if Ux belongs to F(x), and Vx belongs to F(x), then the `intersection' of Ux and Vx belongs also to F(x); (d) if Ux belongs to F(x), there exists a Vx that belongs to F(x), Vx