James H. Fetzer

Program verification: the very idea

The notion of program verification appears to trade upon an equivocation. Algorithms, as logical structures, are appropriate subjects for deductive verification. Programs, as causal models of those structures, are not. The success of program verification as a generally applicable and completely reliable method for guaranteeing program performance is not even a theoretical possibility.

Aspects of Artificial Intelligence

Prologue.- The Semantics of Clocks.- I / Ontological Foundations.- The Pseudorealization Fallacy and the Chinese Room Argument.- In Praise of Narrow Minds: The Frame Problem.- Syntactic Semantics: Foundations of Computational Natural-Language Understanding.- Signs and Minds: An Introduction to the Theory of Semiotic Systems.- Logic for the New AI.- II / Epistemological Dimensions.- Artificial Intelligence is Philosophy.- Artificial Intelligence as an Experimental Science.- Defeasible Reasoning: A Philosophical Analysis in Prolog.- When is Reasoning Nonmonotonic?.- Artificial Intelligence and Effective Epistemology.- Maintaining an Inductive Database.- Epilogue.- Automating Creativity.- Index of Names.- Index of Subject.

Language and Mentality: Computational, Representational, and Dispositional Conceptions

The purpose of this paper is to explore three alternative frameworks for understanding the nature of language and mentality, which accent syntactical, semantical, and pragmatical aspects of the phenomena with which they are concerned, respectively. Although the computational conception currently exerts considerable appeal, its defensibility appears to hing upon an extremely implausible theory of the relation of form to content. Similarly, while the representational approach has much to recommend it, its range is essentially restricted to those units of language that can be understood in terms of undefined units. Thus, the only alternative among these three that can account for the meaning of primitive units of language is one emphasizing the basic role of skills, habits, and tendencies in relating signs and dispositions.

Definitions and definability : philosophical perspectives

Aspects of the Theory of Definition.- I / Preliminary Considerations.- Real and Nominal Definitions.- Primitive Concepts: Habits, Conventions, and Laws.- II / Definitional Desiderata.- Vagueness and the Desiderata for Definition.- Definition in a Quinean World.- III / Formal Developments.- Definitions and Definability.- Towards a General Theory of Identifiability.- IV / Epistemic Dimensions.- Epistemic Terms and the Aims of Epistemology.- Rational Definitions and Defining Rationality.- V / Specialized Conceptions.- Idealized Definitions in Physics and Idealized Dispositions.- Inverted Definitions and Their Uses.- VI / Disciplinary Conceptions.- Definitions in Law.- Defining the Divine.- Philosophical Analyses: An Explanation and Defense.- Index of Names.- Index of Subjects.

Information: Does it Have To Be True?

Luciano Floridi (2003) offers a theory of information as a “strongly semantic” notion, according to which information encapsulates truth, thereby making truth a necessary condition for a sentence to qualify as “information”. While Floridi provides an impressive development of this position, the aspects of his approach of greatest philosophical significance are its foundations rather than its formalization. He rejects the conception of information as meaningful data, which entails at least three theses – that information can be false; that tautologies are information; and, that “It is true that ...” is non-redundant – appear to be defensible. This inquiry offers various logical, epistemic, and ordinary-language grounds to demonstrate that an account of his kind is too narrow to be true and that its adoption would hopelessly obscure crucial differences between information, misinformation, and disinformation.

Signs and Minds: An Introduction to the Theory of Semiotic Systems

Perhaps no other view concerning the theoretical foundations of artificial intelligence has been as widely accepted or as broadly influential as the physical symbol system conception advanced by Newell and Simon (1976), where symbol systems are machines — possibly human — that process symbolic structures through time. From this point of view, artificial intelligence deals with the development and evolution of physical systems that employ symbols to represent and to utilize information or knowledge, a position often either explicitly endorsed or tacitly assumed by authors and scholars at work within this field (cf. Nii et al., 1982 and Buchanan 1985). Indeed, this perspective has been said to be “the heart of research in artificial intelligence” (Rich 1983, p. 3), a view that appears to be representative of its standing within the community at large.

A World of Dispositions

Perhaps the fundamental concepts of physical ontology are those of objects and events; for it is widely assumed that the world itself is amenable to being characterized successfully by means of an event ontology or an object ontology, where the outstanding difficulty is simply one of finding the right sort of fit. Although these pathways have seemed promising, they have not been without their own distinctive difficulties, for despite an area of agreement concerning suitable criteria for the individuation of objects, substantial disagreement abounds regarding appropriate standards for the differentiation of events.1 This matter is consequential for both perspectives, moreover, since whether objects are to be constructed from events or events from objects, neither view presumes either category alone provides a sufficient foundation for an adequate ontology.2 The problems which they share have resisted successful explication, nevertheless.

Thinking and Computing: Computers as Special Kinds of Signs

Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing.

SOCIOBIOLOGY AND EPISTEMOLOGY

Prologue.- Gene-Culture Coevolution: Humankind in the Making.- I. Sociobiological Conceptions.- Sociobiology and the Information Metaphor.- Phenotypic Plasticity, Cultural Transmission, and Human Sociobiology.- Sociobiology and Human Culture.- Evolutionary Biology, Human Nature, and Knowledge.- Love and Morality: The Possibility of Altruism.- II. Epistemological Reflections.- Biological Reductionism and Genic Selectionism.- Adaptationalist Imperatives and Panglossian Paradigms.- Methodological Behaviorism, Evolution, and Game Theory.- Sociobiological Explanation and the Testability of Sociobiological Theory.- Science and Sociobiology.- Epilogue.- Evolutionary Epistemology: Can Sociobiology Help?.- Index of Names.- Index of Subjects.

Disinformation: The Use of False Information

The distinction between misinformation and disinformation becomes especially important in political, editorial, and advertising contexts, where sources may make deliberate efforts to mislead, deceive, or confuse an audience in order to promote their personal, religious, or ideological objectives. The difference consists in having an agenda. It thus bears comparison with lying, because “lies” are assertions that are false, that are known to be false, and that are asserted with the intention to mislead, deceive, or confuse. One context in which disinformation abounds is the study of the death of JFK, which I know from more than a decade of personal research experience. Here I reflect on that experience and advance a preliminary “theory of disinformation” that is intended to stimulate thinking on this increasingly important subject. Five kinds of disinformation are distinguished and exemplified by real life cases I have encountered. It follows that the story you are about to read is true.