This article is a sequel to the conversation on learning initiated by the editors of Educational Researcher in volume 25, number 4. The author’s first aim is to elicit the metaphors for learning that guide our work as learners, teachers, and researchers. Two such metaphors are identified: the acquisition metaphor and the participation metaphor. Subsequently, their entailments are discussed and evaluated. Although some of the implications are deemed desirable and others are regarded as harmful, the article neither speaks against a particular metaphor nor tries to make a case for the other. Rather, these interpretations and applications of the metaphors undergo critical evaluation. In the end, the question of theoretical unification of the research on learning is addressed, wherein the purpose is to show how too great a devotion to one particular metaphor can lead to theoretical distortions and to undesirable practices.

https://www.it.uu.se/edu/course/homepage/cosulearning/st11/reading/Sfard_ER1998.pdf

On Two Metaphors for Learning and the Dangers of Choosing Just One

Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic.

https://science.sciencemag.org/content/sci/263/5147/641.full.pdf

Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

羟氨苄青霉素引起大疱性类天疱疮样疹

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.

Program verification: the very idea

Philosophy and Cognitive Science

I: Metamentality.- 1. What is Artificial Intelligence?.- Criteria of Intelligence.- The Turing Test.- The Chinese Room.- The Korean Room.- Varieties of Intelligence.- Rationality.- Intentionality.- Humanity.- The Aim of this Inquiry.- Behaviorism.- Extensionality.- Reductionism.- 2. Symbol Systems and Semiotic Systems.- Peirce's Theory of Signs.- Abstract and Physical Systems.- Causal and Semiotic Systems.- The Varieties of Semiotic Systems.- Symbol and Causal Systems.- Symbol and Semiotic Systems.- The Symbol and Semiotic-System Hypotheses.- What about Humans and Machines?.- What Difference Does It Make?.- 3. Theories of Language and Mentality.- The Computational Conception.- The Parsing Criterion.- Ambiguity and Synonymy.- The Representational Conception.- The Language of Thought.- The Substitutional Criterion.- The Dispositional Conception.- A Theory of Cognition.- The Functional Role Criterion.- Fundamentally Different Systems.- II: Knowledge and Expertise.- 4. The Nature of Knowledge.- Conditions of Knowledge.- The Classic Definition.- Certainty vs. Fallibility.- A Refined Conception.- Language Frameworks.- Analytic Sentences.- A Priori Knowledge.- Modes of Modality.- The Condition of Truth.- Truth and Meaning.- Austin's Conventions.- Appropriate Beliefs.- 5. Varieties of Knowledge.- Ordinary Knowledge.- Common Sense.- Defeasible Reasoning.- Scientific Knowledge.- Laws of Nature.- Lawlike Sentences.- Maximal Specificity.- Epistemic Scorekeeping.- The Frame Problem.- 6. Expert Systems.- The General Conception.- The Choice of an Expert.- The Nature of Expertise.- Scientific Expertise.- Expert Production Systems.- Logical Structure.- Epistemic Contents.- Truth vs. Practicality.- Varieties of Expert Systems.- The Expert System.- The Knowledge-Processing System.- The Expert-Aided Knowledge-Processing System.- III: Representation and Verification.- 7. Knowledge Representation.- Semantic Networks.- Logical Structure.- Epistemic Contents.- Scripts and Frames.- Scripts as Sequences of Frames.- Scripts as Modes of Understanding.- Selecting a Representation Scheme.- Logical Structures.- Epistemic Contents.- Has the Role of Logic Been Misconceived?.- 8. Program Verification.- The Comparison with Mathematics.- Concepts of Verification.- Probable Verifications.- Two Kinds of Complexity.- Theorems, Algorithms, and Programs.- The Deeper Disanalogy.- Programs as Causal Models.- Abstract and Target Machines.- The Ambiguity of "Program Verification".- Programs as Applied Mathematics.- Testing Computer Programs.- Complexity and Reliability.- 9. Minds, Bodies, and Machines.- The Nature of Mind.- Human Cognition.- Machine Mentality.- Minds and Bodies.- Laws of Human Beings.- Laws of Digital Machines.- Other Minds.- Computers and Cognition.- The Cognitive Computer.- Why People Think Computers Can't.- References.- Numbered Definitions.- List of Figures.- Index of Names.- Index of Subjects.

Artificial Intelligence: Its Scope and Limits

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.

Disinformation: The Use of False Information

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.

James H. Fetzer

Papers

Program verification: the very idea

Philosophy and Cognitive Science

Artificial Intelligence: Its Scope and Limits

Disinformation: The Use of False Information

Aspects of Artificial Intelligence