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Artificial intelligence meets natural stupidity

In J. Haugel, Mind Design. MIT Press. pp. 5-18 (1981)

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  1. The Computer Revolution in Philosophy: Philosophy, Science, and Models of Mind.Aaron Sloman - 1978 - Hassocks UK: Harvester Press.
    Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets not having (...)
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  • Why Machines Will Never Rule the World: Artificial Intelligence without Fear.Jobst Landgrebe & Barry Smith - 2022 - Abingdon, England: Routledge.
    The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...)
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  • From simple associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic binding using temporal synchrony.Lokendra Shastri & Venkat Ajjanagadde - 1993 - Behavioral and Brain Sciences 16 (3):417-51.
    Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...)
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  • Anthropomorphism in AI.Arleen Salles, Kathinka Evers & Michele Farisco - 2020 - American Journal of Bioethics Neuroscience 11 (2):88-95.
    AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI’s functionalities and innovations are often anthropomorphized (i.e., described and conceived as characterized by human traits). The general public’s anthropomorphic attitudes and some (...)
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  • Intentionality and information processing: An alternative model for cognitive science.Kenneth M. Sayre - 1986 - Behavioral and Brain Sciences 9 (1):121-38.
    This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory (...)
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  • Syntactic semantics: Foundations of computational natural language understanding.William J. Rapaport - 1988 - In James H. Fetzer, Aspects of AI. D.
    This essay considers what it means to understand natural language and whether a computer running an artificial-intelligence program designed to understand natural language does in fact do so. It is argued that a certain kind of semantics is needed to understand natural language, that this kind of semantics is mere symbol manipulation (i.e., syntax), and that, hence, it is available to AI systems. Recent arguments by Searle and Dretske to the effect that computers cannot understand natural language are discussed, and (...)
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  • Echoes of myth and magic in the language of Artificial Intelligence.Roberto Musa Giuliano - 2020 - AI and Society 35 (4):1009-1024.
    To a greater extent than in other technical domains, research and progress in Artificial Intelligence has always been entwined with the fictional. Its language echoes strongly with other forms of cultural narratives, such as fairytales, myth and religion. In this essay we present varied examples that illustrate how these analogies have guided not only readings of the AI enterprise by commentators outside the community but also inspired AI researchers themselves. Owing to their influence, we pay particular attention to the similarities (...)
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  • (1 other version)The philosophy of computer science.Raymond Turner - 2013 - Stanford Encyclopedia of Philosophy.
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  • How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room.William J. Rapaport - 2006 - Minds and Machines 16 (4):381-436.
    A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS computational knowledge-representation system, (...)
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  • Syntax, Semantics, and Computer Programs.William J. Rapaport - 2020 - Philosophy and Technology 33 (2):309-321.
    Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.
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  • Intentionally: A problem of multiple reference frames, specificational information, and extraordinary boundary conditions on natural law.M. T. Turvey - 1986 - Behavioral and Brain Sciences 9 (1):153-155.
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  • An alternative to working on machine consciousness.Aaron Sloman - 2010 - International Journal of Machine Consciousness 2 (1):1-18.
    This paper extends three decades of work arguing that researchers who discuss consciousness should not restrict themselves only to (adult) human minds, but should study (and attempt to model) many kinds of minds, natural and artificial, thereby contributing to our understanding of the space containing all of them. We need to study what they do or can do, how they can do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets of requirements (...)
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  • Phenomenal and access consciousness and the "hard" problem: A view from the designer stance.Aaron Sloman - 2010 - International Journal of Machine Consciousness 2 (1):117-169.
    This paper is an attempt to summarise and justify critical comments I have been making over several decades about research on consciousness by philosophers, scientists and engineers. This includes (a) explaining why the concept of "phenomenal consciousness" (P-C), in the sense defined by Ned Block, is semantically flawed and unsuitable as a target for scientific research or machine modelling, whereas something like the concept of "access consciousness" (A-C) with which it is often contrasted refers to phenomena that can be described (...)
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  • Bodily structure and body representation.Adrian J. T. Alsmith - 2019 - Synthese 198 (3):2193-2222.
    This paper is concerned with representational explanations of how one experiences and acts with one’s body as an integrated whole. On the standard view, accounts of bodily experience and action must posit a corresponding representational structure: a representation of the body as an integrated whole. The aim of this paper is to show why we should instead favour the minimal view: given the nature of the body, and representation of its parts, accounts of the structure of bodily experience and action (...)
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  • Thought, language, and the argument from explicitness.Agustín Vicente & Fernando Martínez-Manrique - 2008 - Metaphilosophy 39 (3):381–401.
    This article deals with the relationship between language and thought, focusing on the question of whether language can be a vehicle of thought, as, for example, Peter Carruthers has claimed. We develop and examine a powerful argument—the "argument from explicitness"—against this cognitive role of language. The premises of the argument are just two: (1) the vehicle of thought has to be explicit, and (2) natural languages are not explicit. We explain what these simple premises mean and why we should believe (...)
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  • Intentionality and communication theory.K. M. Sayre - 1986 - Behavioral and Brain Sciences 9 (1):155-165.
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  • Managing Ambiguities at the Edge of Knowledge: Research Strategy and Artificial Intelligence Labs in an Era of Academic Capitalism.Steve G. Hoffman - 2017 - Science, Technology, and Human Values 42 (4):703-740.
    Many research-intensive universities have moved into the business of promoting technology development that promises revenue, impact, and legitimacy. While the scholarship on academic capitalism has documented the general dynamics of this institutional shift, we know less about the ground-level challenges of research priority and scientific problem choice. This paper unites the practice tradition in science and technology studies with an organizational analysis of decision-making to compare how two university artificial intelligence labs manage ambiguities at the edge of scientific knowledge. One (...)
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  • Semantic underdetermination and the cognitive uses of language.Agustín Vicente & Fernando Martínez-Manrique - 2005 - Mind and Language 20 (5):537–558.
    According to the thesis of semantic underdetermination, most sentences of a natural language lack a definite semantic interpretation. This thesis supports an argument against the use of natural language as an instrument of thought, based on the premise that cognition requires a semantically precise and compositional instrument. In this paper we examine several ways to construe this argument, as well as possible ways out for the cognitive view of natural language in the introspectivist version defended by Carruthers. Finally, we sketch (...)
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  • Semantic content: In defense of a network approach.Paul M. Churchland - 1986 - Behavioral and Brain Sciences 9 (1):139-140.
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  • Meinongian Semantics and Artificial Intelligence.William J. Rapaport - 2013 - Humana Mente 6 (25):25-52.
    This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semantic-network semantics. In particular, propositional semantic networks are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented.
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  • Intentionality and information theory.David P. Ellerman - 1986 - Behavioral and Brain Sciences 9 (1):143-144.
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  • Distributing structure over time.John E. Hummel & Keith J. Holyoak - 1993 - Behavioral and Brain Sciences 16 (3):464-464.
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  • Not an alternative model for intentionality in vision.R. Brown, D. C. Earle & S. E. G. Lea - 1986 - Behavioral and Brain Sciences 9 (1):138-139.
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  • Intentionality and the explanation of behavior.John Heil - 1986 - Behavioral and Brain Sciences 9 (1):146-147.
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  • Intrinsic versus contrived intentionality.Donald M. MacKay - 1986 - Behavioral and Brain Sciences 9 (1):149-150.
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  • Cognitive science and the pragmatics of behavior.Lawrence E. Marks - 1986 - Behavioral and Brain Sciences 9 (1):150-150.
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  • A total process approach to perception.Maxine Morphis - 1986 - Behavioral and Brain Sciences 9 (1):150-151.
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  • Intentionality: No mystery.William T. Powers - 1986 - Behavioral and Brain Sciences 9 (1):152-153.
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  • Intentionality as internality.Don Perlis & Rosalie Hall - 1986 - Behavioral and Brain Sciences 9 (1):151-152.
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  • Optimization and simplicity: Computational vision and biological explanation.Daniel J. Gilman - 1996 - Synthese 107 (3):293 - 323.
    David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for cognitive (...)
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  • Turing’s Three Senses of “Emotional”.Diane Proudfoot - 2014 - International Journal of Synthetic Emotions 5 (2):7-20.
    Turing used the expression “emotional” in three distinct ways: to state his philosophical theory of the concept of intelligence, to classify arguments for and against the possibility of machine intelligence, and to describe the education of a “child machine”. The remarks on emotion include several of the most important philosophical claims. This paper analyses these remarks and their significance for current research in Artificial Intelligence.
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  • Developing the explanatory dimensions of part–whole realization.Ronald Endicott - 2016 - Philosophical Studies 173 (12):3347-3368.
    I use Carl Gillett’s much heralded dimensioned theory of realization as a platform to develop a plausible part–whole theory. I begin with some basic desiderata for a theory of realization that its key terms should be defined and that it should be explanatory. I then argue that Gillett’s original theory violates these conditions because its explanatory force rests upon an unspecified “in virtue of” relation. I then examine Gillett’s later version that appeals instead to theoretical terms tied to “mechanisms.” Yet (...)
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  • Artificial Intelligence Inheriting the Historical Crisis in Psychology: An Epistemological and Methodological Investigation of Challenges and Alternatives.Mohamad El Maouch & Zheng Jin - 2022 - Frontiers in Psychology 13:781730.
    By following the arguments developed by Vygotsky and employing the cultural-historical activity theory (CHAT) in addition to dialectical logic, this paper attempts to investigate the interaction between psychology and artificial intelligence (AI) to confront the epistemological and methodological challenges encountered in AI research. The paper proposes that AI is facing an epistemological and methodological crisis inherited from psychology based on dualist ontology. The roots of this crisis lie in the duality between rationalism and objectivism or in the mind-body rupture that (...)
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  • From symbols to neurons: Are we there yet?Garrison W. Cottrell - 1993 - Behavioral and Brain Sciences 16 (3):454-454.
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  • Making a middling mousetrap.Michael R. W. Dawson & Istvan Berkeley - 1993 - Behavioral and Brain Sciences 16 (3):454-455.
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  • Computational and biological constraints in the psychology of reasoning.Mike Oaksford & Mike Malloch - 1993 - Behavioral and Brain Sciences 16 (3):468-469.
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  • Philosophy of Artificial Intelligence: A Course Outline.William J. Rapaport - 1986 - Teaching Philosophy 9 (2):103-120.
    In the Fall of 1983, I offered a junior/senior-level course in Philosophy of Artificial Intelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificial intelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased in the Philosophy Department’s (...)
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  • Synchronous Online Philosophy Courses: An Experiment in Progress.Fritz McDonald - 2018 - APA Newsletter on Philosophy and Computers 18 (1):37-40.
    There are two main ways to teach a course online: synchronously or asynchronously. In an asynchronous course, students can log on at their convenience and do the course work. In a synchronous course, there is a requirement that all students be online at specific times, to allow for a shared course environment. In this article, the author discusses the strengths and weaknesses of synchronous online learning for the teaching of undergraduate philosophy courses. The author discusses specific strategies and technologies he (...)
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  • Time phases, pointers, rules and embedding.John A. Barnden - 1993 - Behavioral and Brain Sciences 16 (3):451-452.
    This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...)
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  • Why some machines may need qualia and how they can have them (Including a demanding new Turing test for robot philosophers.).Aaron Sloman - unknown
    Many debates about consciousness appear to be endless, in part because of conceptual confusions preventing clarity as to what the issues are and what does or does not count as evidence. This makes it hard to decide what should go into a machine if it is to be described as 'conscious'. Thus, triumphant demonstrations by some AI developers may be regarded by others as proving nothing of interest because the system does not satisfy *their* definitions or requirements specifications.
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  • Plausible inference and implicit representation.Malcolm I. Bauer - 1993 - Behavioral and Brain Sciences 16 (3):452-453.
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  • Could static binding suffice?Paul R. Cooper - 1993 - Behavioral and Brain Sciences 16 (3):453-454.
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  • Communication theory and intentionality.John G. Daugman - 1986 - Behavioral and Brain Sciences 9 (1):140-141.
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  • Psychotherapy and Artificial Intelligence: A Proposal for Alignment.Flávio Luis de Mello & Sebastião Alves de Souza - 2019 - Frontiers in Psychology 10.
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  • Engineering's baby.Daniel C. Dennett - 1986 - Behavioral and Brain Sciences 9 (1):141-142.
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  • Reasoning, learning and neuropsychological plausibility.Joachim Diederich - 1993 - Behavioral and Brain Sciences 16 (3):455-456.
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  • Connectionism and syntactic binding of concepts.Georg Dorffner - 1993 - Behavioral and Brain Sciences 16 (3):456-457.
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  • Stalking intentionality.Fred I. Dretske - 1986 - Behavioral and Brain Sciences 9 (1):142-143.
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  • Dynamic bindings by real neurons: Arguments from physiology, neural network models and information theory.Reinhard Eckhorn - 1993 - Behavioral and Brain Sciences 16 (3):457-458.
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  • How Velmans' conscious experiences affected our brains.Ron Chrisley & Aaron Sloman - 2002 - Journal of Consciousness Studies 9 (11):58-62.
    Velmans’ paper raises three problems concerning mental causation: (1) How can consciousness affect the physical, given that the physical world appears causally closed? 10 (2) How can one be in conscious control of processes of which one is not consciously aware? (3) Conscious experiences appear to come too late to causally affect the processes to which they most obviously relate. In an appendix Velmans gives his reasons for refusing to resolve these problems through adopting the position (which he labels ‘physicalism’) (...)
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