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  1. Why Does AI Lie So Much? The Problem Is More Deep Rooted Than You Think.Mir H. S. Quadri - 2024 - Arkinfo Notes.
    The rapid advancements in artificial intelligence, particularly in natural language processing, have brought to light a critical challenge, i.e., the semantic grounding problem. This article explores the root causes of this issue, focusing on the limitations of connectionist models that dominate current AI research. By examining Noam Chomsky's theory of Universal Grammar and his critiques of connectionism, I highlight the fundamental differences between human language understanding and AI language generation. Introducing the concept of semantic grounding, I emphasise the need for (...)
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  2. Intelligence, from Natural Origins to Artificial Frontiers - Human Intelligence vs. Artificial Intelligence.Nicolae Sfetcu - 2024 - Bucharest, Romania: MultiMedia Publishing.
    The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand and reproduce intelligence, AI has emerged (...)
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  3. Deep learning and synthetic media.Raphaël Millière - 2022 - Synthese 200 (3):1-27.
    Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of synthetic audiovisual (...)
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  4. The Unbearable Shallow Understanding of Deep Learning.Alessio Plebe & Giorgio Grasso - 2019 - Minds and Machines 29 (4):515-553.
    This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...)
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  5. Advice seeking network structures and the learning organization.Jarle Aarstad, Marcus Selart & Sigurd Troye - 2011 - Problems and Perspectives in Management 9 (2):44-51.
    Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level of analysis (...)
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  6. (1 other version)The language of thought hypothesis.Murat Aydede - 2010 - Stanford Encyclopedia of Philosophy.
    A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...)
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  7. Logic in Cognitive Science: Bridging the Gap between Symbolic and Connectionist Paradigms.Alistair Isaac & Jakub Szymanik - 2010 - Journal of the Indian Council of Philosophical Research (2):279-309.
    This paper surveys applications of logical methods in the cognitive sciences. Special attention is paid to non-monotonic logics and complexity theory. We argue that these particular tools have been useful in clarifying the debate between symbolic and connectionist models of cognition.
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  8. Connectionist computation.Gualtiero Piccinini - 2007 - In Proceedings of the 2007 International Joint Conference on Neural Networks.
    The following three theses are inconsistent: (1) (Paradigmatic) connectionist systems perform computations. (2) Performing computations requires executing programs. (3) Connectionist systems do not execute programs. Many authors embrace (2). This leads them to a dilemma: either connectionist systems execute programs or they don't compute. Accordingly, some authors attempt to deny (1), while others attempt to deny (3). But as I will argue, there are compelling reasons to accept both (1) and (3). So, we should replace (2) with a more satisfactory (...)
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  9. Proceedings of the 2007 International Joint Conference on Neural Networks.Gualtiero Piccinini (ed.) - 2007
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  10. How do connectionist networks compute?Gerard O'Brien & Jonathan Opie - 2006 - Cognitive Processing 7 (1):30-41.
    Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its _computational_ credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation—no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine (...)
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  11. Paul Churchland.Brian L. Keeley (ed.) - 2005 - Cambridge: Cambridge University Press.
  12. Churchland on connectionism.Aarre Laakso & Garrison W. Cottrell - 2005 - In Brian L. Keeley (ed.), Paul Churchland. Cambridge: Cambridge University Press.
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  13. A.I., Scientific discovery and realism.Mario Alai - 2004 - Minds and Machines 14 (1):21-42.
    Epistemologists have debated at length whether scientific discovery is a rational and logical process. If it is, according to the Artificial Intelligence hypothesis, it should be possible to write computer programs able to discover laws or theories; and if such programs were written, this would definitely prove the existence of a logic of discovery. Attempts in this direction, however, have been unsuccessful: the programs written by Simon's group, indeed, infer famous laws of physics and chemistry; but having found no new (...)
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  14. Causal emergentism.Olga Markič - 2004 - Acta Analytica 19 (33):65-81.
    In this paper I describe basic features of traditional (British) emergentism and Popper’s emergentist theory of consciousness and compare them to the contemporary versions of emergentism present in connectionist approach in cognitive sciences. I argue that despite their similarities, the traditional form, as well as Popper’s theory belong to strong causal emergentism and yield radically different ontological consequences compared to the weaker, contemporary version present in cognitive science. Strong causal emergentism denies the causal closure of the physical domain and introduces (...)
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  15. The Newell test for a theory of cognition.John R. Anderson & Christian Lebiere - 2003 - Behavioral and Brain Sciences 26 (5):587-601.
    Newell proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a (...)
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  16. The case of the mysterious mind: Review of Radiant Cool, by Dan Lloyd. [REVIEW]Susan J. Blackmore - 2003 - New Scientist 13:36-39.
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  17. The Handbook of Brain Theory and Neural Networks, Second Edition.Michael A. Arbib (ed.) - 2002 - MIT Press.
    A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.
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  18. (1 other version)Philosophical issues in brain theory and connectionism.Andy Clark & Chris Eliasmith - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
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  19. (1 other version)Philosophical issues in brain theory and connectionism.Chris Eliasmith & Andy Clark - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
    In this article, we highlight three questions: (1) Does human cognition rely on structured internal representations? (2) How should theories, models and data relate? (3) In what ways might embodiment, action and dynamics matter for understanding the mind and the brain?
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  20. Radical connectionism: Thinking with (not in) language.Gerard O'Brien & Jonathan Opie - 2002 - Language and Communication 22 (3):313-329.
    In this paper we defend a position we call radical connectionism. Radical connectionism claims that cognition _never_ implicates an internal symbolic medium, not even when natural language plays a part in our thought processes. On the face of it, such a position renders the human capacity for abstract thought quite mysterious. However, we argue that connectionism is committed to an analog conception of neural computation, and that representation of the abstract is no more problematic for a system of analog vehicles (...)
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  21. Manfred Spitzer, the mind within the net. Models of learning, thinking, and acting.Kenneth Aizawa - 2001 - Minds and Machines 11 (3):445-448.
    A review of Manfred Spitzer's The mind within the net: Models of learning, thinking, and acting.
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  22. Connectionism today.Kim Plunkett - 2001 - Synthese 129 (2):185-194.
    Connectionist networks have been used to model a wide range of cognitivephenomena, including developmental, neuropsychological and normal adultbehaviours. They have offered radical alternatives to traditional accounts ofwell-established facts about cognition. The primary source of the success ofthese models is their sensitivity to statistical regularities in their trainingenvironment. This paper provides a brief description of the connectionisttoolbox and how this has developed over the past 2 decades, with particularreference to the problem of reading aloud.
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  23. Philosophy and Memory Traces: Descartes to Connectionism by John Sutton. [REVIEW]Monica Meijsing - 2000 - Isis 91:427-428.
  24. Filling the Gaps: Hume and Connectionism on the Continued Existence of Unperceived Objects.Mark Collier - 1999 - Hume Studies 25 (1 and 2):155-170.
    In Book I, part iv, section 2 of the Treatise, "Of scepticism with regard to the senses," Hume presents two different answers to the question of how we come to believe in the continued existence of unperceived objects. He rejects his first answer shortly after its formulation, and the remainder of the section articulates an alternative account of the development of the belief. The account that Hume adopts, however, is susceptible to a number of insurmountable objections, which motivates a reassessment (...)
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  25. Connectionism and novel combinations of skills: Implications for cognitive architecture. [REVIEW]Robert F. Hadley - 1999 - Minds and Machines 9 (2):197-221.
    In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just (...)
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  26. Beginning a theoretician-practitioner dialogue about connectionism.Dianne D. Horgan & Douglas J. Hacker - 1999 - Acta Analytica 144:261-273.
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  27. (1 other version)Short prcis of connectionism and the philosophy of psychology.Terence E. Horgan - 1999 - Acta Analytica 144:9-21.
  28. Authors' replies.Terence E. Horgan & John L. Tienson - 1999 - Acta Analytica 144:275-287.
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  29. Learning connectionist networks and the philosophy of psychology.Mary Litch - 1999 - Acta Analytica 144:87-110.
  30. Connectionism and the problem of consciousness.Ullin T. Place - 1999 - Acta Analytica 144:197-226.
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  31. Connectionist modelling strategies.Jonathan Opie - 1998 - Psycoloquy 9 (30).
    Green offers us two options: either connectionist models are literal models of brain activity or they are mere instruments, with little or no ontological significance. According to Green, only the first option renders connectionist models genuinely explanatory. I think there is a third possibility. Connectionist models are not literal models of brain activity, but neither are they mere instruments. They are abstract, IDEALISED models of the brain that are capable of providing genuine explanations of cognitive phenomena.
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  32. Connectionism and the causal theory of action explanation.Scott R. Sehon - 1998 - Philosophical Psychology 11 (4):511-532.
    It is widely assumed that common sense psychological explanations of human action are a species of causal explanation. I argue against this construal, drawing on Ramsey et al.'s paper, “Connectionism, eliminativism, and the future of folk psychology”. I argue that if certain connec-tionist models are correct, then mental states cannot be identified with functionally discrete causes of behavior, and I respond to some recent attempts to deny this claim. However, I further contend that our common sense psychological practices are not (...)
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  33. Locality, modularity, and computational neural networks.Horst Bischof - 1997 - Behavioral and Brain Sciences 20 (3):516-517.
    There is a distinction between locality and modularity. These two terms have often been used interchangeably in the target article and commentary. Using this distinction we argue in favor of a modularity. In addition we also argue that both PDP-type networks and box-and-arrow models have their own strengths and pitfalls.
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  34. Hayek: Economist and Social Philosopher: A Critical Retrospect.Stephen F. Frowen (ed.) - 1997 - St. Martin's Press.
    This volume provides a critical assessment of the wide spectrum of Hayek's celebrated work as economist and social philosopher. Included are papers on Hayek's early writings in the field of monetary economics, on which his later campaign against inflation, his controversial proposal for competing currencies, and his negative view of the impact of trade unions on the economy are based. Hayek's social philosophy, often regarded as the centre piece of his famous work, and the fundamental findings about human thinking, society, (...)
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  35. Connectionism and the Philosophical Foundations of Cognitive Science.Terence Horgan - 1997 - Metaphilosophy 28 (1-2):1-30.
    This is an overview of recent philosophical discussion about connectionism and the foundations of cognitive science. Connectionist modeling in cognitive science is described. Three broad conceptions of the mind are characterized, and their comparative strengths and weaknesses are discussed: (1) the classical computation conception in cognitive science; (2) a popular foundational interpretation of connectionism that John Tienson and I call “non‐sentential computationalism”; and (3) an alternative interpretation of connectionism we call “dynamical cognition.” Also discussed are two recent philosophical attempts to (...)
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  36. Modelling the noncomputational mind: Reply to Litch.Terence E. Horgan - 1997 - Philosophical Psychology 10 (3):365-371.
    I explain why, within the nonclassical framework for cognitive science we describe in the book, cognitive-state transitions can fail to be tractably computable even if they are subserved by a discrete dynamical system whose mathematical-state transitions are tractably computable. I distinguish two ways that cognitive processing might conform to programmable rules in which all operations that apply to representation-level structure are primitive, and two corresponding constraints on models of cognition. Although Litch is correct in maintaining that classical cognitive science is (...)
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  37. Computation, connectionism and modelling the mind.Mary Litch - 1997 - Philosophical Psychology 10 (3):357-364.
    Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several versions of connectionism one (...)
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  38. Connectionism and epistemic value.Nenad Miscevic - 1997 - Acta Analytica 12:19-37.
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  39. Connectionism and the form of rational norms.Herman E. Stark - 1997 - Acta Analytica 12:39-53.
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  40. What should a connectionist philosophy of science look like?William P. Bechtel - 1996 - In The Churchlands and Their Critics. Oxford University Press. pp. 121--144.
    The reemergence of connectionism2 has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations.
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  41. The Churchlands and Their Critics.William P. Bechtel - 1996 - Oxford University Press.
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  42. On Alan Turing's Anticipation of Connectionism.Jack Copeland & Diane Proudfoot - 1996 - Synthese 108:361-367.
    It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks 'unorganised machines'. By the application of what he described as 'appropriate interference, mimicking education' an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of 'neurons' is sufficient. Turing proposed simulating both the behaviour of the (...)
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  43. Connectionism and the Philosophy of Psychology.Terence Horgan & John Tienson - 1996 - MIT Press.
    In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition.
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  44. Connectionism, reduction, and multiple realizability.John Bickle - 1995 - Behavior and Philosophy 23 (2):29-39.
    I sketch a theory of cognitive representation from recent "connectionist" cognitive science. I then argue that (i) this theory is reducible to neuroscientific theories, yet (ii) its kinds are multiply realized at a neurobiological level. This argument demonstrates that multiple realizability alone is no barrier to the reducibility of psychological theories. I conclude that the multiple realizability argument, the most influential argument against psychophysical reductionism, should be abandoned.
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  45. (1 other version)Connectionism: Debates on Psychological Explanation.Andy Clark - 1995 - Cambridge: Blackwell.
  46. Connectionist minds.Andy Clark - 1995 - In Connectionism: Debates on Psychological Explanation. Cambridge: Blackwell. pp. 83 - 102.
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  47. Connectionism and the rationale constraint on cognitive explanations.Robert Cummins - 1995 - Philosophical Perspectives 9:105-25.
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  48. The philosophical import of connectionism: A critical notice of Andy Clark's associative engines.Manuel García-Carpintero - 1995 - Mind and Language 10 (4):370-401.
    Critical notice of Andy Clark's "Associative Engines".
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  49. Reduction and levels of explanation in connectionism.John Sutton - 1995 - In P. Slezak, T. Caelli & R. Clark (eds.), Perspectives on Cognitive Science, Volume 1: Theories, Experiments, and Foundations. Ablex Publishing. pp. 347-368.
    Recent work in the methodology of connectionist explanation has I'ocrrsccl on the notion of levels of explanation. Specific issucs in conncctionisrn hcrc intersect with rvider areas of debate in the philosophy of psychology and thc philosophy of science generally. The issues I raise in this chapter, then, are not unique to cognitive science; but they arise in new and important contexts when connectionism is taken seriously as a model of cognition. The general questions are the relation between levels and the (...)
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  50. On qualitative modelling.Jarmo J. Ahonen - 1994 - AI and Society 8 (1):17-28.
    Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.
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