Search results for 'Turing Machines' (try it on Scholar)

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  1. M. H. A. Newman, Alan M. Turing, Geoffrey Jefferson, R. B. Braithwaite & S. Shieber (2004). Can Automatic Calculating Machines Be Said to Think? In Stuart M. Shieber (ed.), The Turing Test: Verbal Behavior as the Hallmark of Intelligence. Mit Press.score: 300.0
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  2. Alan M. Turing (1950). Computing Machinery and Intelligence. Mind 59 (October):433-60.score: 160.0
    I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer (...)
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  3. B. Jack Copeland (2002). Accelerating Turing Machines. Minds and Machines 12 (2):281-300.score: 154.0
    Accelerating Turing machines are Turing machines of a sort able to perform tasks that are commonly regarded as impossible for Turing machines. For example, they can determine whether or not the decimal representation of contains n consecutive 7s, for any n; solve the Turing-machine halting problem; and decide the predicate calculus. Are accelerating Turing machines, then, logically impossible devices? I argue that they are not. There are implications concerning the nature of (...)
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  4. Joel David Hamkins (2002). Infinite Time Turing Machines. Minds and Machines 12 (4):567-604.score: 154.0
    Infinite time Turing machines extend the operation of ordinary Turing machines into transfinite ordinal time. By doing so, they provide a natural model of infinitary computability, a theoretical setting for the analysis of the power and limitations of supertask algorithms.
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  5. Robert H. Kane (1966). Turing Machines and Mental Reports. Australasian Journal of Philosophy 44 (December):344-52.score: 150.0
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  6. Aaron Sloman (2002). The Irrelevance of Turing Machines to Artificial Intelligence. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press.score: 150.0
  7. Joel David Hamkins & Andy Lewis (2000). Infinite Time Turing Machines. Journal of Symbolic Logic 65 (2):567-604.score: 148.0
    Infinite time Turing machines extend the operation of ordinary Turing machines into transfinite ordinal time. By doing so, they provide a natural model of infinitary computability, a theoretical setting for the analysis of the power and limitations of supertask algorithms.
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  8. D. King (1996). Is the Human Mind a Turing Machine? Synthese 108 (3):379-89.score: 132.0
    In this paper I discuss the topics of mechanism and algorithmicity. I emphasise that a characterisation of algorithmicity such as the Turing machine is iterative; and I argue that if the human mind can solve problems that no Turing machine can, the mind must depend on some non-iterative principle — in fact, Cantor's second principle of generation, a principle of the actual infinite rather than the potential infinite of Turing machines. But as there has been theorisation (...)
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  9. S. Harnad (2000). Minds, Machines and Turing. Journal of Logic, Language and Information 9 (4):425-445.score: 126.0
    Turing's celebrated 1950 paper proposes a very generalmethodological criterion for modelling mental function: total functionalequivalence and indistinguishability. His criterion gives rise to ahierarchy of Turing Tests, from subtotal (toy) fragments of ourfunctions (t1), to total symbolic (pen-pal) function (T2 – the standardTuring Test), to total external sensorimotor (robotic) function (T3), tototal internal microfunction (T4), to total indistinguishability inevery empirically discernible respect (T5). This is areverse-engineering hierarchy of (decreasing) empiricalunderdetermination of the theory by the data. Level t1 is clearly (...)
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  10. B. Jack Copeland & Oron Shagrir (2011). Do Accelerating Turing Machines Compute the Uncomputable? Minds and Machines 21 (2):221-239.score: 124.0
  11. Neil Tennant (2001). On Turing Machines Knowing Their Own Gödel-Sentences. Philosophia Mathematica 9 (1):72-79.score: 120.0
    Storrs McCall appeals to a particular true but improvable sentence of formal arithmetic to argue, by appeal to its irrefutability, that human minds transcend Turing machines. Metamathematical oversights in McCall's discussion of the Godel phenomena, however, render invalid his philosophical argument for this transcendentalist conclusion.
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  12. Jack Copeland, Even Turing Machines Can Compute Uncomputable Functions.score: 120.0
    Accelerated Turing machines are Turing machines that perform tasks commonly regarded as impossible, such as computing the halting function. The existence of these notional machines has obvious implications concerning the theoretical limits of computability.
     
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  13. Alan Mathison Turing (2012). Alan Turing's Systems of Logic: The Princeton Thesis. Princeton University Press.score: 120.0
     
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  14. D. E. Seabold & J. D. Hamkins (2001). Infinite Time Turing Machines With Only One Tape. Mathematical Logic Quarterly 47 (2):271-287.score: 118.0
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  15. Jack Copeland (1998). Super Turing-Machines. Complexity 4 (1):30-32.score: 116.0
    The tape is divided into squares, each square bearing a single symbol—'0' or '1', for example. This tape is the machine's general-purpose storage medium: the machine is set in motion with its input inscribed on the tape, output is written onto the tape by the head, and the tape serves as a short-term working memory for the results of intermediate steps of the computation. The program governing the particular computation that the machine is to perform is also stored on the (...)
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  16. Dina Goldin & Peter Wegner (2008). The Interactive Nature of Computing: Refuting the Strong Church–Turing Thesis. [REVIEW] Minds and Machines 18 (1):17-38.score: 114.0
    The classical view of computing positions computation as a closed-box transformation of inputs (rational numbers or finite strings) to outputs. According to the interactive view of computing, computation is an ongoing interactive process rather than a function-based transformation of an input to an output. Specifically, communication with the outside world happens during the computation, not before or after it. This approach radically changes our understanding of what is computation and how it is modeled. The acceptance of interaction as a new (...)
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  17. Jack Copeland (1998). Turing's o-Machines, Searle, Penrose, and the Brain. Analysis 58 (2):128-138.score: 108.0
    In his PhD thesis (1938) Turing introduced what he described as 'a new kind of machine'. He called these 'O-machines'. The present paper employs Turing's concept against a number of currently fashionable positions in the philosophy of mind.
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  18. Storrs McCall (1999). Can a Turing Machine Know That the Godel Sentence is True? Journal of Philosophy 96 (10):525-32.score: 102.0
  19. James D. Heffernan (1978). Some Doubts About Turing Machine Arguments. Philosophy of Science 45 (December):638-647.score: 102.0
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  20. Carol E. Cleland (1993). Is the Church-Turing Thesis True? Minds and Machines 3 (3):283-312.score: 98.0
    The Church-Turing thesis makes a bold claim about the theoretical limits to computation. It is based upon independent analyses of the general notion of an effective procedure proposed by Alan Turing and Alonzo Church in the 1930''s. As originally construed, the thesis applied only to the number theoretic functions; it amounted to the claim that there were no number theoretic functions which couldn''t be computed by a Turing machine but could be computed by means of some other (...)
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  21. Hava T. Siegelmann (2003). Neural and Super-Turing Computing. Minds and Machines 13 (1):103-114.score: 98.0
    ``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation'', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that (...)
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  22. B. Maclennan (2003). Transcending Turing Computability. Minds and Machines 13 (1):3-22.score: 98.0
    It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing-machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or (...)
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  23. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.score: 96.0
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  24. Y. Sato & T. Ikegami (2004). Undecidability in the Imitation Game. Minds and Machines 14 (2):133-43.score: 96.0
    This paper considers undecidability in the imitation game, the so-called Turing Test. In the Turing Test, a human, a machine, and an interrogator are the players of the game. In our model of the Turing Test, the machine and the interrogator are formalized as Turing machines, allowing us to derive several impossibility results concerning the capabilities of the interrogator. The key issue is that the validity of the Turing test is not attributed to the (...)
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  25. Oron Shagrir (2002). Effective Computation by Humans and Machines. Minds and Machines 12 (2):221-240.score: 96.0
    There is an intensive discussion nowadays about the meaning of effective computability, with implications to the status and provability of the Church–Turing Thesis (CTT). I begin by reviewing what has become the dominant account of the way Turing and Church viewed, in 1936, effective computability. According to this account, to which I refer as the Gandy–Sieg account, Turing and Church aimed to characterize the functions that can be computed by a human computer. In addition, Turing provided (...)
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  26. Edwin J. Beggs, José Félix Costa & John V. Tucker (2010). Physical Oracles: The Turing Machine and the Wheatstone Bridge. Studia Logica 95 (1/2):279 - 300.score: 96.0
    Earlier, we have studied computations possible by physical systems and by algorithms combined with physical systems. In particular, we have analysed the idea of using an experiment as an oracle to an abstract computational device, such as the Turing machine. The theory of composite machines of this kind can be used to understand (a) a Turing machine receiving extra computational power from a physical process, or (b) an experimenter modelled as a Turing machine performing a test (...)
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  27. Giangiacomo Gerla (1989). Turing L -Machines and Recursive Computability for L -Maps. Studia Logica 48 (2):179 - 192.score: 96.0
    We propose the notion of partial recursiveness and strong partial recursiveness for fuzzy maps. We prove that a fuzzy map f is partial recursive if and only if it is computable by a Turing fuzzy machine and that f is strongly partial recursive and deterministic if and only if it is computable via a deterministic Turing fuzzy machine. This gives a simple and manageable tool to investigate about the properties of the fuzzy machines.
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  28. Wilfried Sieg & John Byrnes, K-Graph Machines: Generalizing Turing's Machines and Arguments.score: 96.0
    Wilfred Sieg and John Byrnes. K-Graph Machines: Generalizing Turing's Machines and Arguments.
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  29. Stuart M. Shieber (2014). There Can Be No Turing-Test-Passing Memorizing Machines. Philosophers' Imprint 14 (16).score: 96.0
    Anti-behaviorist arguments against the validity of the Turing Test as a sufficient condition for attributing intelligence are based on a memorizing machine, which has recorded within it responses to every possible Turing Test interaction of up to a fixed length. The mere possibility of such a machine is claimed to be enough to invalidate the Turing Test. I consider the nomological possibility of memorizing machines, and how long a Turing Test they can pass. I replicate (...)
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  30. Wilfried Sieg & John Byrnes, Gödel, Turing, and K-Graph Machines.score: 96.0
    Wilfried Sieg and John Byrnes. Gödel, Turing, and K-Graph Machines.
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  31. Hector Zenil, Fernando Soler-Toscano & Joost J. Joosten (2012). Empirical Encounters with Computational Irreducibility and Unpredictability. Minds and Machines 22 (3):149-165.score: 96.0
    The paper presents an exploration of conceptual issues that have arisen in the course of investigating speed-up and slowdown phenomena in small Turing machines, in particular results of a test that may spur experimental approaches to the notion of computational irreducibility. The test involves a systematic attempt to outrun the computation of a large number of small Turing machines (3 and 4 state, 2 symbol) by means of integer sequence prediction using a specialized function for that (...)
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  32. B. Jack Copeland & Oron Shagrir (2011). Do Accelerating Turing Machines Compute the Uncomputable? Minds and Machines 21 (2):221-239.score: 96.0
  33. Pierre Wagner (2005). Wittgenstein et les machines de Turing. Revue de Métaphysique Et de Morale 2 (2):181-196.score: 96.0
    Dans l'un des rares textes de Wittgenstein où il est question des machines de Turing, l'auteur écrit que celles-ci « sont les hommes, qui calculent ». L'étrangeté apparente de l'aphorisme disparaît si l'on dissipe certaines confusions fréquentes relatives aux machines de Turing. Une fois éclairci le sens littéral de ce passage des Remarques sur la philosophie de la psychologie, on peut s'interroger sur ce que Wittgenstein veut dire, dans le § 1096. Comme souvent chez l'auteur, on (...)
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  34. Peter Millican & Andy Clark (eds.) (1999). Machines and Thought: The Legacy of Alan Turing, Volume I. Clarendon Press.score: 96.0
    This is the first of two volumes of essays in commemoration of Alan Turing, whose pioneering work in the theory of artificial intelligence and computer science continues to be widely discussed today. A group of prominent academics from a wide range of disciplines focus on three questions famously raised by Turing: What, if any, are the limits on machine 'thinking'? Could a machine be genuinely intelligent? Might we ourselves be biological machines, whose thought consists essentially in nothing (...)
     
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  35. J. J. Clarke (1972). Turing Machines and the Mind-Body Problem. British Journal for the Philosophy of Science 23 (February):1-12.score: 94.0
  36. Bruce Edmonds (2000). The Constructability of Artificial Intelligence (as Defined by the Turing Test). Journal of Logic Language and Information 9 (4):419-424.score: 92.0
    The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), (...)
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  37. Crispin Wright (1995). Intuitionists Are Not (Turing) Machines. Philosophia Mathematica 3 (1):86-102.score: 92.0
    Lucas and Penrose have contended that, by displaying how any characterisation of arithmetical proof programmable into a machine allows of diagonalisation, generating a humanly recognisable proof which eludes that characterisation, Gödel's incompleteness theorem rules out any purely mechanical model of the human intellect. The main criticisms of this argument have been that the proof generated by diagonalisation (i) will not be humanly recognisable unless humans can grasp the specification of the object-system (Benacerraf); and (ii) counts as a proof only on (...)
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  38. Steven Pinker (2005). So How Does the Mind Work? Mind and Language 20 (1):1-38.score: 90.0
    In my book How the Mind Works, I defended the theory that the human mind is a naturally selected system of organs of computation. Jerry Fodor claims that 'the mind doesn't work that way'(in a book with that title) because (1) Turing Machines cannot duplicate humans' ability to perform abduction (inference to the best explanation); (2) though a massively modular system could succeed at abduction, such a system is implausible on other grounds; and (3) evolution adds nothing to (...)
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  39. Stevan Harnad (2000). Minds, Machines and Turing: The Indistinguishability of Indistinguishables. Journal of Logic, Language and Information 9 (4):425-445.score: 90.0
    Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination (...)
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  40. Michael Rescorla (2007). Church's Thesis and the Conceptual Analysis of Computability. Notre Dame Journal of Formal Logic 48 (2):253-280.score: 90.0
    Church's thesis asserts that a number-theoretic function is intuitively computable if and only if it is recursive. A related thesis asserts that Turing's work yields a conceptual analysis of the intuitive notion of numerical computability. I endorse Church's thesis, but I argue against the related thesis. I argue that purported conceptual analyses based upon Turing's work involve a subtle but persistent circularity. Turing machines manipulate syntactic entities. To specify which number-theoretic function a Turing machine computes, (...)
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  41. Luciano Floridi & Mariarosaria Taddeo (2009). Turing's Imitation Game: Still an Impossible Challenge for All Machines and Some Judges––an Evaluation of the 2008 Loebner Contest. [REVIEW] Minds and Machines 19 (1):145-150.score: 90.0
    An evaluation of the 2008 Loebner contest.
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  42. Aaron Sloman (2002). The Irrelevance of Turing Machines to AI. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press.score: 90.0
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  43. Vann McGee (1991). We Turing Machines Aren't Expected-Utility Maximizers (Even Ideally). Philosophical Studies 64 (1):115 - 123.score: 90.0
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  44. Pavel Tichý (1969). Intension in Terms of Turing Machines. Studia Logica 24 (1):7 - 25.score: 90.0
  45. Neil D. Jones & Alan L. Selman (1974). Turing Machines and the Spectra of First-Order Formulas. Journal of Symbolic Logic 39 (1):139-150.score: 90.0
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  46. Luciano Floridi, Mariarosaria Taddeo & Matteo Turilli (2008). Turing’s Imitation Game: Still an Impossible Challenge for All Machines and Some Judges. Minds and Machines 19 (1):145-150.score: 90.0
    An Evaluation of the 2008 Loebner Contest.
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  47. David Israel (2002). Reflections on Gödel's and Gandy's Reflections on Turing's Thesis. Minds and Machines 12 (2):181-201.score: 90.0
    We sketch the historical and conceptual context of Turing's analysis of algorithmic or mechanical computation. We then discuss two responses to that analysis, by Gödel and by Gandy, both of which raise, though in very different ways. The possibility of computation procedures that cannot be reduced to the basic procedures into which Turing decomposed computation. Along the way, we touch on some of Cleland's views.
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  48. Philip D. Welch (2004). On the Possibility, or Otherwise, of Hypercomputation. British Journal for the Philosophy of Science 55 (4):739-746.score: 90.0
    We claim that a recent article of P. Cotogno ([2003]) in this journal is based on an incorrect argument concerning the non-computability of diagonal functions. The point is that whilst diagonal functions are not computable by any function of the class over which they diagonalise, there is no ?logical incomputability? in their being computed over a wider class. Hence this ?logical incomputability? regrettably cannot be used in his argument that no hypercomputation can compute the Halting problem. This seems to lead (...)
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  49. Verónica Becher & Santiago Figueira (2005). Kolmogorov Complexity for Possibly Infinite Computations. Journal of Logic, Language and Information 14 (2):133-148.score: 90.0
    In this paper we study the Kolmogorov complexity for non-effective computations, that is, either halting or non-halting computations on Turing machines. This complexity function is defined as the length of the shortest input that produce a desired output via a possibly non-halting computation. Clearly this function gives a lower bound of the classical Kolmogorov complexity. In particular, if the machine is allowed to overwrite its output, this complexity coincides with the classical Kolmogorov complexity for halting computations relative to (...)
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  50. Joel David Hamkins & Alexei Miasnikov (2006). The Halting Problem Is Decidable on a Set of Asymptotic Probability One. Notre Dame Journal of Formal Logic 47 (4):515-524.score: 90.0
    The halting problem for Turing machines is decidable on a set of asymptotic probability one. The proof is sensitive to the particular computational models.
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