Search results for 'accelerated Turing machine' (try it on Scholar)

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  1. B. Jack Copeland (2002). Accelerating Turing Machines. Minds and Machines 12 (2):281-300.score: 251.0
    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 effective procedures and (...)
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  2. Alan M. Turing (1950). Computing Machinery and Intelligence. Mind 59 (October):433-60.score: 240.0
    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 (...)
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  3. B. Jack Copeland & Oron Shagrir (2011). Do Accelerating Turing Machines Compute the Uncomputable? Minds and Machines 21 (2):221-239.score: 202.3
  4. Vincent C. Müller (2011). On the Possibilities of Hypercomputing Supertasks. Minds and Machines 21 (1):83-96.score: 201.0
    This paper investigates the view that digital hypercomputing is a good reason for rejection or re-interpretation of the Church-Turing thesis. After suggestion that such re-interpretation is historically problematic and often involves attack on a straw man (the ‘maximality thesis’), it discusses proposals for digital hypercomputing with Zeno-machines , i.e. computing machines that compute an infinite number of computing steps in finite time, thus performing supertasks. It argues that effective computing with Zeno-machines falls into a dilemma: either they are specified (...)
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  5. 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: 168.0
    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 (...)
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  6. Jack Copeland (1999). Beyond the Universal Turing Machine. Australasian Journal of Philosophy 77 (1):46-67.score: 160.0
    We describe an emerging field, that of nonclassical computability and nonclassical computing machinery. According to the nonclassicist, the set of well-defined computations is not exhausted by the computations that can be carried out by a Turing machine. We provide an overview of the field and a philosophical defence of its foundations.
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  7. S. G. Sterrett, Turing on the Integration of Human and Machine Intelligence.score: 144.0
    Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There (...)
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  8. Huma Shah & Kevin Warwick (2010). From the Buzzing in Turing’s Head to Machine Intelligence Contests. In TCIT 2010 / AISB 2010 Convention.score: 144.0
    This paper presents an analysis of three major contests for machine intelligence. We conclude that a new era for Turing’s test requires a fillip in the guise of a committed sponsor, not unlike DARPA, funders of the successful 2007 Urban Challenge.
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  9. Susan G. Sterrett (2012). Bringing Up Turing's 'Child-Machine'. In S. Barry Cooper (ed.), How the World Computes. 703--713.score: 144.0
    Turing wrote that the “guiding principle” of his investigation into the possibility of intelligent machinery was “The analogy [of machinery that might be made to show intelligent behavior] with the human brain.” [10] In his discussion of the investigations that Turing said were guided by this analogy, however, he employs a more far-reaching analogy: he eventually expands the analogy from the human brain out to “the human community as a whole.” Along the way, he takes note of an (...)
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  10. José Hernández-Orallo & David L. Dowe (2013). On Potential Cognitive Abilities in the Machine Kingdom. Minds and Machines 23 (2):179-210.score: 142.0
    Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be (...)
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  11. D. King (1996). Is the Human Mind a Turing Machine? Synthese 108 (3):379-89.score: 138.7
    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 (...)
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  12. B. Jack Copeland & Oron Shagrir (2007). Physical Computation: How General Are Gandy's Principles for Mechanisms? [REVIEW] Minds and Machines 17 (2):217-231.score: 133.0
    What are the limits of physical computation? In his ‘Church’s Thesis and Principles for Mechanisms’, Turing’s student Robin Gandy proved that any machine satisfying four idealised physical ‘principles’ is equivalent to some Turing machine. Gandy’s four principles in effect define a class of computing machines (‘Gandy machines’). Our question is: What is the relationship of this class to the class of all (ideal) physical computing machines? Gandy himself suggests that the relationship is identity. We do not (...)
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  13. B. Jack Copeland (2000). Narrow Versus Wide Mechanism: Including a Re-Examination of Turing's Views on the Mind-Machine Issue. Journal of Philosophy 97 (1):5-33.score: 120.0
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  14. Alan Mathison Turing (2012). Alan Turing's Systems of Logic: The Princeton Thesis. Princeton University Press.score: 120.0
     
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  15. Carol E. Cleland (2002). On Effective Procedures. Minds and Machines 12 (2):159-179.score: 116.0
    Since the mid-twentieth century, the concept of the Turing machine has dominated thought about effective procedures. This paper presents an alternative to Turing's analysis; it unifies, refines, and extends my earlier work on this topic. I show that Turing machines cannot live up to their billing as paragons of effective procedure; at best, they may be said to provide us with mere procedure schemas. I argue that the concept of an effective procedure crucially depends upon distinguishing (...)
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  16. Matjaz Gams (2002). The Turing Machine May Not Be the Universal Machine. Minds and Machines 12 (1):137-142.score: 114.0
    Can mind be modeled as a Turing machine? If you find such questions irrelevant, e.g. because the subject is already exhausted, then you need not read the book Mind versus Computer (Gams et al., 1991). If, on the other hand, you do find such questions relevant, then perhaps you need not read Dunlop's review of the book (Dunlop, 2000). (...).
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  17. P. D. Welch (2000). Eventually Infinite Time Turing Machine Degrees: Infinite Time Decidable Reals. Journal of Symbolic Logic 65 (3):1193-1203.score: 112.0
    We characterise explicitly the decidable predicates on integers of Infinite Time Turing machines, in terms of admissibility theory and the constructible hierarchy. We do this by pinning down ζ, the least ordinal not the length of any eventual output of an Infinite Time Turing machine (halting or otherwise); using this the Infinite Time Turing Degrees are considered, and it is shown how the jump operator coincides with the production of mastercodes for the constructible hierarchy; further that (...)
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  18. Daniel King (2003). Cartesian Dualism, and the Universe as Turing Machine. Philosophy Today 47 (2):138-146.score: 112.0
    In the field of computability and algorithmicity, there have recently been two essays that are of great interest: Peter Slezak's "Descartes's Diagonal Deduction," and David Deutsch's "Quantum Theory, the Church-Turing Principle and the Universal Quantum Computer." In brief, the former shows that Descartes' Cogito argument is structurally similar to Godel's proof that there are statements true but cannot be proven within a formal system such as Principia Mathematica, while Deutsch provides strong arguments for believing that the universe can be (...)
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  19. Joel David Hamkins (2002). Infinite Time Turing Machines. Minds and Machines 12 (4):567-604.score: 111.3
    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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  20. Storrs McCall (1999). Can a Turing Machine Know That the Godel Sentence is True? Journal of Philosophy 96 (10):525-32.score: 110.7
  21. James D. Heffernan (1978). Some Doubts About Turing Machine Arguments. Philosophy of Science 45 (December):638-647.score: 110.7
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  22. Chris Eliasmith (2002). The Myth of the Turing Machine: The Failings of Functionalism and Related Theses. Journal of Experimental and Theoretical Artificial Intelligence 14 (1):1-8.score: 109.3
    The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved (...)
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  23. Joel David Hamkins & Andy Lewis (2000). Infinite Time Turing Machines. Journal of Symbolic Logic 65 (2):567-604.score: 109.3
    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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  24. B. Jack Copeland & Diane Proudfoot (2000). What Turing Did After He Invented the Universal Turing Machine. Journal of Logic, Language and Information 9 (4):491-509.score: 108.0
    Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings (...)
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  25. Jack Copeland, Even Turing Machines Can Compute Uncomputable Functions.score: 102.7
    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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  26. Dale Jacquette (forthcoming). Computable Diagonalizations and Turing's Cardinality Paradox. Journal for General Philosophy of Science:1-24.score: 102.0
    A. N. Turing’s 1936 concept of computability, computing machines, and computable binary digital sequences, is subject to Turing’s Cardinality Paradox. The paradox conjoins two opposed but comparably powerful lines of argument, supporting the propositions that the cardinality of dedicated Turing machines outputting all and only the computable binary digital sequences can only be denumerable, and yet must also be nondenumerable. Turing’s objections to a similar kind of diagonalization are answered, and the implications of the paradox for (...)
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  27. B. Edmonds (2000). The Constructibility of Artificial Intelligence (as Defined by the Turing Test). Journal of Logic, Language and Information 9 (4):419-424.score: 102.0
    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 an off-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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  28. Aurea Anguera de Sojo, Juan Ares, Juan A. Lara, David Lizcano, María A. Martínez & Juan Pazos (2013). Turing and the Serendipitous Discovery of the Modern Computer. Foundations of Science 18 (3):545-557.score: 102.0
    In the centenary year of Turing’s birth, a lot of good things are sure to be written about him. But it is hard to find something new to write about Turing. This is the biggest merit of this article: it shows how von Neumann’s architecture of the modern computer is a serendipitous consequence of the universal Turing machine, built to solve a logical problem.
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  29. Helena Granström & Bo Göranzon (2013). Turing's Man: A Dialogue. [REVIEW] AI and Society 28 (1):21-25.score: 102.0
    soft servants of durable material: they live without pretension in complicated relays and electrical circuits. Speed, docility are their strength. One asks: “What is 2 × 2?”—“Are you a machine?” They answer or refuse to answer, depending on what you demand. There are, however, other machines as well, more abstract automatons, bolder and more inaccessible, which eat their tape in mathematical formulae. They imitate in language. In infinite loops, farther and farther back in their retreat towards more subtle algorithms, (...)
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  30. 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: 100.0
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  31. Toby Ord, Hypercomputation: Computing More Than the Turing Machine.score: 97.3
    In this report I provide an introduction to the burgeoning field of hypercomputation – the study of machines that can compute more than Turing machines. I take an extensive survey of many of the key concepts in the field, tying together the disparate ideas and presenting them in a structure which allows comparisons of the many approaches and results. To this I add several new results and draw out some interesting consequences of hypercomputation for several different disciplines.
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  32. Wilfried Sieg & John Byrnes, Generalizing Turing's Machine and Arguments.score: 96.0
    Wilfred Sieg and John Byrnes. Generalizing Turing's Machine and Arguments.
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  33. Santosh Putchala & Nikhil Agarwal (2011). Machine Vision: An Aid in Reverse Turing Test. [REVIEW] AI and Society 26 (1):95-101.score: 96.0
    Information security is perceived as an important and vital aspect for the survival of any business. Preserving user identity and limiting the access of web resources only to the humans and restricting ‘bots’ is an ever challenging area of study. With the increase in computing power and development of newer approaches towards circumvention and reverse-engineering, the recognition gap present between the machines and the humans is said to be decreasing. Turing test and its modified versions are in place to (...)
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  34. D. E. Seabold & J. D. Hamkins (2001). Infinite Time Turing Machines With Only One Tape. Mathematical Logic Quarterly 47 (2):271-287.score: 96.0
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  35. Iain A. Stewart (1996). The Demise of the Turing Machine in Complexity Theory. In P. J. R. Millican & A. Clark (eds.), Machines and Thought: The Legacy of Alan Turing, Volume 1. Clarendon Press.score: 96.0
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  36. Justin Leiber (2006). Turing's Golden: How Well Turing's Work Stands Today. Philosophical Psychology 19 (1):13-46.score: 94.0
    A. M. Turing has bequeathed us a conceptulary including 'Turing, or Turing-Church, thesis', 'Turing machine', 'universal Turing machine', 'Turing test' and 'Turing structures', plus other unnamed achievements. These include a proof that any formal language adequate to express arithmetic contains undecidable formulas, as well as achievements in computer science, artificial intelligence, mathematics, biology, and cognitive science. Here it is argued that these achievements hang together and have prospered well in the 50 (...)
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  37. B. Jack Copeland & Oron Shagrir (2011). Do Accelerating Turing Machines Compute the Uncomputable? Minds and Machines 21 (2):221-239.score: 93.0
  38. Robert H. Kane (1966). Turing Machines and Mental Reports. Australasian Journal of Philosophy 44 (December):344-52.score: 90.7
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  39. Aaron Sloman (2002). The Irrelevance of Turing Machines to Artificial Intelligence. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press.score: 90.7
  40. Paul Richard Blum, Michael Polanyi: Can the Mind Be Represented by a Machine? Existence and Anthropology.score: 90.0
    On the 27th of October, 1949, the Department of Philosophy at the University of Manchester organized a symposium "Mind and Machine", as Michael Polanyi noted in his Personal Knowledge (1974, p. 261). This event is known, especially among scholars of Alan Turing, but it is scarcely documented. Wolfe Mays (2000) reported about the debate, which he personally had attended, and paraphrased a mimeographed document that is preserved at the Manchester University archive. He forwarded a copy to Andrew Hodges (...)
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  41. Jeremy Seligman (2002). The Scope of Turing's Analysis of Effective Procedures. Minds and Machines 12 (2):203-220.score: 89.3
    Turing's (1936) analysis of effective symbolic procedures is a model of conceptual clarity that plays an essential role in the philosophy of mathematics. Yet appeal is often made to the effectiveness of human procedures in other areas of philosophy. This paper addresses the question of whether Turing's analysis can be applied to a broader class of effective human procedures. We use Sieg's (1994) presentation of Turing's Thesis to argue against Cleland's (1995) objections to Turing machines and (...)
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  42. Gualtiero Piccinini (2007). Computational Modeling Vs. Computational Explanation: Is Everything a Turing Machine, and Does It Matter to the Philosophy of Mind? Australasian Journal of Philosophy 85 (1):93 – 115.score: 84.0
    According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.
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  43. Storrs McCall (1999). Can a Turing Machine Know That the Gödel Sentence is True? Journal of Philosophy 96 (10):525 - 532.score: 84.0
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  44. Philip K. Hooper (1966). The Undecidability of the Turing Machine Immortality Problem. Journal of Symbolic Logic 31 (2):219-234.score: 84.0
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  45. R. J. Nelson (1980). Turing Machine Arguments. Philosophy of Science 47 (4):630-633.score: 84.0
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  46. S. Ginsburg (1972). Review: J. Hartmanis, Context-Free Languages and Turing Machine Computations. [REVIEW] Journal of Symbolic Logic 37 (4):759-759.score: 84.0
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  47. Ariel Zylberberg, Stanislas Dehaene, Pieter R. Roelfsema & Mariano Sigman (2011). The Human Turing Machine: A Neural Framework for Mental Programs. Trends in Cognitive Sciences 15 (7):293-300.score: 84.0
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  48. Jiri Becvar (1968). Review: F. C. Hennie, One-Tape, Off-Line Turing Machine Computations. [REVIEW] Journal of Symbolic Logic 33 (1):119-120.score: 84.0
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  49. B. Jack Copeland & Richard Sylvan (1999). Beyond the Universal Turing Machine. Australasian Journal of Philosophy 77 (1):46-66.score: 84.0
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  50. H. B. Enderton (1975). Review: Shen Lin, Tibor Rado, Computer Studies of Turing Machine Problems; Allen H. Brady, The Conjectured Highest Scoring Machines for Rado's $Sum(K)$ for the Value $K = 4$; Milton W. Green, A Lower Bound on Rado's Sigma Function for Binary Turing Machines. [REVIEW] Journal of Symbolic Logic 40 (4):617-617.score: 84.0
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