Results for 'Universal Turing machine'

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  1. The Turing Machine on the Dissecting Table.Jana Horáková - 2013 - Teorie Vědy / Theory of Science 35 (2):269-288.
    Since the beginning of the twenty-first century there has been an increasing awareness that software rep- resents a blind spot in new media theory. The growing interest in software also influences the argument in this paper, which sets out from the assumption that Alan M. Turing's concept of the universal machine, the first theoretical description of a computer program, is a kind of bachelor machine. Previous writings based on a similar hypothesis have focused either on a (...)
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  2. Turing Machines and Semantic Symbol Processing: Why Real Computers Don’t Mind Chinese Emperors.Richard Yee - 1993 - Lyceum 5 (1):37-59.
    Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent (...)
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  3. David Wolpert on impossibility, incompleteness, the liar paradox, the limits of computation, a non-quantum mechanical uncertainty principle and the universe as computer—the ultimate theorem in Turing Machine Theory.Michael Starks - manuscript
    I have read many recent discussions of the limits of computation and the universe as computer, hoping to find some comments on the amazing work of polymath physicist and decision theorist David Wolpert but have not found a single citation and so I present this very brief summary. Wolpert proved some stunning impossibility or incompleteness theorems (1992 to 2008-see arxiv.org) on the limits to inference (computation) that are so general they are independent of the device doing the computation, and even (...)
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  4. (1 other version)Wolpert, Chaitin and Wittgenstein on impossibility, incompleteness, the liar paradox, theism, the limits of computation, a non-quantum mechanical uncertainty principle and the universe as computer—the ultimate theorem in Turing Machine Theory (revised 2019).Michael Starks - 2019 - In Suicidal Utopian Delusions in the 21st Century -- Philosophy, Human Nature and the Collapse of Civilization-- Articles and Reviews 2006-2019 4th Edition Michael Starks. Las Vegas, NV USA: Reality Press. pp. 294-299.
    I have read many recent discussions of the limits of computation and the universe as computer, hoping to find some comments on the amazing work of polymath physicist and decision theorist David Wolpert but have not found a single citation and so I present this very brief summary. Wolpert proved some stunning impossibility or incompleteness theorems (1992 to 2008-see arxiv dot org) on the limits to inference (computation) that are so general they are independent of the device doing the computation, (...)
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  5. From Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine?Antoine Danchin & André A. Fenton - 2022 - Frontiers in Ecology and Evolution 10:796413.
    The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous (...)
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  6. Philosophy and Science, the Darwinian-Evolved Computational Brain, a Non-Recursive Super-Turing Machine & Our Inner-World-Producing Organ.Hermann G. W. Burchard - 2016 - Open Journal of Philosophy 6 (1):13-28.
    Recent advances in neuroscience lead to a wider realm for philosophy to include the science of the Darwinian-evolved computational brain, our inner world producing organ, a non-recursive super- Turing machine combining 100B synapsing-neuron DNA-computers based on the genetic code. The whole system is a logos machine offering a world map for global context, essential for our intentional grasp of opportunities. We start from the observable contrast between the chaotic universe vs. our orderly inner world, the noumenal cosmos. (...)
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  7. Reward-Punishment Symmetric Universal Intelligence.Samuel Allen Alexander & Marcus Hutter - 2021 - In Samuel Allen Alexander & Marcus Hutter (eds.), AGI.
    Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We show that if the background encodings and Universal Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the resulting Legg-Hutter intelligence measure is symmetric about the origin. In particular, this implies reward-ignoring agents have Legg-Hutter intelligence 0 according to such UTMs.
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  8. The Turing Guide.Jack Copeland, Jonathan Bowen, Robin Wilson & Mark Sprevak (eds.) - 2017 - Oxford: Oxford University Press.
    This volume celebrates the various facets of Alan Turing (1912–1954), the British mathematician and computing pioneer, widely considered as the father of computer science. It is aimed at the general reader, with additional notes and references for those who wish to explore the life and work of Turing more deeply. -/- The book is divided into eight parts, covering different aspects of Turing’s life and work. -/- Part I presents various biographical aspects of Turing, some from (...)
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  9. Revisiting Turing and His Test: Comprehensiveness, Qualia, and the Real World.Vincent C. Müller & Aladdin Ayesh (eds.) - 2012 - AISB.
    Proceedings of the papers presented at the Symposium on "Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World" at the 2012 AISB and IACAP Symposium that was held in the Turing year 2012, 2–6 July at the University of Birmingham, UK. Ten papers. - http://www.pt-ai.org/turing-test --- Daniel Devatman Hromada: From Taxonomy of Turing Test-Consistent Scenarios Towards Attribution of Legal Status to Meta-modular Artificial Autonomous Agents - Michael Zillich: My Robot is Smarter than Your Robot: (...)
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  10. Wolpert, Chaitin and Wittgenstein on impossibility, incompleteness, the limits of computation, theism and the universe as computer-the ultimate Turing Theorem.Michael Starks - 2017 - Philosophy, Human Nature and the Collapse of Civilization Michael Starks 3rd Ed. (2017).
    I have read many recent discussions of the limits of computation and the universe as computer, hoping to find some comments on the amazing work of polymath physicist and decision theorist David Wolpert but have not found a single citation and so I present this very brief summary. Wolpert proved some stunning impossibility or incompleteness theorems (1992 to 2008-see arxiv.org) on the limits to inference (computation) that are so general they are independent of the device doing the computation, and even (...)
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  11. Logically possible machines.Eric Steinhart - 2002 - Minds and Machines 12 (2):259-280.
    I use modal logic and transfinite set-theory to define metaphysical foundations for a general theory of computation. A possible universe is a certain kind of situation; a situation is a set of facts. An algorithm is a certain kind of inductively defined property. A machine is a series of situations that instantiates an algorithm in a certain way. There are finite as well as transfinite algorithms and machines of any degree of complexity (e.g., Turing and super-Turing machines (...)
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  12. Infinitely Complex Machines.Eric Steinhart - 2007 - In Intelligent Computing Everywhere. Springer. pp. 25-43.
    Infinite machines (IMs) can do supertasks. A supertask is an infinite series of operations done in some finite time. Whether or not our universe contains any IMs, they are worthy of study as upper bounds on finite machines. We introduce IMs and describe some of their physical and psychological aspects. An accelerating Turing machine (an ATM) is a Turing machine that performs every next operation twice as fast. It can carry out infinitely many operations in finite (...)
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  13. Turing and the evaluation of intelligence.Francesco Bianchini - 2014 - Isonomia: Online Philosophical Journal of the University of Urbino:1-18.
    The article deals with some ideas by Turing concerning the background and the birth of the well-known Turing Test, showing the evolution of the main question proposed by Turing on thinking machine. The notions he used, especially that one of imitation, are not so much exactly defined and shaped, but for this very reason they have had a deep impact in artificial intelligence and cognitive science research from an epistemological point of view. Then, it is suggested (...)
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  14. A Quantum Computer in a 'Chinese Room'.Vasil Penchev - 2020 - Mechanical Engineering eJournal (Elsevier: SSRN) 3 (155):1-8.
    Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside (...)
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  15. Evolution: The Computer Systems Engineer Designing Minds.Aaron Sloman - 2011 - Avant: Trends in Interdisciplinary Studies 2 (2):45-69.
    What we have learnt in the last six or seven decades about virtual machinery, as a result of a great deal of science and technology, enables us to offer Darwin a new defence against critics who argued that only physical form, not mental capabilities and consciousness could be products of evolution by natural selection. The defence compares the mental phenomena mentioned by Darwin’s opponents with contents of virtual machinery in computing systems. Objects, states, events, and processes in virtual machinery which (...)
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  16. 沃尔珀特、柴廷和维特根斯坦关于不可能、不完整、说谎的悖论、有论、计算极限、非量子力学不确定性原理和宇宙作为计算机——图灵机器理论的终极定理 (Wolpert, Chaitin and Wittgenstein on impossibility, incompleteness, the liar paradox, theism, the limits of computation, a non-quantum mechanical uncertainty principle and the universe as computer—the ultimate theorem in T Machine Theory) (修订 2019).Michael Richard Starks - 2020 - In 欢迎来到地球上的地狱 婴儿,气候变化,比特币,卡特尔,中国,民主,多样性,养成基因,平等,黑客,人权,伊斯兰教,自由主义,繁荣,网络,混乱。饥饿,疾病,暴力,人工智能,战争. Las Vegas, NV USA: Reality Press. pp. 173-177.
    我最近读过许多关于计算极限和宇宙作为计算机的讨论,希望找到一些关于多面体物理学家和决策理论家大卫·沃尔珀特的惊人工作的评论,但没有发现一个引文,所以我提出这个非常简短的总结。Wolpert 证明了一些惊人的不可能或不完整的定理(1992-2008-见arxiv dot org)对推理(计算)的限制,这些极限非常一般,它们独立于执行计算的设备,甚至独立于物理定律,因此,它们适用于计算机、物理和人类行为。他们利用Cantor的对角线、骗子悖论和世界线来提供图灵机器理论的 终极定理,并似乎提供了对不可能、不完整、计算极限和宇宙的见解。计算机,在所有可能的宇宙和所有生物或机制,产生,除其他外,非量子力学不确定性原理和一神论的证明。与柴廷、所罗门诺夫、科莫尔加罗夫和维特根斯 坦的经典作品以及任何程序(因此没有设备)能够生成比它拥有的更大复杂性的序列(或设备)的概念有着明显的联系。有人可能会说,这一工作意味着无政府主义,因为没有比物质宇宙更复杂的实体,从维特根斯坦的观点来看 ,"更复杂的"是毫无意义的(没有满足的条件,即真理制造者或测试)。即使是"上帝"(即具有无限时间/空间和能量的"设备")也无法确定给定的&q uot;数字"是否为"随机",也无法找到某种方式来显示给定的"公式"、"定理"或"句子"或"设备&q uot;(所有这些语言都是复杂的语言)游戏)是特定"系统"的一部分。 那些希望从现代两个系统的观点来看为人类行为建立一个全面的最新框架的人,可以查阅我的书《路德维希的哲学、心理学、Mind 和语言的逻辑结构》维特根斯坦和约翰·西尔的《第二部》(2019年)。那些对我更多的作品感兴趣的人可能会看到《会说话的猴子——一个末日星球上的哲学、心理学、科学、宗教和政治——文章和评论2006-201 9年第二次(2019年)》和《自杀乌托邦幻想》第21篇世纪4日 (2019).
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  17. A Dilemma for Solomonoff Prediction.Sven Neth - 2023 - Philosophy of Science 90 (2):288-306.
    The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This (...)
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  18. (2 other versions)MICHAEL POLANYI: CAN THE MIND BE REPRESENTED BY A MACHINE?Paul Richard Blum - 2010 - Polanyiana 19 (1-2):35-60.
    In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with Polanyi’s (...)
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  19. My mind is not the universe: the map is not the territory.Xiaoyang Yu - manuscript
    In order to describe my findings/conclusions systematically, a new semantic system (i.e., a new language) has to be intentionally defined by the present article. Humans are limited in what they know by the technical limitation of their cortical language network. A reality is a situation model (SM). For example, the conventionally-called “physical reality” around my conventionally-called “physical body” is actually a “geometric” SM of my brain. The universe is an autonomous objective parallel computing automaton which evolves by itself automatically/unintentionally – (...)
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  20. The Development of Ideas on Computable Intelligence.Yinsheng Zhang - 2017 - Journal of Human Cognition 1 (1):97-108.
    This paper sums up the fundamental features of intelligence through the common features stated by various definitions of "intelligence": Intelligence is the ability of achieving systematic goals (functions) of brain and nerve system through selecting, and artificial intelligence or machine intelligence is an imitation of life intelligence or a replication of features and functions. Based on the definition mentioned above, this paper discusses and summarizes the development routes of ideas on computable intelligence, including Godel's "universal recursive function", the (...)
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  21. Information, learning and falsification.David Balduzzi - 2011
    There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The (...)
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  22. Against digital ontology.Luciano Floridi - 2009 - Synthese 168 (1):151 - 178.
    The paper argues that digital ontology (the ultimate nature of reality is digital, and the universe is a computational system equivalent to a Turing Machine) should be carefully distinguished from informational ontology (the ultimate nature of reality is structural), in order to abandon the former and retain only the latter as a promising line of research. Digital vs. analogue is a Boolean dichotomy typical of our computational paradigm, but digital and analogue are only “modes of presentation” of Being (...)
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  23. Informatics: Science or Téchne?Tito Palmeiro - 2016 - O Que Nos Faz Pensar 25:88-97.
    Informatics is generally understood as a “new technology” and is therewith discussed according to technological aspects such as speed, data retrieval, information control and so on. Its widespread use from home appliances to enterprises and universities is not the result of a clear-cut analysis of its inner possibilities but is rather dependent on all sorts of ideological promises of unlimited progress. We will discuss the theoretical definition of informatics proposed in 1936 by Alan Turing in order to show that (...)
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  24. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as (...)
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  25. A Turing Machine for Exponential Function.P. M. F. Lemos - manuscript
    This is a Turing Machine which computes the exponential function f(x,y) = xˆy. Instructions format and operation of this machine are intended to best reflect the basic conditions outlined by Alan Turing in his On Computable Numbers, with an Application to the Entscheidungsproblem (1936), using the simplest single-tape and single-symbol version, in essence due to Kleene (1952) and Carnielli & Epstein (2008). This machine is composed by four basic task machines: one which checks if exponent (...)
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  26. The Kochen - Specker theorem in quantum mechanics: a philosophical comment (part 1).Vasil Penchev - 2013 - Philosophical Alternatives 22 (1):67-77.
    Non-commuting quantities and hidden parameters – Wave-corpuscular dualism and hidden parameters – Local or nonlocal hidden parameters – Phase space in quantum mechanics – Weyl, Wigner, and Moyal – Von Neumann’s theorem about the absence of hidden parameters in quantum mechanics and Hermann – Bell’s objection – Quantum-mechanical and mathematical incommeasurability – Kochen – Specker’s idea about their equivalence – The notion of partial algebra – Embeddability of a qubit into a bit – Quantum computer is not Turing (...) – Is continuality universal? – Diffeomorphism and velocity – Einstein’s general principle of relativity – „Mach’s principle“ – The Skolemian relativity of the discrete and the continuous – The counterexample in § 6 of their paper – About the classical tautology which is untrue being replaced by the statements about commeasurable quantum-mechanical quantities – Logical hidden parameters – The undecidability of the hypothesis about hidden parameters – Wigner’s work and и Weyl’s previous one – Lie groups, representations, and psi-function – From a qualitative to a quantitative expression of relativity − psi-function, or the discrete by the random – Bartlett’s approach − psi-function as the characteristic function of random quantity – Discrete and/ or continual description – Quantity and its “digitalized projection“ – The idea of „velocity−probability“ – The notion of probability and the light speed postulate – Generalized probability and its physical interpretation – A quantum description of macro-world – The period of the as-sociated de Broglie wave and the length of now – Causality equivalently replaced by chance – The philosophy of quantum information and religion – Einstein’s thesis about “the consubstantiality of inertia ant weight“ – Again about the interpretation of complex velocity – The speed of time – Newton’s law of inertia and Lagrange’s formulation of mechanics – Force and effect – The theory of tachyons and general relativity – Riesz’s representation theorem – The notion of covariant world line – Encoding a world line by psi-function – Spacetime and qubit − psi-function by qubits – About the physical interpretation of both the complex axes of a qubit – The interpretation of the self-adjoint operators components – The world line of an arbitrary quantity – The invariance of the physical laws towards quantum object and apparatus – Hilbert space and that of Minkowski – The relationship between the coefficients of -function and the qubits – World line = psi-function + self-adjoint operator – Reality and description – Does a „curved“ Hilbert space exist? – The axiom of choice, or when is possible a flattening of Hilbert space? – But why not to flatten also pseudo-Riemannian space? – The commutator of conjugate quantities – Relative mass – The strokes of self-movement and its philosophical interpretation – The self-perfection of the universe – The generalization of quantity in quantum physics – An analogy of the Feynman formalism – Feynman and many-world interpretation – The psi-function of various objects – Countable and uncountable basis – Generalized continuum and arithmetization – Field and entanglement – Function as coding – The idea of „curved“ Descartes product – The environment of a function – Another view to the notion of velocity-probability – Reality and description – Hilbert space as a model both of object and description – The notion of holistic logic – Physical quantity as the information about it – Cross-temporal correlations – The forecasting of future – Description in separable and inseparable Hilbert space – „Forces“ or „miracles“ – Velocity or time – The notion of non-finite set – Dasein or Dazeit – The trajectory of the whole – Ontological and onto-theological difference – An analogy of the Feynman and many-world interpretation − psi-function as physical quantity – Things in the world and instances in time – The generation of the physi-cal by mathematical – The generalized notion of observer – Subjective or objective probability – Energy as the change of probability per the unite of time – The generalized principle of least action from a new view-point – The exception of two dimensions and Fermat’s last theorem. (shrink)
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  27. Observability of Turing Machines: a refinement of the theory of computation.Yaroslav Sergeyev & Alfredo Garro - 2010 - Informatica 21 (3):425–454.
    The Turing machine is one of the simple abstract computational devices that can be used to investigate the limits of computability. In this paper, they are considered from several points of view that emphasize the importance and the relativity of mathematical languages used to describe the Turing machines. A deep investigation is performed on the interrelations between mechanical computations and their mathematical descriptions emerging when a human (the researcher) starts to describe a Turing machine (the (...)
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  28. Halting Problem Proof from Finite Strings to Final States.P. Olcott - manuscript
    If there truly is a proof that shows that no universal halt decider exists on the basis that certain tuples: (H, Wm, W) are undecidable, then this very same proof (implemented as a Turing machine) could be used by H to reject some of its inputs. When-so-ever the hypothetical halt decider cannot derive a formal proof from its input strings and initial state to final states corresponding the mathematical logic functions of Halts(Wm, W) or Loops(Wm, W), halting (...)
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  29. Single-tape and multi-tape Turing machines through the lens of the Grossone methodology.Yaroslav Sergeyev & Alfredo Garro - 2013 - Journal of Supercomputing 65 (2):645-663.
    The paper investigates how the mathematical languages used to describe and to observe automatic computations influence the accuracy of the obtained results. In particular, we focus our attention on Single and Multi-tape Turing machines which are described and observed through the lens of a new mathematical language which is strongly based on three methodological ideas borrowed from Physics and applied to Mathematics, namely: the distinction between the object (we speak here about a mathematical object) of an observation and the (...)
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  30. Rezension von "Die äußeren Grenzen der Vernunft " (The Outer Limits of Reason) von Noson Yanofsky 403p (2013) ( Überprüfung überarbeitet 2019).Michael Richard Starks - 2020 - In Willkommen in der Hölle auf Erden: Babys, Klimawandel, Bitcoin, Kartelle, China, Demokratie, Vielfalt, Dysgenie, Gleichheit, Hacker, Menschenrechte, Islam, Liberalismus, Wohlstand, Internet, Chaos, Hunger, Krankheit, Gewalt, Künstliche Intelligenz, Krieg. Reality Press. pp. 191-206.
    Ich gebe einen ausführlichen Überblick über 'The Outer Limits of Reason' von Noson Yanofsky aus einer einheitlichen Perspektive von Wittgenstein und Evolutionspsychologie. Ich weise darauf hin, dass die Schwierigkeit bei Themen wie Paradoxon in Sprache und Mathematik, Unvollständigkeit, Unbedenklichkeit, Berechenbarkeit, Gehirn und Universum als Computer usw. allesamt auf das Versäumnis zurückzuführen ist, unseren Sprachgebrauch im geeigneten Kontext sorgfältig zu prüfen, und daher das Versäumnis, Fragen der wissenschaftlichen Tatsache von Fragen der Funktionsweise von Sprache zu trennen. Ich bespreche Wittgensteins Ansichten über (...)
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  31. Wolpert, Chaitin und Wittgenstein über Unmöglichkeit, Unvollständigkeit, das Lügner-Paradoxon, Theismus, die Grenzen der Berechnung, ein nicht-quantenmechanisches Unsicherheitsprinzip und das Universum als Computer – der ultimative Satz in Turing Machine Theory (überarbeitet 2019).Michael Richard Starks - 2020 - In Willkommen in der Hölle auf Erden: Babys, Klimawandel, Bitcoin, Kartelle, China, Demokratie, Vielfalt, Dysgenie, Gleichheit, Hacker, Menschenrechte, Islam, Liberalismus, Wohlstand, Internet, Chaos, Hunger, Krankheit, Gewalt, Künstliche Intelligenz, Krieg. Reality Press. pp. 186-190.
    Ich habe viele kürzliche Diskussionen über die Grenzen der Berechnung und das Universum als Computer gelesen, in der Hoffnung, einige Kommentare über die erstaunliche Arbeit des Polymath Physikers und Entscheidungstheoretikers David Wolpert zu finden, aber habe kein einziges Zitat gefunden und so präsentiere ich diese sehr kurze Zusammenfassung. Wolpert bewies einige verblüffende Unmöglichkeit oder Unvollständigkeit Theoreme (1992 bis 2008-siehe arxiv dot org) über die Grenzen der Schlussfolgerung (Berechnung), die so allgemein sind, dass sie unabhängig von dem Gerät, das die Berechnung, (...)
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  32. Wolpert, Chaitin et Wittgenstein sur l’impossibilité, l’incomplétude, le paradoxe menteur, le théisme, les limites du calcul, un principe d’incertitude mécanique non quantique et l’univers comme ordinateur, le théorème ultime dans Turing Machine Theory (révisé 2019).Michael Richard Starks - 2020 - In Bienvenue en Enfer sur Terre : Bébés, Changement climatique, Bitcoin, Cartels, Chine, Démocratie, Diversité, Dysgénique, Égalité, Pirates informatiques, Droits de l'homme, Islam, Libéralisme, Prospérité, Le Web, Chaos, Famine, Maladie, Violence, Intellige. Las Vegas, NV USA: Reality Press. pp. 185-189.
    J’ai lu de nombreuses discussions récentes sur les limites du calcul et de l’univers en tant qu’ordinateur, dans l’espoir de trouver quelques commentaires sur le travail étonnant du physicien polymathe et théoricien de la décision David Wolpert, mais n’ont pas trouvé une seule citation et je présente donc ce résumé très bref. Wolpert s’est avéré quelques théoricaux d’impossibilité ou d’incomplétude renversants (1992 à 2008-voir arxiv dot org) sur les limites de l’inférence (computation) qui sont si généraux qu’ils sont indépendants de (...)
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  33. Can machines think? The controversy that led to the Turing test.Bernardo Gonçalves - 2023 - AI and Society 38 (6):2499-2509.
    Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why (...)
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  34. (1 other version)Turing on the Integration of Human and Machine Intelligence.Susan Sterrett - 2017 - In Alisa Bokulich & Juliet Floyd (eds.), Philosophical Explorations of the Legacy of Alan Turing. Springer Verlag. pp. 323-338.
    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 are (...)
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  35. Can machines be people? Reflections on the Turing triage test.Robert Sparrow - 2011 - In Patrick Lin, Keith Abney & George A. Bekey (eds.), Robot Ethics: The Ethical and Social Implications of Robotics. MIT Press. pp. 301-315.
    In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that complexify the application (...)
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  36. Turing’s imitation game: still an impossible challenge for all machines and some judges.Luciano Floridi, Mariarosaria Taddeo & Matteo Turilli - 2009 - Minds and Machines 19 (1):145–150.
    An Evaluation of the 2008 Loebner Contest.
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  37. Whispers and Shouts. The measurement of the human act.Fernando Flores Morador & Luis de Marcos Ortega (eds.) - 2021 - Alcalá de Henares, Madrid: Departement of Computational Sciences. University of Alcalá; Madrid.
    The 20th Century is the starting point for the most ambitious attempts to extrapolate human life into artificial systems. Norbert Wiener’s Cybernetics, Claude Shannon’s Information Theory, John von Neumann’s Cellular Automata, Universal Constructor to the Turing Test, Artificial Intelligence to Maturana and Varela’s Autopoietic Organization, all shared the goal of understanding in what sense humans resemble a machine. This scientific and technological movement has embraced all disciplines without exceptions, not only mathematics and physics but also biology, sociology, (...)
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  38. Turing’s imitation game: still an impossible challenge for all machines and some judges––an evaluation of the 2008 Loebner contest. [REVIEW]Luciano Floridi & Mariarosaria Taddeo - 2009 - Minds and Machines 19 (1):145-150.
    An evaluation of the 2008 Loebner contest.
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  39. Could a machine think? Alan M. Turing vs. John R. Searle.Günther Mario - unknown
    “Could a machine think?” asks John R. Searle in his paper Minds, Brains, and Programs. He answers that “only a machine could think1, and only very special kinds of machines, namely brains.”2 The subject of this paper is the analysis of the aforementioned question through presentation of the symbol manipulation approach to intelligence and Searle's well-known criticism to this approach, namely the Chinese room argument. The examination of these issues leads to the systems reply of the Chinese room (...)
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  40. L'interaction humain-machine à la lumière de Turing et Wittgenstein.Charles Bodon - 2023 - Revue Implications Philosophiques.
    Nous proposons une étude de la constitution du sens dans l'interaction humain-machine à partir des définitions que donnent Turing et Wittgenstein à propos de la pensée, la compréhension, et de la décision. Nous voulons montrer par l'analyse comparative des proximités et différences conceptuelles entre les deux auteurs que le sens commun entre humains et machines se co-constitue dans et à partir de l'action, et que c'est précisément dans cette co-constitution que réside la valeur sociale de leur interaction. Il (...)
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  41. 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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  42. Can AI Abstract the Architecture of Mathematics?Posina Rayudu - manuscript
    The irrational exuberance associated with contemporary artificial intelligence (AI) reminds me of Charles Dickens: "it was the age of foolishness, it was the epoch of belief" (cf. Nature Editorial, 2016; to get a feel for the vanity fair that is AI, see Mitchell and Krakauer, 2023; Stilgoe, 2023). It is particularly distressing—feels like yet another rerun of Seinfeld, which is all about nothing (pun intended); we have seen it in the 60s and again in the 90s. AI might have had (...)
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  43. Turing Test, Chinese Room Argument, Symbol Grounding Problem. Meanings in Artificial Agents (APA 2013).Christophe Menant - 2013 - American Philosophical Association Newsletter on Philosophy and Computers 13 (1):30-34.
    The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and the (...)
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  44. Turing's two tests for intelligence.Susan G. Sterrett - 1999 - Minds and Machines 10 (4):541-559.
    On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication (...)
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  45. Rethinking Turing’s Test and the Philosophical Implications.Diane Proudfoot - 2020 - Minds and Machines 30 (4):487-512.
    In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to explain (...)
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  46. ‘The Action of the Brain’. Machine Models and Adaptive Functions in Turing and Ashby.Hajo Greif - 2017 - In Vincent C. Müller (ed.), Philosophy and theory of artificial intelligence 2017. Berlin: Springer. pp. 24-35.
    Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turing’s and Ashby’s work on modelling “the action of the brain” (letter from Turing to Ashby, 1946) will help to shed light on the seemingly strict symbolic/embodied dichotomy: While it is clear that Turing was committed to formal, computational and Ashby to material, analogue methods of modelling, there is no straightforward mapping of (...)
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  47. ¿Existen las Máquinas Aceleradas de Turing? Paradojas y posibilidades lógicas.Jose Alejandro Fernández Cuesta - 2023 - Techno Review. International Technology, Science and Society Review 13 (1):49.74.
    Las máquinas aceleradas de Turing (ATMs) son dispositivos capaces de ejecutar súper-tareas. Sin embargo, el simple ejercicio de definirlas ha generado varias paradojas. En el presente artículo se definirán las nociones de súper-tarea y ATM de manera exhaustiva y se aclarará qué debe entenderse en un contexto lógico-formal cuando se pregunta por la existencia de un objeto. A partir de la distinción entre posibilidades lógicas y físicas se disolverán las paradojas y se concluirá que las ATMs son posibles y (...)
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  48. A Minimal Turing Test: Reciprocal Sensorimotor Contingencies for Interaction Detection.Pamela Barone, Manuel G. Bedia & Antoni Gomila - 2020 - Frontiers in Human Neuroscience 14:481235.
    In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, (...)
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  49. Turing and Computationalism.Napoleon M. Mabaquiao - 2014 - Philosophia: International Journal of Philosophy (Philippine e-journal) 15 (1):50-62.
    Due to his significant role in the development of computer technology and the discipline of artificial intelligence, Alan Turing has supposedly subscribed to the theory of mind that has been greatly inspired by the power of the said technology which has eventually become the dominant framework for current researches in artificial intelligence and cognitive science, namely, computationalism or the computational theory of mind. In this essay, I challenge this supposition. In particular, I will try to show that there is (...)
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  50. Turing test: 50 years later.Ayse Pinar Saygin, Ilyas Cicekli & Varol Akman - 2000 - Minds and Machines 10 (4):463-518.
    The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and similar topics in philosophy of mind (...)
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