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Summary Computers are currently intended as general purpose, programmable devices that carry out algorithmic instructions by way of arithmetic and logical operations. The philosophical literature on computers include the varied spectrum of theoretical, scientific, and technological issues that computers induce. Under theoretical issues are of particular importance those related to computability theory (such as the Church-Turing thesis), complexity, the limits of the computable, the relations between the mind and computers. Under the scientific problems of philosophical relevance are those related to computer-based mathematics, computer-generated arts, the explanation of computational events, pedagogy and human-computer interaction. Under the technological aspects of philosophical importance fall the design and correctness of programs, the nature of simulations, the representation and implementation of data and the nature and semantics of programming languages. 
Key works The philosophical relevance of computers is currently investigated in the large body of work that falls under the Philosophy of Computer Science, see Turner 2013
Introductions See Piccinini 2008 for an explication of the notion of computer according to the mechanistic account of computing mechanisms. For other issues see Turner 2013.
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  1. Carol E. Cleland (2002). On Effective Procedures. Minds and Machines 12 (2):159-179.
    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 procedures as definite courses (...)
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  2. Carol E. Cleland (1995). Effective Procedures and Computable Functions. Minds and Machines 5 (1):9-23.
    Horsten and Roelants have raised a number of important questions about my analysis of effective procedures and my evaluation of the Church-Turing thesis. They suggest that, on my account, effective procedures cannot enter the mathematical world because they have a built-in component of causality, and, hence, that my arguments against the Church-Turing thesis miss the mark. Unfortunately, however, their reasoning is based upon a number of misunderstandings. Effective mundane procedures do not, on my view, provide an analysis of ourgeneral concept (...)
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  3. Carol E. Cleland (1993). Is the Church-Turing Thesis True? Minds and Machines 3 (3):283-312.
    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 kind of effective (...)
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  4. B. Jack Copeland (2002). Hypercomputation. Minds and Machines 12 (4):461-502.
    A survey of the field of hypercomputation, including discussion of a variety of objections.
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  5. B. Jack Copeland (2002). Accelerating Turing Machines. Minds and Machines 12 (2):281-300.
    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 the theoretical limits of computability. Contrary (...)
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  6. B. Jack Copeland (1996). What is Computation? Synthese 108 (3):335-59.
    To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of computation against (...)
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  7. B. Jack Copeland & Oron Shagrir (2007). Physical Computation: How General Are Gandy's Principles for Mechanisms? [REVIEW] Minds and Machines 17 (2):217-231.
    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 share this view. We (...)
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  8. Jack Copeland, Even Turing Machines Can Compute Uncomputable Functions.
    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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  9. Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (2009). Brain and Mind Operational Architectonics and Man-Made “Machine” Consciousness. Cognitive Processing 10 (2):105-111.
    To build a true conscious robot requires that a robot’s “brain” be capable of supporting the phenomenal consciousness as human’s brain enjoys. Operational Architectonics framework through exploration of the temporal structure of information flow and inter-area interactions within the network of functional neuronal populations [by examining topographic sharp transition processes in the scalp electroencephalogram (EEG) on the millisecond scale] reveals and describes the EEG architecture which is analogous to the architecture of the phenomenal world. This suggests that the task of (...)
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  10. Luciano Floridi (2011). The Construction of Personal Identities Online. Minds and Machines 21 (4):477-479.
    The Construction of Personal Identities Online Content Type Journal Article Category Introduction Pages 1-3 DOI 10.1007/s11023-011-9254-y Authors Luciano Floridi, Department of Philosophy, University of Hertfordshire, de Havilland Campus, Hatfield, Hertfordshire, AL10 9AB UK Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495.
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  11. Vinod Goel (1991). Notationality and the Information Processing Mind. Minds and Machines 1 (2):129-166.
    Cognitive science uses the notion of computational information processing to explain cognitive information processing. Some philosophers have argued that anything can be described as doing computational information processing; if so, it is a vacuous notion for explanatory purposes.An attempt is made to explicate the notions of cognitive information processing and computational information processing and to specify the relationship between them. It is demonstrated that the resulting notion of computational information processing can only be realized in a restrictive class of dynamical (...)
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  12. Joanna Golinska-Pilarek, Angel Mora & Emilio Munoz Velasco (2008). An ATP of a Relational Proof System for Order of Magnitude Reasoning with Negligibility, Non-Closeness and Distance. In Tu-Bao Ho & Zhi-Hua Zhou (eds.), PRICAI 2008: Trends in Artificial Intelligence. Springer. 128--139.
    We introduce an Automatic Theorem Prover (ATP) of a dual tableau system for a relational logic for order of magnitude qualitative reasoning, which allows us to deal with relations such as negligibility, non-closeness and distance. Dual tableau systems are validity checkers that can serve as a tool for verification of a variety of tasks in order of magnitude reasoning, such as the use of qualitative sum of some classes of numbers. In the design of our ATP, we have introduced some (...)
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  13. Hartmut Haberland (1996). Cognitive Technology and Pragmatics: Analogies and (Non-)Alignments. [REVIEW] AI and Society 10 (3-4):303-308.
    This paper presents some considerations about the relationship between languages and computer systems from a pragmatic, user-centered point of view.
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  14. Boaz Miller & Isaac Record (2013). Justified Belief in a Digital Age: On the Epistemic Implications of Secret Internet Technologies. Episteme 10 (02):117 - 134.
    People increasingly form beliefs based on information gained from automatically filtered Internet ‎sources such as search engines. However, the workings of such sources are often opaque, preventing ‎subjects from knowing whether the information provided is biased or incomplete. Users’ reliance on ‎Internet technologies whose modes of operation are concealed from them raises serious concerns about ‎the justificatory status of the beliefs they end up forming. Yet it is unclear how to address these concerns ‎within standard theories of knowledge and justification. (...)
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  15. Gualtiero Piccinini, Computation in Physical Systems. Stanford Encyclopedia of Philosophy.
  16. Gualtiero Piccinini (2008). Computers. Pacific Philosophical Quarterly 89 (1):32–73.
    I offer an explication of the notion of computer, grounded in the practices of computability theorists and computer scientists. I begin by explaining what distinguishes computers from calculators. Then, I offer a systematic taxonomy of kinds of computer, including hard-wired versus programmable, general-purpose versus special-purpose, analog versus digital, and serial versus parallel, giving explicit criteria for each kind. My account is mechanistic: which class a system belongs in, and which functions are computable by which system, depends on the system's mechanistic (...)
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  17. Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. Neural Networks 21 (2-3):311-321.
    I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend
    the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.
    Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks
    compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.
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  18. Gualtiero Piccinini (2007). Computing Mechanisms. Philosophy of Science 74 (4):501-526.
    This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing (...)
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  19. Ricardo Restrepo (2013). Realismo científico, computacionalismo y la máxima pragmática. In Douglas Anderson, Ricardo Restrepo, Victor Hugo Chica & Diana Patricia Carmona (eds.), El pragmatismo norteamericano. IAEN.
    Se identifica el argumento de que la teoría de que hay propiedades computacionales suficientes para propiedades mentales es una teoría o falsa o vacía, ya que las propiedades computacionales no son empíricamente descubriles, intrínsecas ni causales, como sí lo son las propiedades mentales. Es un argumento que se puede destilar de los problemas que John Searle imputa a la ciencia cognitiva computacional, pero encuentra su correlato antecedente en el argumento que Max Newman utilizó para refutar el estructuralismo físico de Bertrand (...)
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  20. Ricardo Restrepo (2013). Realismo científico, computacionalismo y la máxima pragmática. In Douglas Anderson, Ricardo Restrepo, Victor Hugo Chica & Diana Patricia Carmona (eds.), El pragmatismo norteamericano.
    Se identifica el argumento de que la teoría de que hay propiedades computacionales suficientes para propiedades mentales es una teoría o falsa o vacía, ya que las propiedades computacionales no son empíricamente descubriles, intrínsecas ni causales, como sí lo son las propiedades mentales. Es un argumento que se puede destilar de los problemas que John Searle imputa a la ciencia cognitiva computacional, pero encuentra su correlato antecedente en el argumento que Max Newman utilizó para refutar el estructuralismo físico de Bertrand (...)
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  21. Oron Shagrir (1999). What is Computer Science About? The Monist 82 (1):131-149.
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  22. Eric Steinhart (2014). Your Digital Afterlives: Computational Theories of Life After Death. Palgrave.
    Our digital technologies have inspired new ways of thinking about old religious topics. Digitalists include computer scientists, transhumanists, singularitarians, and futurists. Digitalists have worked out novel and entirely naturalistic ways of thinking about bodies, minds, souls, universes, gods, and life after death. Your Digital Afterlives starts with three digitalist theories of life after death. It examines personality capture, body uploading, and promotion to higher levels of simulation. It then examines the idea that reality itself is ultimately a system of self-surpassing (...)
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  23. Eric Steinhart (2013). On the Plurality of Gods. Religious Studies 49 (3):289-312.
    Ordinal polytheism is motivated by the cosmological and design arguments. It is also motivated by Leibnizian–Lewisian modal realism. Just as there are many universes, so there are many gods. Gods are necessary concrete grounds of universes. The god-universe relation is one-to-one. Ordinal polytheism argues for a hierarchy of ranks of ever more perfect gods, one rank for every ordinal number. Since there are no maximally perfect gods, ordinal polytheism avoids many of the familiar problems of monotheism. It links theology with (...)
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  24. Eric Steinhart (2003). Supermachines and Superminds. Minds and Machines 13 (1):155-186.
    If the computational theory of mind is right, then minds are realized by machines. There is an ordered complexity hierarchy of machines. Some finite machines realize finitely complex minds; some Turing machines realize potentially infinitely complex minds. There are many logically possible machines whose powers exceed the Church–Turing limit (e.g. accelerating Turing machines). Some of these supermachines realize superminds. Superminds perform cognitive supertasks. Their thoughts are formed in infinitary languages. They perceive and manipulate the infinite detail of fractal objects. They (...)
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  25. Raymond Turner, The Philosophy of Computer Science. Stanford Encyclopedia of Philosophy.
  26. Franck Varenne (forthcoming). Chains of Reference in Computer Simulations. In S. Vaienti & P. Livet (eds.), Simulations and Networks. Presses Universitaires d'Aix-Marseille.
    This paper proposes an extensionalist analysis of computer simulations (CSs). It puts the emphasis not on languages nor on models, but on symbols, on their extensions, and on their various ways of referring. It shows that chains of reference of symbols in CSs are multiple and of different kinds. As they are distinct and diverse, these chains enable different kinds of remoteness of reference and different kinds of validation for CSs. Although some methodological papers have already underlined the role of (...)
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