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  1. Samuel Alexander (2011). A Paradox Related to the Turing Test. The Reasoner 5 (6):90-90.
  2. Paul Richard Blum, Michael Polanyi: Can the Mind Be Represented by a Machine? Existence and Anthropology.
    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 and B. (...)
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  3. Joe Dewhurst (forthcoming). Mechanistic Miscomputation: A Reply to Fresco and Primiero. Philosophy and Technology:1-4.
    Fresco and Primiero’s recent article, ‘Miscomputation’ (Philosophy & Technology online first, doi:10.1007/s13347-013-0112-0), provides a useful framework with which to think about miscomputation, as well as an admirably broad taxonomy of different kinds of miscomputation. However, it also misconstrues the mechanistic approach to miscomputation, which I will argue should not recognise design errors as miscomputations per se. I argue that a computing mechanism, if it is functioning correctly in the physical sense, cannot miscompute on the basis of an error made by (...)
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  4. Gordana Dodig-Crnkovic (2008). Knowledge Generation as Natural Computation. Journal of Systemics, Cybernetics and Informatics 6 (2).
    Knowledge generation can be naturalized by adopting computational model of cognition and evolutionary approach. In this framework knowledge is seen as a result of the structuring of input data (data → information → knowledge) by an interactive computational process going on in the agent during the adaptive interplay with the environment, which clearly presents developmental advantage by increasing agent’s ability to cope with the situation dynamics. This paper addresses the mechanism of knowledge generation, a process that may be modeled as (...)
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  5. Gordana Dodig-Crnkovic (2007). WHERE DO NEW IDEAS COME FROM? HOW DO THEY EMERGE? - EPISTEMOLOGY AS COMPUTATION. In Christian Calude (ed.), Randomness & Complexity, from Leibniz to Chaitin.
    This essay presents arguments for the claim that in the best of all possible worlds (Leibniz) there are sources of unpredictability and creativity for us humans, even given a pancomputational stance. A suggested answer to Chaitin’s questions: “Where do new mathematical and biological ideas come from? How do they emerge?” is that they come from the world and emerge from basic physical (computational) laws. For humans as a tiny subset of the universe, a part of the new ideas comes as (...)
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  6. Amnon H. Eden (2007). Three Paradigms of Computer Science. Minds and Machines 17 (2):135-167.
    We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The rationalist (...)
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  7. Alexander R. Galloway (2012). The Interface Effect. Polity.
    Introduction : the computer as a mode of mediation -- The unworkable interface -- Software and ideology -- Are some things unrepresentable? -- Disingenuous informatics -- Postscript : we are the gold farmers.
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  8. Lucja Iwańska (1993). Logical Reasoning in Natural Language: It is All About Knowledge. [REVIEW] Minds and Machines 3 (4):475-510.
    A formal, computational, semantically clean representation of natural language is presented. This representation captures the fact that logical inferences in natural language crucially depend on the semantic relation of entailment between sentential constituents such as determiner, noun, adjective, adverb, preposition, and verb phrases.The representation parallels natural language in that it accounts for human intuition about entailment of sentences, it preserves its structure, it reflects the semantics of different syntactic categories, it simulates conjunction, disjunction, and negation in natural language by computable (...)
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  9. Robert Janusz (2012). Roberto Busa i humanistyczna informatyka. Rocznik Filozoficzny Ignatianum:91-106.
    Fr. Roberto Busa was an Italian Jesuit. In this article his biography will briefly be presented, and some issues raised by his philosophy analyzed. Busa was known as a pioneer of computerized research in the humanities. With the support of IBM he constructed the Index Thomisticus, containing all the works of St. Thomas Aquinas. He believed that expressions of the human can be mathematically modeled. He was the originator of a specific conception of hypertext, in which logically structured programs are (...)
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  10. Aran Nayebi (2014). Practical Intractability: A Critique of the Hypercomputation Movement. [REVIEW] Minds and Machines 24 (3):275-305.
    For over a decade, the hypercomputation movement has produced computational models that in theory solve the algorithmically unsolvable, but they are not physically realizable according to currently accepted physical theories. While opponents to the hypercomputation movement provide arguments against the physical realizability of specific models in order to demonstrate this, these arguments lack the generality to be a satisfactory justification against the construction of any information-processing machine that computes beyond the universal Turing machine. To this end, I present a more (...)
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  11. Matthew W. Parker (2003). Three Concepts of Decidability for General Subsets of Uncountable Spaces. Theoretical Computer Science 351 (1):2-13.
    There is no uniquely standard concept of an effectively decidable set of real numbers or real n-tuples. Here we consider three notions: decidability up to measure zero [M.W. Parker, Undecidability in Rn: Riddled basins, the KAM tori, and the stability of the solar system, Phil. Sci. 70(2) (2003) 359–382], which we abbreviate d.m.z.; recursive approximability [or r.a.; K.-I. Ko, Complexity Theory of Real Functions, Birkhäuser, Boston, 1991]; and decidability ignoring boundaries [d.i.b.; W.C. Myrvold, The decision problem for entanglement, in: R.S. (...)
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  12. William J. Rapaport (2005). Philosophy of Computer Science. Teaching Philosophy 28 (4):319-341.
    There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  13. William J. Rapaport (1999). Implementation Is Semantic Interpretation. The Monist 82 (1):109-130.
    What is the computational notion of "implementation"? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation. The online version differs from the published version in being a bit longer and going into a bit more detail.
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  14. Emilio Sanfilippo, Emanuele Ratti, Francesca Quattri, Aleksandra Sojic, Federico Boem, Gaoussou Camara & Eric Chuk (2013). Diverse Perspectives on Ontology. Applied Ontology 8 (8):59-71.
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  15. Jeremy Seligman (2002). The Scope of Turing's Analysis of Effective Procedures. Minds and Machines 12 (2):203-220.
    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 we evaluate her proposal (...)
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  16. Wilfried Sieg & John Byrnes, K-Graph Machines: Generalizing Turing's Machines and Arguments.
    Wilfred Sieg and John Byrnes. K-Graph Machines: Generalizing Turing's Machines and Arguments.
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  17. Jonathan Simon (2005). Formal Ontology for Natural Language Processing and the Integration of Biomedical Databases. International Journal of Medical Informatics 75:224-231.
    The central hypothesis of the collaboration between Language and Computing (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is that the methodology and conceptual rigor of a philosophically inspired formal ontology greatly benefits application ontologies. To this end r®, L&C’s ontology, which is designed to integrate and reason across various external databases simultaneously, has been submitted to the conceptual demands of IFOMIS’s Basic Formal Ontology (BFO). With this project we aim to move beyond the level of (...)
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  18. Beckett Sterner (2009). Object Spaces: An Organizing Strategy for Biological Theorizing. Biological Theory 4 (3):280-286.
    A classic analytic approach to biological phenomena seeks to refine definitions until classes are sufficiently homogenous to support prediction and explanation, but this approach founders on cases where a single process produces objects with similar forms but heterogeneous behaviors. I introduce object spaces as a tool to tackle this challenging diversity of biological objects in terms of causal processes with well-defined formal properties. Object spaces have three primary components: (1) a combinatorial biological process such as protein synthesis that generates objects (...)
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