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  1. added 2020-10-22
    Genealogy of Algorithms: Datafication as Transvaluation.Virgil W. Brower - 2020 - le Foucaldien 6 (1):1-43.
    This article investigates religious ideals persistent in the datafication of information society. Its nodal point is Thomas Bayes, after whom Laplace names the primal probability algorithm. It reconsiders their mathematical innovations with Laplace's providential deism and Bayes' singular theological treatise. Conceptions of divine justice one finds among probability theorists play no small part in the algorithmic data-mining and microtargeting of Cambridge Analytica. Theological traces within mathematical computation are emphasized as the vantage over large numbers shifts to weights beyond enumeration in (...)
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  2. added 2020-09-19
    Strengthening Weak Emergence.Nora Berenstain - forthcoming - Erkenntnis:1-18.
    Bedau's influential (1997) account analyzes weak emergence in terms of the non-derivability of a system’s macrostates from its microstates except by simulation. I offer an improved version of Bedau’s account of weak emergence in light of insights from information theory. Non-derivability alone does not guarantee that a system’s macrostates are weakly emergent. Rather, it is non-derivability plus the algorithmic compressibility of the system’s macrostates that makes them weakly emergent. I argue that the resulting information-theoretic picture provides a metaphysical account of (...)
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  3. added 2020-09-17
    Data Storage, Security And Techniques In Cloud Computing.R. Dinesh Arpitha & Shobha R. Sai - 2018 - International Journal of Research and Analytical Reviews 5 (4).
    Cloud computing is the computing technology which provides resources like software, hardware, services over the internet. Cloud computing provides computation, software, data access, and storage services that do not require end- user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing enables the user and organizations to store their data remotely and enjoy good quality applications on the demand without having any burden associated with local hardware resources and software managements but it possesses (...)
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  4. added 2020-09-11
    What Have Google’s Random Quantum Circuit Simulation Experiments Demonstrated About Quantum Supremacy?Jack K. Horner & John Symons - forthcoming - In Hamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti & Quoc-Nam Tran (eds.), Advances in Software Engineering, Education, and e-Learning. Cham, Switzerland: Springer Nature.
    Quantum computing is of high interest because it promises to perform at least some kinds of computations much faster than classical computers. Arute et al. 2019 (informally, “the Google Quantum Team”) report the results of experiments that purport to demonstrate “quantum supremacy” – the claim that the performance of some quantum computers is better than that of classical computers on some problems. Do these results close the debate over quantum supremacy? We argue that they do not. In the following, we (...)
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  5. added 2020-08-07
    A Class of Examples Demonstrating That 'P ≠ NP' in the 'P Vs NP' Problem.Vasil Penchev - 2020 - Computing Methodology eJournal (Elsevier: SSRN) 3 (19):1-19.
    The CMI Millennium “P vs NP Problem” can be resolved e.g. if one shows at least one counterexample to the "P = NP" conjecture. A certain class of problems being such counterexamples will be formulated. This implies the rejection of the hypothesis that "P = NP" for any conditions satisfying the formulation of the problem. Thus, the solution "P is different from NP" of the problem in general is proved. The class of counterexamples can be interpreted as any quantum superposition (...)
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  6. added 2019-07-18
    Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...)
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  7. added 2018-02-12
    Toward an Algorithmic Metaphysics.Steve Petersen - 2013 - In David Dowe (ed.), Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence. Springer. pp. 306-317.
    There are writers in both metaphysics and algorithmic information theory (AIT) who seem to think that the latter could provide a formal theory of the former. This paper is intended as a step in that direction. It demonstrates how AIT might be used to define basic metaphysical notions such as *object* and *property* for a simple, idealized world. The extent to which these definitions capture intuitions about the metaphysics of the simple world, times the extent to which we think the (...)
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  8. added 2015-12-04
    God is Random: A Novel Argument for the Existence of God.Serkan Zorba - 2016 - European Journal of Science and Theology 12 (1):51-67.
    Applying the concepts of Kolmogorov-Chaitin complexity and Turing’s uncomputability from the computability and algorithmic information theories to the irreducible and incomputable randomness of quantum mechanics, a novel argument for the existence of God is presented. Concepts of ‘transintelligence’ and ‘transcausality’ are introduced, and from them, it is posited that our universe must be epistemologically and ontologically an open universe. The proposed idea also proffers a new perspective on the nonlocal nature and the infamous wave-function-collapse problem of quantum mechanics.
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