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  1. 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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  2. 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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  3. added 2017-02-01
    Randomness is Unpredictability.Jon Williamson - manuscript
  4. added 2017-01-30
    Algorithmic Randomness Over General Spaces.Kenshi Miyabe - 2014 - Mathematical Logic Quarterly 60 (3):184-204.
  5. added 2017-01-24
    Algorithmic Randomness and Measures of Complexity.George Barmpalias - forthcoming - Association for Symbolic Logic: The Bulletin of Symbolic Logic.
    We survey recent advances on the interface between computability theory and algorithmic randomness, with special attention on measures of relative complexity. We focus on (weak) reducibilities that measure (a) the initial segment complexity of reals and (b) the power of reals to compress strings, when they are used as oracles. The results are put into context and several connections are made with various central issues in modern algorithmic randomness and computability.
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  6. added 2016-11-04
    Inductive Inference in the Limit of Empirically Adequate Theories.Bernhard Lauth - 1995 - Journal of Philosophical Logic 24 (5):525 - 548.
    Most standard results on structure identification in first order theories depend upon the correctness and completeness (in the limit) of the data, which are provided to the learner. These assumption are essential for the reliability of inductive methods and for their limiting success (convergence to the truth). The paper investigates inductive inference from (possibly) incorrect and incomplete data. It is shown that such methods can be reliable not in the sense of truth approximation, but in the sense that the methods (...)
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  7. added 2016-08-30
    The Semimeasure Property of Algorithmic Probability -- “Feature‘ or “Bug‘?Douglas Campbell - 2013 - In David L. Dowe (ed.), Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence. Springer Berlin Heidelberg. pp. 79--90.
    An unknown process is generating a sequence of symbols, drawn from an alphabet, A. Given an initial segment of the sequence, how can one predict the next symbol? Ray Solomonoff’s theory of inductive reasoning rests on the idea that a useful estimate of a sequence’s true probability of being outputted by the unknown process is provided by its algorithmic probability (its probability of being outputted by a species of probabilistic Turing machine). However algorithmic probability is a “semimeasure”: i.e., the sum, (...)
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  8. added 2016-03-28
    Universal Intelligence: A Definition of Machine Intelligence.Shane Legg & Marcus Hutter - 2007 - Minds and Machines 17 (4):391-444.
    A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...)
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  9. 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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  10. added 2015-08-24
    On Some Scientific Modalities: Propensities, Randomness and Causation.Antony Eagle - 2004 - Dissertation, Princeton University
    The essays that constitute this dissertation explore three strategies for understanding the role of modality in philosophical accounts of propensities, randomness, and causation. In Chapter 1, I discuss how the following essays are to be considered as illuminating the prospects for these strategies, which I call reductive essentialism, subjectivism and pragmatism. The discussion is framed within a survey of approaches to modality more broadly construed. ;In Chapter 2, I argue that any broadly dispositional analysis of probability as a physical property (...)
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  11. added 2015-06-27
    Quantum Computing.Amit Hagar & Michael Cuffaro - 2015 - Stanford Encyclopedia of Philosophy.
    Combining physics, mathematics and computer science, quantum computing has developed in the past two decades from a visionary idea to one of the most fascinating areas of quantum mechanics. The recent excitement in this lively and speculative domain of research was triggered by Peter Shor (1994) who showed how a quantum algorithm could exponentially "speed up" classical computation and factor large numbers into primes much more rapidly (at least in terms of the number of computational steps involved) than any known (...)
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  12. added 2014-07-23
    How-Possibly Explanations in (Quantum) Computer Science.Michael E. Cuffaro - 2015 - Philosophy of Science 82 (5):737-748.
    A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds and to describe the possibility spaces associated with these processes. By doing this, we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is gained in subsequently asking a (...)
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  13. added 2014-03-26
    Some Thoughts About the Hardest Logic Puzzle Ever.Tim S. Roberts - 2001 - Journal of Philosophical Logic 30 (6):609-612.
    "The Hardest Logic Puzzle Ever" was first described by the late George Boolos in the Spring 1996 issue of the Harvard Review of Philosophy. Although not dissimilar in appearance from many other simpler puzzles involving gods (or tribesmen) who always tell the truth or always lie, this puzzle has several features that make the solution far from trivial. This paper examines the puzzle and describes a simpler solution than that originally proposed by Boolos.
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  14. added 2013-09-24
    Computability and Complexity: From a Programming Perspective Vol. 21.N. D. Jones - 1997 - MIT Press.
    This makes his book especially valuable." -- Yuri Gurevich, Professor of Computer Science, University of Michigan Computability and complexity theory should be of central concern to practitioners as well as theorists.
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  15. added 2013-09-06
    Randomness Through Computation: Some Answers, More Questions.Hector Zenil - unknown
    The book is intended to explain the larger and intuitive concept of randomness by means of computation, particularly through algorithmic complexity and recursion theory. It also includes the transcriptions (by A. German) of two panel discussion on the topics: Is The Universe Random?, held at the University of Vermont in 2007; and What is Computation? (How) Does Nature Compute?, held at the University of Indiana Bloomington in 2008. The book is intended to the general public, undergraduate and graduate students in (...)
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  16. added 2013-05-19
    Towards a Stable Definition of Program-Size Complexity.Hector Zenil - unknown
    We propose a test based on the theory of algorithmic complexity and an experimental evaluation of Levin's universal distribution to identify evidence in support of or in contravention of the claim that the world is algorithmic in nature. To this end statistical comparisons are undertaken of the frequency distributions of data from physical sources--repositories of information such as images, data stored in a hard drive, computer programs and DNA sequences--and the output frequency distributions generated by purely algorithmic means--by running abstract (...)
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  17. added 2013-05-19
    On the Kolmogorov-Chaitin Complexity for Short Sequences.Hector Zenil - unknown
    This is a presentation about joint work between Hector Zenil and Jean-Paul Delahaye. Zenil presents Experimental Algorithmic Theory as Algorithmic Information Theory and NKS, put together in a mixer. Algorithmic Complexity Theory defines the algorithmic complexity k(s) as the length of the shortest program that produces s. But since finding this short program is in general an undecidable question, the only way to approach k(s) is to use compression algorithms. He shows how to use the Compress function in Mathematica to (...)
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  18. added 2012-07-12
    Proving Theorems of the Second Order Lambek Calculus in Polynomial Time.Erik Aarts - 1994 - Studia Logica 53 (3):373 - 387.
    In the Lambek calculus of order 2 we allow only sequents in which the depth of nesting of implications is limited to 2. We prove that the decision problem of provability in the calculus can be solved in time polynomial in the length of the sequent. A normal form for proofs of second order sequents is defined. It is shown that for every proof there is a normal form proof with the same axioms. With this normal form we can give (...)
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  19. added 2011-06-07
    A Philosophical Treatise of Universal Induction.Samuel Rathmanner & Marcus Hutter - 2011 - Entropy 13 (6):1076-1136.
    Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results (...)
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  20. added 2011-01-12
    Counting Steps: A Finitist Interpretation of Objective Probability in Physics.Amit Hagar & Giuseppe Sergioli - 2015 - Epistemologia 37 (2):262-275.
    We propose a new interpretation of objective deterministic chances in statistical physics based on physical computational complexity. This notion applies to a single physical system (be it an experimental set--up in the lab, or a subsystem of the universe), and quantifies (1) the difficulty to realize a physical state given another, (2) the 'distance' (in terms of physical resources) from a physical state to another, and (3) the size of the set of time--complexity functions that are compatible with the physical (...)
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  21. added 2010-08-23
    Chance Versus Randomness.Antony Eagle - 2010 - Stanford Encyclopedia of Philosophy.
    This article explores the connection between objective chance and the randomness of a sequence of outcomes. Discussion is focussed around the claim that something happens by chance iff it is random. This claim is subject to many objections. Attempts to save it by providing alternative theories of chance and randomness, involving indeterminism, unpredictability, and reductionism about chance, are canvassed. The article is largely expository, with particular attention being paid to the details of algorithmic randomness, a topic relatively unfamiliar to philosophers.
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  22. added 2010-01-08
    A Simple Solution to the Hardest Logic Puzzle Ever.Brian Rabern & Landon Rabern - 2008 - Analysis 68 (2):105-112.
    We present the simplest solution ever to 'the hardest logic puzzle ever'. We then modify the puzzle to make it even harder and give a simple solution to the modified puzzle. The final sections investigate exploding god-heads and a two-question solution to the original puzzle.
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