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Abstracts-Significant Digits
SIGNIFICANT DIGITS Responsible Use of Quantitative Information @Brussels, Fondation Universitaire 9-10 June 2015 Joint Research Centre Joint Research Centre Brussels, 09-10 June 2015 Preamble We live in an age when good policies are assumed to be evidence-based. And that evidential base is assumed to be at its best when expressed in numbers. The digital information may be derived from quantitative data organised in statistics, or from qualitative data organised in indicators. Either way, evidence in digital form provides the accepted foundation of policy arguments over a very broad range of issues. In the policy realm there are frequent debates over particular policy issues and their associated evidence. But only rarely is the nature of the evidence called into question. Such a faith in numbers can be dangerous. Policies in economic and financial policy, based on numbers whose significance was less than assumed, recently turned out to be quite disastrously wrong. Other examples can easily be cited. The decades-long period of blaming dietary fats for heart disease, rather than sugar, is a notable recent case. We are concerned here with the systemic problem: whether we are regularly placing too much of an evidentiary burden on quantitative sciences whose strength and maturity are inherently inadequate. The harm that has been done to those sciences, as well as to the policy process, should be recognised. Only in that way can future errors be avoided. In this workshop we will review a seminal essay by Andrea Saltelli and Mario Giampietro, ‘The Fallacy of Evidence Based Policy’. That paper contains positive recommendations for the development of a responsible quantification. -
APA Newsletter on Philosophy and Computers, Vol. 18, No. 2 (Spring
NEWSLETTER | The American Philosophical Association Philosophy and Computers SPRING 2019 VOLUME 18 | NUMBER 2 FEATURED ARTICLE Jack Copeland and Diane Proudfoot Turing’s Mystery Machine ARTICLES Igor Aleksander Systems with “Subjective Feelings”: The Logic of Conscious Machines Magnus Johnsson Conscious Machine Perception Stefan Lorenz Sorgner Transhumanism: The Best Minds of Our Generation Are Needed for Shaping Our Future PHILOSOPHICAL CARTOON Riccardo Manzotti What and Where Are Colors? COMMITTEE NOTES Marcello Guarini Note from the Chair Peter Boltuc Note from the Editor Adam Briggle, Sky Croeser, Shannon Vallor, D. E. Wittkower A New Direction in Supporting Scholarship on Philosophy and Computers: The Journal of Sociotechnical Critique CALL FOR PAPERS VOLUME 18 | NUMBER 2 SPRING 2019 © 2019 BY THE AMERICAN PHILOSOPHICAL ASSOCIATION ISSN 2155-9708 APA NEWSLETTER ON Philosophy and Computers PETER BOLTUC, EDITOR VOLUME 18 | NUMBER 2 | SPRING 2019 Polanyi’s? A machine that—although “quite a simple” one— FEATURED ARTICLE thwarted attempts to analyze it? Turing’s Mystery Machine A “SIMPLE MACHINE” Turing again mentioned a simple machine with an Jack Copeland and Diane Proudfoot undiscoverable program in his 1950 article “Computing UNIVERSITY OF CANTERBURY, CHRISTCHURCH, NZ Machinery and Intelligence” (published in Mind). He was arguing against the proposition that “given a discrete- state machine it should certainly be possible to discover ABSTRACT by observation sufficient about it to predict its future This is a detective story. The starting-point is a philosophical behaviour, and this within a reasonable time, say a thousand discussion in 1949, where Alan Turing mentioned a machine years.”3 This “does not seem to be the case,” he said, and whose program, he said, would in practice be “impossible he went on to describe a counterexample: to find.” Turing used his unbreakable machine example to defeat an argument against the possibility of artificial I have set up on the Manchester computer a small intelligence. -
Supersizing Daubert Science for Litigation and Its Implications for Legal Practice and Scientific Research
Volume 52 Issue 4 Article 6 2007 Supersizing Daubert Science for Litigation and Its Implications for Legal Practice and Scientific Research Gary Edmond Follow this and additional works at: https://digitalcommons.law.villanova.edu/vlr Part of the Evidence Commons Recommended Citation Gary Edmond, Supersizing Daubert Science for Litigation and Its Implications for Legal Practice and Scientific Research, 52 Vill. L. Rev. 857 (2007). Available at: https://digitalcommons.law.villanova.edu/vlr/vol52/iss4/6 This Symposia is brought to you for free and open access by Villanova University Charles Widger School of Law Digital Repository. It has been accepted for inclusion in Villanova Law Review by an authorized editor of Villanova University Charles Widger School of Law Digital Repository. Edmond: Supersizing Daubert Science for Litigation and Its Implications f 2007] SUPERSIZING DAUBERT SCIENCE FOR LITIGATION AND ITS IMPLICATIONS FOR LEGAL PRACTICE AND SCIENTIFIC RESEARCH GARY EDMOND* I. INTRODUCTION: SCIENCE FOR LITIGATION AND THE EXCLUSIONARY ETHOS T HIS essay is about sciencefor litigation, its implications and limitations.' "Science for litigation" is expert evidence specifically developed for litigation-extant, pending or anticipated.2 Although, as we shall see, federal judges have demonstrated a curious tendency to restrict the concept to expert evidence associated with litigation which is underway or pending. Ordinarily, expert evidence developed for, or tailored to, litigation carries an epistemic stigma. Conventionally, the fact that expert evidence is ori- ented toward a specific goal is thought to impair the independence of the experts and the reliability of their evidence. This often manifests in con- cerns that evidence developed for litigation is inconsistent with scientific knowledge and the opinions of experts not embroiled in litigation. -
APA Newsletters NEWSLETTER on PHILOSOPHY and COMPUTERS
APA Newsletters NEWSLETTER ON PHILOSOPHY AND COMPUTERS Volume 08, Number 1 Fall 2008 FROM THE EDITOR, PETER BOLTUC FROM THE CHAIR, MICHAEL BYRON PAPERS ON ROBOT CONSCIOUSNESS Featured Article STAN FRANKLIN, BERNARD J. BAARS, AND UMA RAMAMURTHY “A Phenomenally Conscious Robot?” GILBERT HARMAN “More on Explaining a Gap” BERNARD J. BAARS, STAN FRANKLIN, AND UMA RAMAMURTHY “Quod Erat Demonstrandum” ANTONIO CHELLA “Perception Loop and Machine Consciousness” MICHAEL WHEELER “The Fourth Way: A Comment on Halpin’s ‘Philosophical Engineering’” DISCUSSION ARTICLES ON FLORIDI TERRELL WARD BYNUM “Toward a Metaphysical Foundation for Information Ethics” JOHN BARKER “Too Much Information: Questioning Information Ethics” © 2008 by The American Philosophical Association EDWARD HOWLETT SPENCE “Understanding Luciano Floridi’s Metaphysical Theory of Information Ethics: A Critical Appraisal and an Alternative Neo-Gewirthian Information Ethics” DISCUSSION ARTICLES ON BAKER AMIE L. THOMASSON “Artifacts and Mind-Independence: Comments on Lynne Rudder Baker’s ‘The Shrinking Difference between Artifacts and Natural Objects’” BETH PRESTON “The Shrinkage Factor: Comment on Lynne Rudder Baker’s ‘The Shrinking Difference between Artifacts and Natural Objects’” PETER KROES AND PIETER E. VERMAAS “Interesting Differences between Artifacts and Natural Objects” BOOK REVIEW Amie Thomasson: Ordinary Objects REVIEWED BY HUAPING LU-ADLER PAPERS ON ONLINE EDUCATION H.E. BABER “Access to Information: The Virtuous and Vicious Circles of Publishing” VINCENT C. MÜLLER “What A Course on Philosophy of Computing Is Not” GORDANA DODIG-CRNKOVIC “Computing and Philosophy Global Course” NOTES CONSTANTINOS ATHANASOPOULOS “Report on the International e-Learning Conference for Philosophy, Theology and Religious Studies, York, UK, May 14th-15th, 2008” “Call for Papers on the Ontological Status of Web-Based Objects” APA NEWSLETTER ON Philosophy and Computers Piotr Bołtuć, Editor Fall 2008 Volume 08, Number 1 phenomenal consciousness remains open. -
History and Philosophy of Neural Networks
HISTORY AND PHILOSOPHY OF NEURAL NETWORKS J. MARK BISHOP Abstract. This chapter conceives the history of neural networks emerging from two millennia of attempts to rationalise and formalise the operation of mind. It begins with a brief review of early classical conceptions of the soul, seating the mind in the heart; then discusses the subsequent Cartesian split of mind and body, before moving to analyse in more depth the twentieth century hegemony identifying mind with brain; the identity that gave birth to the formal abstractions of brain and intelligence we know as `neural networks'. The chapter concludes by analysing this identity - of intelligence and mind with mere abstractions of neural behaviour - by reviewing various philosophical critiques of formal connectionist explanations of `human understanding', `mathematical insight' and `consciousness'; critiques which, if correct, in an echo of Aristotelian insight, sug- gest that cognition may be more profitably understood not just as a result of [mere abstractions of] neural firings, but as a consequence of real, embodied neural behaviour, emerging in a brain, seated in a body, embedded in a culture and rooted in our world; the so called 4Es approach to cognitive science: the Embodied, Embedded, Enactive, and Ecological conceptions of mind. Contents 1. Introduction: the body and the brain 2 2. First steps towards modelling the brain 9 3. Learning: the optimisation of network structure 15 4. The fall and rise of connectionism 18 5. Hopfield networks 23 6. The `adaptive resonance theory' classifier 25 7. The Kohonen `feature-map' 29 8. The multi-layer perceptron 32 9. Radial basis function networks 34 10. -
Post Normal Science: Working Deliberatively Within Imperfections
Copernicus Institute Studium Generale Wageningen Science, Policy and Complex Phenomena 21 March 2007 Post Normal Science: working deliberatively within imperfections Dr. Jeroen P. van der Sluijs Copernicus Institute for Sustainable Development and Innovation Utrecht University & Centre d'Economie et d'Ethique pour l'Environnement et le Développement, Université de Versailles Saint-Quentin-en-Yvelines, France Universiteit Utrecht Copernicus Institute Haugastøl group: Jeroen van der Sluijs; Ragnar Fjelland; Jerome Ravetz; Anne Ingeborg Myhr; Roger Strand; Silvio Funtowicz; Kamilla Kjølberg; Kjellrun Hiis Hauge; Bruna De Marchi; Andrea Saltelli Universiteit Utrecht Copernicus Institute Complex - uncertain -risks Typical characteristics (Funtowicz & Ravetz): • Decisions will need to be made before conclusive scientific evidence is available; • Potential impacts of ‘wrong’ decisions can be huge • Values are in dispute • Knowledge base is characterized by large (partly irreducible, largely unquantifiable) uncertainties, multi- causality, knowledge gaps, and imperfect understanding; • More research ≠ less uncertainty; unforeseen complexities! • Assessment dominated by models, scenarios, assumptions, extrapolations • Many (hidden) value loadings reside in problem frames, indicators chosen, assumptions made Knowledge Quality Assessment is essential Universiteit Utrecht Copernicus Institute Universiteit Utrecht Copernicus Institute 3 paradigms of uncertain risks 'deficit view' • Uncertainty is provisional • Reduce uncertainty, make ever more complex -
Science on the Verge
Science on the Verge Andrea Saltelli Centre for the Study of the Sciences and the Humanities (SVT) - University of Bergen (UIB) & Institut de Ciència i Tecnologia Ambientals (ICTA) - Universitat Autonoma de Barcelona (UAB) Symposium at Copernicus Institute, Utrecht, Ruppertbuilding room 134, 25 February 2016, h16.30-18.00 Published by the Consortium for Science, Policy and Outcomes at Arizona State University, March 2016, on Amazon. http://www.amazon.com/Rightful-Place-Science- Verge/dp/0692596380/ref=sr_1_1?s=books&ie=UTF8&qid=1456255907&sr=1-1&keywords=saltelli http://www.andreasaltelli.eu/science-on-the-verge Dan Sarewitz, Preface Almodóvar, Swift, Laputa’s portrayal of XVIII science, science’s present predicaments Chapter 1. Andrea Saltelli, Jerome Ravetz, Silvio Funtowicz, Who will solve the crisis in science? This talk Chapter 2. Andrea Saltelli, Mario Giampietro, The fallacy of evidence based policy Quantification as hypocognition; socially constructed ignorance & uncomfortable knowledge; ancien régime; quantitative story telling Chapter 3. Alice Benessia, Silvio Funtowicz, Never late, never lost, never unprepared Trajectories of innovation and modes of demarcation of science from society: ‘separation’, ‘hybridization’ and ‘substitution’; what contradictions these trajectories generate Chapter 4. Ângela Guimarães Pereira, Andrea Saltelli , Institutions on the verge: working at the science policy interface The special case of the commission’s in house science service; the Joint Research Centre as a boundary institutions; diagnosis, -
Neural Network
© 2014 IJIRT | Volume 1 Issue 5 | ISSN : 2349-6002 NEURAL NETWORK Neha, Aastha Gupta, Nidhi Abstract- This research paper gives a short description 1.1.BIOLOGICAL MOTIVATION of what an artificial neural network is and its biological motivation .A clear description of the various ANN The human brain is a great information processor, models and its network topologies has been given. It even though it functions quite slower than an also focuses on the different learning paradigms. This ordinary computer. Many researchers in the field of paper also focuses on the applications of ANN. artificial intelligence look to the organization of the I. INTRODUCTION brain as a model for building intelligent machines.If we think of a sort of the analogy between the In machine learning and related fields, artificial complex webs of interconnected neurons in a brain neural networks (ANNs) are basically and the densely interconnected units making up computational models ,inspired by the biological an artificial neural network, where each unit,which is nervous system (in particular the brain), and are used just like a biological neuron,is capable of taking in a to estimate or the approximate functions that can number of inputs and also producing an output. depend on a large number of inputs applied . The complexity of real neurons is highly abstracted Artificial neural networks are generally presented as when modelling artificial systems of interconnected nerve cells or neurons(“as neurons. These basically consist of inputs (like they are called”) which can compute values from synapses), which are multiplied by weights inputs, and are capable of learning,which is possible (i.e the strength of the respective signals), and then is only due to their adaptive nature. -
NTNU Cyborg: a Study Into Embodying Neuronal Cultures Through Robotic Systems
NTNU Cyborg: A study into embodying Neuronal Cultures through Robotic Systems August Martinius Knudsen Master of Science in Cybernetics and Robotics Submission date: July 2016 Supervisor: Sverre Hendseth, ITK Norwegian University of Science and Technology Department of Engineering Cybernetics Problem description Through The NTNU Cyborg initiative, a cybernetic (bio-robotic) organism is currently under development. Using neural tissue, cultured on a microelectrode array (MEA), the goal is to use in-vitro biological neurons to control a robotic platform. The objective for this thesis, is to research and discuss the necessary aspects of developing such a cybernetic system. A literary search into similar studies is suggested, along with getting acquainted with the biological sides of the project. The student is free to explore any fields deemed relevant for the goal of embodying a neuronal culture. The student may contribute to this discussion with own ideas and suggestions. Part of the objective for this assignment, is to lay the ground work for a further Phd project as well as for the future development of the NTNU Cyborg system. i Sammendrag Gjennom NTNU Cyborg, er en kybernetisk organisme (en blanding an biologi or robot) under utvikling. Ved hjelp av nevrale vev, dyrket pa˚ toppen av mikroelektroder (MEA), er malet˚ a˚ bruke biologiske nerveceller til a˚ styre en robot. Denne avhandlingen dan- ner grunnlaget for denne utviklingen, og fungerer som et forstudium til en Phd oppgave angaende˚ samme tema. Det har blitt gjennomført en undersøkelse av de nødvendige aspekter ved det a˚ integrere en nervecellekultur i en robot. Med utgangspunkt i lignende forskning, samt kunnskap fra fagomradene˚ nevrovitenskap og informatikk, er de nødvendige komponenter for a˚ bygge et slikt system diskutert. -
SCIENCE, LABOR and SCIENTIFIC PROGRESS by George Borg B.Sc
TITLE PAGE SCIENCE, LABOR AND SCIENTIFIC PROGRESS by George Borg B.Sc. in Chemistry, University of California, Berkeley 1999 Ph.D. in Chemistry, Harvard University, 2007 M.A. in Philosophy, Tufts University, 2012 Submitted to the Graduate Faculty of the Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2020 COMMITTE E PAGE UNIVERSITY OF PITTSBURGH DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by George Borg It was defended on June 5, 2020 and approved by Paolo Palmieri, Associate Professor, History and Philosophy of Science Kevin Zollman, Associate Professor, Philosophy at Carnegie Mellon University Dissertation Co-Director: John D. Norton, Distinguished Professor, History and Philosophy of Science Dissertation Co-Director: Michael R. Dietrich, Professor, History and Philosophy of Science ii Copyright © by George Borg 2020 iii ABST RACT SCIENCE, LABOR AND SCIENTIFIC PROGRSS George Borg, PhD University of Pittsburgh, 2020 My dissertation introduces a new materialist theory of scientific progress built on a novel characterization of scientific work and an analysis of progress appropriate to it. Two questions, crucial for understanding scientific progress, are answered: Why is it possible for scientists at a given time to have more epistemic abilities than scientists at an earlier time? How can knowledge acquired in the past be used in on-going or future research? I argue that these questions are best answered by analyzing science as a form of labor. The elements of the labor process, involving both intellectual and material means, provide a starting-point for the systematic study of how scientific abilities evolve. -
A Complete Bibliography of Publications of Claude Elwood Shannon
A Complete Bibliography of Publications of Claude Elwood Shannon Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 09 July 2021 Version 1.81 Abstract This bibliography records publications of Claude Elwood Shannon (1916–2001). Title word cross-reference $27 [Sil18]. $4.00 [Mur57]. 7 × 10 [Mur57]. H0 [Siq98]. n [Sha55d, Sha93-45]. P=N →∞[Sha48j]. s [Sha55d, Sha93-45]. 1939 [Sha93v]. 1950 [Ano53]. 1955 [MMS06]. 1982 [TWA+87]. 2001 [Bau01, Gal03, Jam09, Pin01]. 2D [ZBM11]. 3 [Cer17]. 978 [Cer17]. 978-1-4767-6668-3 [Cer17]. 1 2 = [Int03]. Aberdeen [FS43]. ablation [GKL+13]. above [TT12]. absolute [Ric03]. Abstract [Sha55c]. abundance [Gor06]. abundances [Ric03]. Academics [Pin01]. According [Sav64]. account [RAL+08]. accuracy [SGB02]. activation [GKL+13]. Active [LB08]. actress [Kah84]. Actually [Sha53f]. Advanced [Mar93]. Affecting [Nyq24]. After [Bot88, Sav11]. After-Shannon [Bot88]. Age [ACK+01, Cou01, Ger12, Nah13, SG17, Sha02, Wal01b, Sil18, Cer17]. A’h [New56]. A’h-mose [New56]. Aid [Bro11, Sha78, Sha93y, SM53]. Albert [New56]. alcohol [SBS+13]. Algebra [Roc99, Sha40c, Sha93a, Jan14]. algorithm [Cha72, HDC96, LM03]. alignments [CFRC04]. Allied [Kah84]. alpha [BSWCM14]. alpha-Shannon [BSWCM14]. Alphabets [GV15]. Alternate [Sha44e]. Ambiguity [Loe59, PGM+12]. America [FB69]. American [Ger12, Sha78]. among [AALR09, Di 00]. Amplitude [Sha54a]. Analogue [Sha43a, Sha93b]. analogues [Gor06]. analyses [SBS+13]. Analysis [Sha37, Sha38, Sha93-51, GB00, RAL+07, SGB02, TKL+12, dTS+03]. -
Democratizing Science in an Age of Uncertainty
June 2016 Democratizing Science in an Age of Uncertainty An Interview with Jerome Ravetz Uncertainty, one of the defining features of many of the challenges facing humanity in the twenty-first century, has considerable implications for scientific thought and practice. Allen White, Senior Fellow at Tellus Institute, talks with Jerome Ravetz, a pioneer of post-normal science and a leading advocate of citizen science, about why it is time to retire the doctrine of scientific certainty and why science is too important to be left to the scientists alone. You are perhaps best known for your work on post-normal science. What drew you to the study of the philosophy of science in the first place? I began my studies in mathematics, but I was never as passionate about it as some of my peers were. Science seemed like a relatively safe career path. Meanwhile, some seminars in philosophy piqued my interest in the philosophy of science, though I had serious misgivings. To me, philosophy’s image of science had little relation to what I was studying, or even to the questions that I was beginning to have about science. In graduate school, I intended to study physics, but ended up in mathematics. After settling in England, I connected with Stephen Toulmin, a distinguished philosopher of science, who enabled me to jump from mathematics to the history and philosophy of science. I spent much of the next ten years pondering questions of where science is and where it should be going. This led to the publication Scientific Knowledge and Its Social Problems in 1971, in which I argued that scientific research is not a simple discovery of facts, but rather craftsmen’s work on “intellectually constructed classes of things and events.” The health and vitality, even the survival, of objective knowledge of the world around us depends critically on the subjective commitments to quality of the scientists themselves.