Access the full text.
Sign up today, get DeepDyve free for 14 days.
D. Danks, A. London (2017)
Algorithmic Bias in Autonomous Systems
D. Guest, Kyle Cranmer, D. Whiteson (2018)
Deep Learning and Its Application to LHC PhysicsAnnual Review of Nuclear and Particle Science
(2017)
Theory and Observation in Science
Thomas Cowles (1934)
Dr. Henry Power's Poem on the MicroscopeIsis, 21
Michael Weisberg (2013)
Biology and Philosophy symposium on Simulation and Similarity: Using Models to Understand the WorldBiology & Philosophy, 30
Anil Jain, J. Mao, K. Mohiuddin (1996)
Artificial Neural Networks: A TutorialComputer, 29
Robert Nishikawa, M. Giger, Kunio Doi, C. Metz, F. Yin, C. Vyborny, R. Schmidt (1994)
Effect of case selection on the performance of computer-aided detection schemes.Medical physics, 21 2
(2017)
Calibration: Modeling the Measurement Process
J. Becker, P. Duhem, P. Wiener (1955)
The Aim and Structure of Physical TheorySouthern Economic Journal, 22
M. Massimi (2012)
Scientific Perspectivism and Its FoesPerspectivalism
Sandra Mitchell (2019)
Perspectives, Representation, and IntegrationUnderstanding Perspectivism
K. Darling (2002)
The complete Duhemian underdetermination argument: scientific language and practiceStudies in History and Philosophy of Science, 33
J. Bogen, J. Woodward (1988)
Saving the phenomenaThe Philosophical Review, 97
(2009)
Unsimple Truths: Science
(2003)
“ Toward a more developed philosophy of experimentation ” in Hans Radder ( ed . ) The Philosophy of Scientific Experimentation . :
Jeffrey Fauw, J. Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomašev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O'Donoghue, D. Visentin, George Driessche, Balaji Lakshminarayanan, Clemens Meyer, Faith Mackinder, Simon Bouton, K. Ayoub, Reena Chopra, Dominic King, A. Karthikesalingam, Cían Hughes, R. Raine, J. Hughes, D. Sim, C. Egan, A. Tufail, Hugh Montgomery, D. Hassabis, G. Rees, T. Back, P. Khaw, Mustafa Suleyman, Julien Cornebise, P. Keane, O. Ronneberger (2018)
Clinically applicable deep learning for diagnosis and referral in retinal diseaseNature Medicine, 24
(2017)
Theory and Observation in Science", The Stanford Encyclopedia of Philosophy (Summer
“ Scientific Perspectivism and its Foes ” , Philosophica 84 ( 2012 ) 2552
P. Humphreys (2004)
Extending Ourselves: Computational Science, Empiricism, and Scientific Method
E. Madden (1967)
Completeness in Science. Richard SchlegelPhilosophy of Science, 34
Sandra Mitchell (2009)
Unsimple Truths: Science, Complexity, and Policy
G. Morris (1992)
Systematic sources of signal irreproducibility and t1 noise in high-field NMR spectrometersJournal of Magnetic Resonance, 100
C. Craver, David Kaplan (2018)
Are More Details Better? On the Norms of Completeness for Mechanistic ExplanationsThe British Journal for the Philosophy of Science, 71
Sandra Mitchell (2000)
Dimensions of Scientific LawPhilosophy of Science, 67
(2007)
Causal perspectivalism”, in R. Corry and H. Price (eds.) Causation, Physics, and the Constitution of Reality (Oxford: OUP)
H. Price (2005)
Causal perspectivalism ∗
(2003)
Toward a more developed philosophy of experimentation
Cameron Buckner (2018)
Empiricism without magic: transformational abstraction in deep convolutional neural networksSynthese, 195
Martin Thomson-Jones (2011)
Scientific Representation: Paradoxes of PerspectiveAustralasian Journal of Philosophy, 89
(2018)
Clinically applicable deep
(2006)
Scientific Perspectivism
(2013)
Simulation and Similarity
Eran Tal (2017)
Calibration: Modelling the measurement process.Studies in history and philosophy of science, 65-66
O. Spies (1954)
The aim and structure of physical theoryJournal of The Franklin Institute-engineering and Applied Mathematics, 257
H. Schwalbe (2003)
Kurt Wüthrich, the ETH Zürich, and the Development of NMR Spectroscopy for the Investigation of Structure, Dynamics, and Folding of ProteinsChemBioChem, 4
(2008)
Scientific Representation. Paradoxes of Perspective (New York: OUP)
(2006)
Scientific Perspectivism (Chicago: University of Chicago Press)
Riashat Islam, Peter Henderson, Maziar Gomrokchi, Doina Precup (2017)
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous ControlArXiv, abs/1708.04133
Samuel Dodge, Lina Karam (2017)
A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions2017 26th International Conference on Computer Communication and Networks (ICCCN)
C. Craver (2006)
When mechanistic models explainSynthese, 153
(1967)
Book Review of Richard Schlegel. Completeness in science
H. Pfeifer (1999)
A short history of nuclear magnetic resonance spectroscopy and of its early years in GermanyMagnetic Resonance in Chemistry, 37
G. Kane (1994)
Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological ModelsJAMA, 271
Hasok Chang (2004)
Inventing Temperature: Measurement and Scientific Progress
M. Hutson (2018)
Artificial intelligence faces reproducibility crisis.Science, 359 6377
( forthcoming ) “ Perspectives , Representation and Integration ” in in M . Massimi and
[The question, “Will science remain human?” expresses a worry that deep learning algorithms will replace scientists in making judgments of classification and inference and that something crucial will be lost if that happens. Ever since the introduction of telescopes and microscopes humans have relied on technologies to extend beyond human sensory perception in acquiring scientific knowledge. In this paper I explore whether the ways in which new learning technologies extend beyond human cognitive aspects of science can be treated instrumentally. I will consider the norms for determining the reliability of a detection instrument, nuclear magnetic resonance spectroscopy, in predicting models of protein atomic structure. Can the same norms that apply in that case be used to judge the reliability of Artificial Intelligence deep learning algorithms?]
Published: Feb 6, 2020
Keywords: Instrumental perspectivism; Machine learning; Nuclear magnetic resonance spectroscopy; Artificial intelligence
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.