About: When we can trust computers (and when we can't)

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	When we can trust computers (and when we can't)
Creator	Highfield, Roger Coveney, Peter
source	ArXiv
abstract	With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering that are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long-term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation.
has issue date	2020-07-08(xsd:dateTime)
has license	arxiv
sha1sum (hex)	c3a43a45e59247124def664876e9e3d817539eb7
resource representing a document's title	When we can trust computers (and when we can't)
resource representing a document's body	covid:c3a43a45e59247124def664876e9e3d817539eb7#body_text
is schema:about of	named entity 'complex systems' named entity 'natural world' named entity 'uncertainty quantification' named entity 'machine learning' named entity 'reproducibility' »more»

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