Computational Artifacts: the Things of Computer Science

Turner, Raymond

doi:10.1007/s13347-019-00369-4

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…These systems are abstract, physical and experimental artifacts at the same time. In other words, each computational system is a mathematical structure that is realized in a machine (the computer) by which we carry out scientific tasks using models and simulations (Turner, 2018;Piccinini, 2018). The algorithm always has three lives: an abstract one, a physical one and an experimental one.…”

Section: The Difficulty Of Being An Aimentioning

confidence: 99%

Algorithmic unconscious: why psychoanalysis helps in understanding AI

Possati

2020

Palgrave Commun

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The central hypothesis of this paper is that the concepts and methods of psychoanalysis can be applied to the study of AI and human/AI interaction. The paper connects three research fields: machine behavior approach, psychoanalysis and anthropology of science. In the "Machine behavior: research perspectives" section, I argue that the behavior of AI systems cannot be studied only in a logical-mathematical or engineering perspective. We need to study AI systems not merely as engineering artifacts, but as a class of social actors with particular behavioral patterns and ecology. Hence, AI behavior cannot be fully understood without human and social sciences. In the "Why an unconscious for AI? What this paper is about" section, I give some clarifications about the aims of the paper. In the "Unconscious and technology. Lacan and Latour" section, I introduce the central thesis. I propose a re-interpretation of Lacan's psychoanalysis through Latour's anthropology of sciences. The aim of this re-interpretation is to show that the concept of unconscious is not so far from technique and technology. In the "The difficulty of being an AI" section, I argue that AI is a new stage in the human identification process, namely, a new development of the unconscious identification. After the imaginary and symbolic registers, AI is the third register of identification. Therefore, AI extends the movement that is at work in the Lacanian interpretation of the mirror stage and Oedipus complex and which Latour's reading helps us to clarify. From this point of view, I describe an AI system as a set of three contrasting forces: the human desire for identification, logic and machinery. In the "Miscomputation and information" section, I show how this interpretative model improves our understanding of AI.

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Section: The Difficulty Of Being An Aimentioning

confidence: 99%

Algorithmic unconscious: why psychoanalysis helps in understanding AI

Possati

2020

Palgrave Commun

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“…UML diagrams or any specification language). Essential properties specific for the good and successful design of programming languages are (see [11,Ch.19]):…”

Section: Designmentioning

confidence: 99%

“…The possible conflict between simplicity and expressive power is solved by a balance between principles of (see [11,Ch.20]):…”

Section: Designmentioning

confidence: 99%

Design, Malfunction, Validity: Three More Tasks for the Philosophy of Computing

Primiero

2019

Philos. Technol.

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“… See Gruber (1995), Smith (2003.4 SeeTurner et al (2019), Turner (2019), Primiero (2020.5 SeeGruber (1993);Guarino and Giaretta (1995);Breitman et al (2007);Guarino and Musen (2015).6 See Pâslaru-Bontaş (2007),Munn (2008),Goy and Magro (2015),Couclelis (2019). 7 For an analysis of the ISOs' core components, seeNoy and McGuinness (2003);Jaziri and Gargouri (2010); Tambassi (2021a).…”

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confidence: 99%

Completeness in Information Systems Ontologies

Tambassi

2021

Axiomathes

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In the domain of information systems ontologies, the notion of completeness refers to ontological contents by demanding that they be exhaustive with respect to the domain that the ontology aims to represent. The purpose of this paper is to analyze such a notion, by distinguishing different varieties of completeness and by questioning its consistency with the open-world assumption, which formally assumes the incompleteness of conceptualizations on information systems ontologies.

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Computational Artifacts: the Things of Computer Science

Cited by 8 publications

References 9 publications

Algorithmic unconscious: why psychoanalysis helps in understanding AI

Algorithmic unconscious: why psychoanalysis helps in understanding AI

Design, Malfunction, Validity: Three More Tasks for the Philosophy of Computing

Completeness in Information Systems Ontologies

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