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  • 1.
    Eklöf, Jon
    et al.
    Department of Computer Science, University of Copenhagen (DNK) ,GKN Aerospace Engines, Trollhättan (SWE).
    Hamelryck, Thomas
    Department of Computer Science, University of Copenhagen (DNK).
    Last, Cadell
    Center Leo Apostel (CLEA), Vrije Universiteit, Brussels (BEL).
    Grima, Alexander
    GKN Aerospace Engines, Trollhättan (SWE).
    Lundh Snis, Ulrika
    University West, School of Business, Economics and IT, Divison of Informatics.
    Abstraction, mimesis and the evolution of deep learning2023In: AI & Society: The Journal of Human-Centred Systems and Machine Intelligence, ISSN 0951-5666, E-ISSN 1435-5655, p. 1-9Article in journal (Refereed)
    Abstract [en]

    Deep learning developers typically rely on deep learning software frameworks (DLSFs)—simply described as pre-packaged libraries of programming components that provide high-level access to deep learning functionality. New DLSFs progressively encapsulate mathematical, statistical and computational complexity. Such higher levels of abstraction subsequently make it easier for deep learning methodology to spread through mimesis (i.e., imitation of models perceived as successful). In this study, we quantify this increase in abstraction and discuss its implications. Analyzing publicly available code from Github, we found that the introduction of DLSFs correlates both with significant increases in the number of deep learning projects and substantial reductions in the number of lines of code used. We subsequently discuss and argue the importance of abstraction in deep learning with respect to ephemeralization, technological advancement, democratization, adopting timely levels of abstraction, the emergence of mimetic deadlocks, issues related to the use of black box methods including privacy and fairness, and the concentration of technological power. Finally, we also discuss abstraction as a symptom of an ongoing technological metatransition.

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