Prolegomenon to a media theory of machine learning

Berry, David M (2017) Prolegomenon to a media theory of machine learning. Media Theory, 1 (1). pp. 74-87. ISSN 2557-826X

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This paper, outlines a groundwork for a media theory of machine learning by introducing two new concepts, compute-computing and compute-computed, and a framework for their interaction. Compute-computing (computing as generative) is here understood as the “active” learning component of a system, whereas compute-computed (computing as generated) is understood as the “passive”, coded, imprinted or inscribed aspect of a system. I introduce these two concepts to help us to think through the specificity of algorithmic systems that are more than just the operative, sequential or parallel systems of computational processing to which we have become accustomed. Indeed, in the case of machine learning systems, these systems have the capacity to be self-positing in the sense of generating models and data structures that internalise certain pattern characteristics of data, without the requirement that they are translated into formal data structures by a human programmer. That is, they are able to capture the abstract form of data input into the system, identify key characteristics, frames or patterns, and store this for comparison and classification of other data streams or objects.

Item Type: Article
Keywords: Media theory, machine learning, Spinoza, technology, digital humanities
Schools and Departments: School of Media, Film and Music > Media and Film
Research Centres and Groups: Sussex Humanities Lab
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
H Social Sciences > HM Sociology > HM1001 Social psychology > HM1176 Social influence. Social pressure > HM1181 Attitude > HM1206 Communication. Mass media
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Depositing User: David Berry
Date Deposited: 25 Sep 2017 12:34
Last Modified: 23 Mar 2021 08:45

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