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Prolegomenon to a media theory of machine learning

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posted on 2023-06-09, 08:04 authored by David BerryDavid Berry
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.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Media Theory

ISSN

2557-826X

Issue

1

Volume

1

Page range

74-87

Department affiliated with

  • Media and Film Publications

Research groups affiliated with

  • Sussex Humanities Lab Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-09-25

First Open Access (FOA) Date

2017-10-02

First Compliant Deposit (FCD) Date

2017-10-02

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