Article:
AI Generative Art as Algorithmic Remediation

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dc.creatorBolter, Jay David
dc.date.accessioned2024-06-14T11:09:37Z
dc.date.available2024-06-14T11:09:37Z
dc.date.issued2023
dc.description.abstractAs the essays in this collection demonstrate, AI generative imagery raises compelling theoretical and historical questions for media studies. One fruitful approach is to regard these AI systems as a medium rooted in the principle of remediation, because the AI models depend on vast numbers of samples of other media (painting, drawing, photography, and textual captions) scraped from the web. This algorithmic remediation is related to, but distinct from earlier forms of remix, such as hip-hop. To generate new images from the AI models, the user types in a textual prompt. The resulting text-image pairs constitute a kind of metapicture, as defined by William J.T. Mitchell in Picture Theory (1994).en
dc.identifier.doihttp://dx.doi.org/10.25969/mediarep/22321
dc.identifier.urihttps://mediarep.org/handle/doc/23753
dc.languageeng
dc.publisherHerbert von Halem
dc.publisher.placeKöln
dc.relation.isPartOfissn:1614-0885
dc.relation.ispartofseriesIMAGE. Zeitschrift für interdisziplinäre Bildwissenschaft
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectmedia studiesen
dc.subjectAI systemsen
dc.subjectremediationen
dc.subject.ddcddc:700
dc.titleAI Generative Art as Algorithmic Remediationen
dc.typearticle
dc.type.statuspublishedVersion
dspace.entity.typeArticle
local.coverpage2024-06-16T03:03:18
local.source.epage207
local.source.issue1
local.source.issueTitleGenerative Imagery: Towards a ‘New Paradigm’ of Machine Learning-Based Image Production
local.source.spage195
local.source.volume19

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