The End of Prediction? AI Technologies in a No-Analog World.

AI technologies mine past data to anticipate future events, and yet our world of environmental and political crisis ushers in unprecedented conditions. Mixing examples of operational environments (AI in the oil and gas industry) with insights from media, cultural, and environmental studies, this art...

Descripción completa

Detalles Bibliográficos
Publicado en:Substance: A Review of Theory & Literary Criticism Vol. 52; no. 2; pp. 59 - 74
Autor principal: Munn, Luke
Formato: Artículo
Publicado: Johns Hopkins University Press 2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=172011489&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 172011489
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00492426
        2FG
      jtl: Substance: A Review of Theory & Literary Criticism
      issn: 00492426
      maglogo: N
    pubinfo:
      dt: 2023
      vid: 52
      iid: 2
      pid: 226
      pub: Johns Hopkins University Press
    artinfo:
      ui:
        172011489
        10.1353/sub.2023.a907149
      ppf: 59
      ppct: 15
      formats:
        fmt:
          @attributes:
            type: P
            size: 7.1MB
      tig:
        atl: The End of Prediction? AI Technologies in a No-Analog World.
      aug:
        au: Munn, Luke
        affil: University of Queensland
      su:
        Artificial intelligence
        Data mining
        Acquisition of data
        Prediction models
        Climate change
      sug:
        subj:
          Artificial intelligence
          Data mining
          Acquisition of data
          Prediction models
          Climate change
      keyword:
        AI
        climate change
        knowledge
        machine learning
        prediction
        uncertainty
      ab: AI technologies mine past data to anticipate future events, and yet our world of environmental and political crisis ushers in unprecedented conditions. Mixing examples of operational environments (AI in the oil and gas industry) with insights from media, cultural, and environmental studies, this article explores this grappling with uncertainty. To manage uncertainty, companies strive to internalize the complexity and contingency of the real world, collecting more data, designing more accurate sensors, and developing more exhaustive models. And yet prediction is a fraught exercise that struggles with correlation versus causation, the epistemological outside (the unknown), and the ontological outside (the open-endedness of the future). In addition, technology's role in accelerating and intensifying the destructive logics of capital contributes to more volatile planetary conditions, undermining the stability and continuity that prediction requires. The article thus argues that, at a fundamental level, a highly fluid future will increasingly frustrate any meaningful degree of prediction.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      custom: Copyright of Substance: A Review of Theory & Literary Criticism is the property of Johns Hopkins University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use.
      item: Substance: A Review of Theory & Literary Criticism
      holder: Johns Hopkins University Press
      dt:
        @attributes:
          year: 2023
    holdings:
      @attributes:
        islocal: N