IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i11p182-d435195.html
   My bibliography  Save this article

Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity

Author

Listed:
  • Yana Agafonova

    (School of Arts and Humanities, Department of Philology, National Research University Higher School of Economics, 199034 St. Petersburg, Russia)

  • Alexey Tikhonov

    (Yandex, 10117 Berlin, Germany)

  • Ivan P. Yamshchikov

    (Max Planck Institute for Mathematics in the Sciences, Max Planck Society, 04103 Leipzig, Germany)

Abstract

This paper revisits the receptive theory in the context of computational creativity. It presents a case study of a Paranoid Transformer—a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output, and discuss the impact of receptive theory, chance discovery, and simulation of fringe mental state on the understanding of computational creativity.

Suggested Citation

  • Yana Agafonova & Alexey Tikhonov & Ivan P. Yamshchikov, 2020. "Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity," Future Internet, MDPI, vol. 12(11), pages 1-12, October.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:182-:d:435195
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/11/182/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/12/11/182/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:182-:d:435195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.