Is writing prompts really making art?

Jon McCormack, Camilo Cruz Gambardella, Nina Rajcic, Stephen James Krol, Maria Teresa Llano, Meng Yang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

In recent years Generative Machine Learning systems have advanced significantly. A current wave of generative systems use text prompts to create complex imagery, video, even 3D datasets. The creators of these systems claim a revolution in bringing creativity and art to anyone who can type a prompt. In this position paper, we question the basis for these claims, dividing our analysis into three areas: the limitations of linguistic descriptions, implications of the dataset, and lastly, matters of materiality and embodiment. We conclude with an analysis of the creative possibilities enabled by prompt-based systems, asking if they can be considered a new artistic medium.

Original languageEnglish
Title of host publication12th International Conference, EvoMUSART 2023 Held as Part of EvoStar 2023 Brno, Czech Republic, April 12–14, 2023 Proceedings
EditorsColin Johnson, Nereida Rodríguez-Fernández, Sérgio M. Rebelo
Place of PublicationCham Switzerland
PublisherSpringer
Pages196-211
Number of pages16
ISBN (Electronic)9783031299568
ISBN (Print)9783031299551
DOIs
Publication statusPublished - 2023
EventInternational Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design 2023: held as Part of EvoStar 2023 - Brno, Czechia
Duration: 12 Apr 202314 Apr 2023
Conference number: 12th
https://link.springer.com/book/10.1007/978-3-031-29956-8 (Proceedings)
https://www.evostar.org/2023/evomusart/ (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13988
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design 2023
Abbreviated titleEvoMUSART 2023
Country/TerritoryCzechia
CityBrno
Period12/04/2314/04/23
Internet address

Keywords

  • Art
  • Artificial Intelligence
  • Diffusion Models
  • Neural Networks

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