Switch to: References

Add citations

You must login to add citations.
  1. Developing creativity: Artificial barriers in artificial intelligence. [REVIEW]Kyle E. Jennings - 2010 - Minds and Machines 20 (4):489-501.
    The greatest rhetorical challenge to developers of creative artificial intelligence systems is convincingly arguing that their software is more than just an extension of their own creativity. This paper suggests that “creative autonomy,” which exists when a system not only evaluates creations on its own, but also changes its standards without explicit direction, is a necessary condition for making this argument. Rather than requiring that the system be hermetically sealed to avoid perceptions of human influence, developing creative autonomy is argued (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  • Assessing the Novelty of Computer-Generated Narratives Using Empirical Metrics.Federico Peinado, Virginia Francisco, Raquel Hervás & Pablo Gervás - 2010 - Minds and Machines 20 (4):565-588.
    Novelty is a key concept to understand creativity. Evaluating a piece of artwork or other creation in terms of novelty requires comparisons to other works and considerations about the elements that have been reused in the creative process. Human beings perform this analysis intuitively, but in order to simulate it using computers, the objects to be compared and the similarity metrics to be used should be formalized and explicitly implemented. In this paper we present a study on relevant elements for (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • Active Divergence with Generative Deep Learning - A Survey and Taxonomy.Terence Broad, Sebastian Berns, Simon Colton & Mick Grierson - 2021 - In Terence Broad, Sebastian Berns, Simon Colton & Mick Grierson (eds.), Proceedings of the 12th International Conference on Computational Creativity (ICCC ’21).
    Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results. In the context of computational creativity, however, a major shortcoming is that they are unable to explicitly diverge from the training data in creative ways and are limited to fitting the target data distribution. To address these limitations, there have been a growing number of approaches for optimising, hacking and rewriting these models in order to actively diverge from (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • Complex Systems in Aesthetics and Arts.Juan Romero, Colin Johnson & Jon McCormack - 2019 - Complexity 2019:1-2.
    The arts are one of the most complex of human endeavours, and so it is fitting that a special issue on Complex Systems in Aesthetics and Arts is being published. As the editors of this special issue, we would like to thank the reviewers of the submitted papers for their hard work in making this issue possible, as well as the authors who submitted their work and were very responsive to the comments of the reviewers and editors.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • On the creativity of large language models.Giorgio Franceschelli & Mirco Musolesi - forthcoming - AI and Society:1-11.
    Large language models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article, we first analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. In particular, we focus our discussion on the dimensions of value, novelty, and surprise as (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • Evaluating Human-Computer Co-creative Processes in Music: A Case Study on the CHAMELEON Melodic Harmonizer.Asterios Zacharakis, Maximos Kaliakatsos-Papakostas, Stamatia Kalaitzidou & Emilios Cambouropoulos - 2021 - Frontiers in Psychology 12.
    CHAMELEON is a computational melodic harmonization assistant. It can harmonize a given melody according to a number of independent harmonic idioms or blends between idioms based on principles of conceptual blending theory. Thus, the system is capable of offering a wealth of possible solutions and viewpoints for melodic harmonization. This study investigates how human creativity may be influenced by the use of CHAMELEON in a melodic harmonization task. Professional and novice music composers participated in an experiment where they were asked (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • Creativity refined: Bypassing the gatekeepers of appropriateness and value.Alan Dorin & Kevin Korb - unknown
    Download  
     
    Export citation  
     
    Bookmark