Guest Editor: Anna Longo
Artistic creation and aesthetic evaluation are traditionally considered as faculties that differentiate human thinking from machine operations. However, recent developments in computer science and artificial intelligence seem to have challenged the assumption that machines are incapable of expressing creative behaviours.
Recent advancements in AI and machine learning have led to the production of systems that exhibit creative behaviour: they are able to find unpredictable, original and valuable solutions to given problems. These programs have been successfully applied to produce images, pieces of music and texts that are appealing for humans and that are uneasily distinguished from artworks created by human artists. Software used for generating music, artistic images, literature and poetry computationally, has motivated a research field called Computational Creativity. This multidisciplinary enquiry aims to model, simulate or replicate human creativity by using computers. It seeks to understand the cognitive mechanism that allows humans to introduce unexpected and valuable innovations to artificially generate outputs comparable to them. Important results have been obtained to support the thesis that machines can be creative. Moreover, research in Computational Creativity is concerned with analysing the criteria of human appreciation in terms of patterns in order to program systems that are capable of evaluating cultural products. For example, algorithms are now used to efficiently rank movies, musical pieces and images, appearing to recognize features that make these works valuable.
Research on Computational Creativity is, therefore, providing a better understanding of the processes of learning that allow humans to evolve their knowledge and practices in a surprising and unpredictable way, but they also seem to dismiss the idea that humans can actually think outside a computationally reproducible procedure. To this regard, we can consider philosophers like Heidegger, Deleuze or Lyotard who refuse to include artistic creation in the set of technologically produced novelties: art is the result of an activity that testifies the thinker’s freedom from the learning procedure suitable for obtaining pragmatically valuable patterns of information. Hence, Computational Creativity seems to contradict the philosophical assumption that artistic creation is a form of resistance against the control that operates through information technologies or a practice that reveals the excess of thinking over scientific reasoning.
Can we accept that machines are creative not only like human agents confronted with daily problem solving but also like artists? Are computers able to determine the aesthetic value of cultural products to create digital objects that assume to produce the same cognitive responses that are occasioned by human artists’ artworks? Can we think of a reconciliation of philosophical aesthetics and computational creativity? Or do we have to rethink the very notion of aesthetic knowledge?
This issue aims to explore Computational Creativity by analysing its machinic productions, but also the way in which contemporary human artists employ algorithmic and AI in their own works. Moreover, it invites us to rethink the notion of aesthetics by accepting the challenge that Computational Creativity poses to traditional anthropocentric views on artistic creation and evaluation.
We invite contributions in the form of academic articles from across disciplines. The average required length of a contribution is 5,000 words (bibliography and foot notes excluded), accompanied by an abstract. Interested contributors please send 300–500-word abstracts and a short 100-word biography to the editor (firstname.lastname@example.org) before December 30th 2022. As for the house style of formatting, please follow the Technophany submission guidelines, where a word template for articles can be found: http://journal.philosophyandtechnology.network/submission-guidelines/
Abstract due 30 December 2022
Abstract acceptance 1 February 2023
First draft chapters due 1 October 2023
publication May 2024