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of human imagination, whose aesthetics are determined by the content
of the dataset on which the tool was trained. Current AI tools are virtuoso
imitators of the results of human creativity, but their computational power
can compensate for their limitations to a certain extent. The authors of
AI for Arts provided a list of the essentially primitive operations on which
AI-assisted image generation is based: “Analyse and summarise; generate;
imitate; translate (mostly relevant for literature.” (Hageback – Hedblom,
2022, p. 50) Ariellie described this modern phenomenon of generative
art computer mannerism because these AI software outputs only “mimic
existing styles and are not creative at all. In those instances, computers
receive pre-existing examples and generate variants conforming to their
patterns, while trying to introduce some level of variation. These algorithms
do not generate styles of music or painting that are entirely new, instead,
they are instances of what we might call computational mannerism.”
(Manovich – Arielli, 2021, p. 7) Joanna Zylinska refers to the mainstream
production of AI images, sounds, and texts based on the uncritical use
of the tool as crowdsourced beauty. (Zylinska, 2020, p. 51). According to
her, this is an art production that uses AI tools and arouses the most at-
tention from the general public, whose aesthetic qualities do not exceed
the ambition to look beautiful, i.e., “symmetrical, mesmerising, garish,
and, first of all, similar to what already exists.” (Zylinska, 2020, p. 51)
Currently, however, artists are emerging on the art scene who manage to
use AI tools in a truly innovative way, respecting the specifics of materi-
ality and functional properties of the chosen medium. The most notable
art projects using AI software straddle the line between curatorial and
authorial approaches. Notable names include Memo Akten, Refik Anadol
and Mario Klingemann.
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