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Fabricating Algorithmic Art

Authors: McLean, Alex; Harlizius-Klück, Ellen;

Fabricating Algorithmic Art

Abstract

“We build our computers the way we build our cities -- over time, without a plan, on top of ruins.” Ellen Ullman (1998) The above quote refers to the historical layers that make up our computer operating systems, where newly developed user interfaces are successively placed on top of the old ones, creating a kind of palimpsest. Behind the graphical user interface we find a text-based one, then a programming language, then a low-level assembly language, then machine and microcode, until we eventually meet with physical electronic circuits. The conventional timeline for computing technology as a whole begins earlier still, with the discovery of the electronic transistor a century ago. Each of these layers has had its heyday as the dominant user interface of its time, and indeed each has been used to make algorithmic systems for, or indeed as, art. There is much artwork to be recognised throughout this period, but if we keep digging, there are many more ruins to be found. Through research during our European Research Council project PENELOPE, we find that algorithms have been present in everyday life for millennia. In the following we will explore some examples which support this claim, with focus on our recent work while resident at the Textiles Zentrum Haslach in Austria.

Keywords

live coding, algorave, digital art, weaving, algorithmic art

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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