SciArt Magazine - All Issues December 2015 | Page 8

ity. Graham Budgett, a professor at the University of
California, Santa Barbara, has been teaching a class on
algorithmic art for many years now. Through his art,
he says he is trying “to fabricate complex metaphors
that implicitly reflect upon the production and display
systems of art while illuminating the pathos of individual
and collective human subjectivity.
“I call my practice ‘doing theory’ and ‘a scopophilic
conceptualism’, but it’s also comedic and allegorical,
intending a critique of regressive or reactionary tendencies in art theory and practice,” says Budgett. “The
context of my works, the title or accompanying text for
instance, is as important as any visuals. Aesthetics and
poetics, alongside humor and criticism, have always had
a role in my work.” In this way, Budgett imbues a strong
human component into his work, despite the fact that
the middleman that brings the art into existence is a
programmable code made of letters and numbers.
Budgett is hesitant to use labels like ‘algorithmic art’ to
really describe what he does, but he believes his REGRETS project, made in collaboration with Jane Mulfinger, comes closest to the definition. He calls REGRETS
“an interactive archive, public conceptual artwork, and
action–research study regarding the human capacity for
remorse. It comprises a local community element—so
far, Cambridge, Linz, Paris, and Santa Barbara—and a
global web archive” that is constantly growing.
A mobile booth and five nomadic backpack units
roam public space in a given community, collecting and
displaying anonymous regrets from local people to fill up
a sociological database of time– and site–specific sentiment in the community.
“For instance in Cambridge,” says Budgett, “after ten
days collecting, local peoples’ anonymous regrets were
displayed as large animated projections on the facade of
the Cambridge City Council Guildhall in the city centre Market Square. Communally shared, but typically
private recollections, each regret is like the ‘tip of an
iceberg’, representing fragmentary evidence of a much
larger hidden narrative. Together on public display, their
random juxtaposition and comic/tragic interplay approaches the epic poignancy of a history poem or saga.”
A private system instantly sends algorithmically generated and calculated feedback to the individual whose
regret is on display, “based on other locals’ similar
concerns to ‘share the burden.’” It’s a wonderful counterargument to the common cry that technology is creating barriers between people. Budgett and Mulfinger are
showing, instead, that a computer program can be used
to help make an individual’s emotions more universal
and understandable throughout the greater community.

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Obviously the whole field of algorithmic art is progressing very fast, thanks to new technologies that seem
to create splinter groups focused on developing and
refining a specific method or aesthetic. Hobbs identifies
five different categories that current artists are immersed in: cellular automata, fractals and other recursive
techniques, glitching, tiling patterns, and data visualization.
“These categories aren’t clearly defined,” says Hobbs,
“but they each have specific approaches to creating artwork. These also aren’t all–encompassing. For example,
my Community 5 piece takes a cellular automata approach, but almost none of my other works fit into one
of those categories.”
The biggest question, however, is how algorithmic art
is evolving—and there’s no easy to way to predict that.
“I think the applications of the techniques are changing more than the techniques themselves,” says Hobbs.
“Rather than stopping at a purely digital representation
of the artwork, many artists are translating the work
into a physical representation through 3D printing, lighting, mechanical apparatuses, plotters, and more. I also
expect we’ll see a lot of work in virtual reality when the
hardware and tools for that become more accessible.”
Artist Casey REAS takes a more personal approach
when predicting what algorithmic art will be. “‘Algorithmic art’ has no central source or community,” he says.
“I moved away from emergent systems a few years ago
to focus on working in a different way that leaves more
open to change operations (random calculations). The
work is still a system that has ‘some degree of autonomy’, but the growth and clear instructions that were the
focus of something like my ‘Process’ series have disappeared.”
Instead, like many others, REAS is embracing the full
potential and capability of what emerging technologies will be capable of creating of their own accord. “At
the moment, I’m starting to explore machine learning
through deep neural networks. This area is wide open
and extremely interesting,” due to the potential for
artificially intelligent systems to make art of their own
choosing.
As algorithmic art moves forward, the art community
may, at some point, have to brace for the possibility
that some of their peers may in fact simply be computer
systems that have learned to emulate hum