T4T LAB 2019 Texas A&M University. Invited Professor: Joris Putteneers.
Team: Emily Majors, Cynthia Castro, Jeannelle Fernandez, Alejandra Valdovinos
Dump Vestige
As Timothy Morton asserts, “the fantasy we have regarding
trash lies in that it disappears [and] dissolves.” In the United States alone,
humans are generating trash at a rate of 4.6 pounds per day per person, which
translates to 251 million tons per year. As a result, greenhouse gas production
is increasing and studies indicate that the earth will become uninhabitable for
human life by the year 2060. Hundreds of species rely on organic waste produced
by human activity for survival. This
raises the question of how an ecosystem dependent on the production of human
waste, such as the garbage dump can survive without humans available to
generate input?
This machine uses big data collected from digital waste and
physical waste in order to optimize dump emissions with the intent of
sustaining both the earth and the ecology of the rubbish dump, privileging the
dumps’ agenda to preserve itself in the case of human extinction through a
process of machine learning and synthetic trash manufacturing.
Occupying the territory of the dump, the self-generating
structure operates cyclically, fluctuating, expanding, and contracting over
time as more garbage accumulates and system optimization occurs. The cycle
begins with the insertion of an algorithmic primitive that collects, learns,
and expands until it begins phases of consolidation and optimization. The cycle
begins again as the machine updates and refines its understanding of the dump.
The machine determines the desired composition and form for
optimized trash based on a gained understanding of the chemical composition of
trash required for a positive impact on the ecosystem. The physical collection
mechanism is interested in collecting samples of organic material and in
rescuing lost data found in e-waste material such as computers, hard drives,
mobile devices, etc. The machine combines on-site collection and observation
techniques with its access to digital waste found in the cloud to better
process garbage input.
Although the preservation of human life is not the machine’s
intent, the machine’s ability to produce optimized waste that could fertilize
soil, purify water, or counter the effects of carbon emissions could
potentially postpone human extinction. Human extinction or not, the machine is
primarily concerned with self-preservation through optimized synthetic trash
manufacturing.
The machine is not pushing any aesthetic agenda. The machine
derives its aesthetic regime from its own assimilation of how the machine
becomes a part of its ecology, acquires big data, and produces as needed. It
establishes a completely new aesthetic regime based on the algorithm big data
allowed it to produce, but it is not assimilating any known aesthetic. The
media exhibited in the presentation represents our speculation on the qualities
of the machine’s aesthetic at all scales; large, in elevation and plan, smaller
in interior and exterior machine detailing. The smallest scale of speculation
can be observed in our photography and film studies.
