Invited Distinguished Professor:
Gabriel Esquivel, Studio Professor
Texas A&M University
Team: Emily Majors, Cynthia Castro, Jeannelle Fernandez, Ale Valdovinos.
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.