Learning Nature (2018)
Is it inevitable that only our largest organizations, with their vast data sets, will decide how we will use AI? What if, instead, we could start small, to work at the scale of the personal, and to engage directly with AI. Could doing so allow us to develop new intuitions and understandings of what the technology is, and what it could enable?
It is from this perspective that I approach my work. Instead of scale and efficiency, I ask: Can aesthetic experiences give a fresh start to how we think about AI? And, can beauty be a basis from which we imagine new possibilities for AI?
This work exists in the rural context of upstate New York and the domain of nature. I chose this, not only to emphasize its difference from mainstream AI, but to place it in the area’s rich creative history. Almost two hundred years ago the Hudson River School used painting to express man’s relationship to nature. What would it mean for an AI today to understand/interpret that same nature?
The machine was taught using my photographs of upstate flowers, and it generated its own “photographs” using a DCGAN. Nothing that emerges is accurate, but the work isn’t asking for accuracy — it’s asking for the machine to build its own unique vision of the natural world. The misinterpretation is a piece of the work. Just as theorists have argued that the entire history of culture has been interpretation and misinterpretation of the cultural movements that preceded it, so too this work embraces the misinterpretation of nature by machine. We know that there’s not a human “intelligence” in the code, but still we anthropomorphize.
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