Machine Wilderness

When landscapes first appeared as subject in European art, they emerged as a landscape of symbols. The features that populated Gothic depictions of Earth served primarily as convenient symbols for a narrative. Some natural objects were treated realistically, but many -like the absolutely fantastical mountain formations depicted in The Thebaid - are almost ideograms for mountains taken straight from Byzantine tradition. This is landscape seen over the shoulders of the main subject: humans (patrons) and biblical figures. A space where features are tagged placeholders in a larger narrative geography.

According to the eminent art historian Kenneth Clarck, for Flemish painter Jan van Eyck, the environment first appeared as a landscape of fact. “In a single lifetime” Clarck writes in Landscape into Art, “van Eyck progressed the history of art in a way that an unsuspecting art historian might assume to take centuries. In these first ‘modern’ landscapes, van Eyck achieves by color, a tone of light that seems to already fully breathe the air of the Renaissance.”

At present, landscape is emerging in artificial minds through machines learning from such domains as precision agriculture, mining, forestry, autonomous vehicle navigation and ecology. Until recently, the ability to make sense of the environment was limited to biological beings, but machines are now blurring those lines. I’m interested in shifting the debate centered on machine intelligence beyond human-centered preoccupations - like job security, privacy, and politics – and towards the impact of these technologies on non-human lives, i.e. the other 99,99% of life on Earth.

The Machine Gaze

From the machine perception, the environment seems to emerge as a landscape of commodity.

Unlike the painting tradition, machine perceptions of landscape aren’t rooted in Byzantine art, but defined by platforms and training sets that humans provide. Leading image classifier platforms, like Inception, typically include household items, human infrastructure, and machinery, but also a peculiar collection of animal and plant species, such as, for instance, hundreds of dog breeds. These organism do not refer to any existing ecosystem, but are a handpicked bunch that are of interest to humans. When such an AI is introduced to a real-world terrain, these pre-trained sets prove about as relevant as cat-videos to the work of a marine-ecologist. For example, during our Ars Bioarctica residency in the Finnish Arctic in 2018, we turned the camera-eye to the surroundings of the Kilpisjarvi Biological Research Station where the machine gazed across a snow-covered terrain. Our human eyes saw hundreds of birch trees, lichen-covered rocks and perhaps some passing birds, but when asked, the AI said it only saw snowmobiles. There were none. It was hallucinating. It was hallucinating a landscape full of snowmobiles. And perhaps more strikingly, it didn’t see the trees.

The worldview of these platform A.I.s is largely populated by human artefacts, including snowmobiles, vacuum cleaners, and even guillotines. Like us, the worldview of our technology isn’t neutral. Our landscapes, however, are still full of trees before they are planks; rocks before they are architecture; water before it is Evian. As it turns out cyborgs do not dream of electric sheep, but have much more commodity-centered imaginations. Platform A.I.s of late capitalism develop as human centered. With much of the world’s current environmental predicament stemming from anthropocentric bias, a question is raised: is it problematic that machines learn exclusively from humans? It is problematic that their current habitat is corporate? or do intelligent machines

need training-forests, like the ones for orphaned Orangutans in Indonesian rehabilitation programmes? Do the artificial agents that are currently taking seat in corporate boardrooms need to spend their weekends floating around coral reefs, volunteering at an organic farm, or wandering the tundra with reindeer? Should machines also learn directly from animals and plants? If a machine is less confined to human classifications, would it invent something radically different from Linnaean taxonomy? What features of natural phenomena would catch its attention and into what kinds of unknown bestiaries would it cluster them?

DeepSteward

In an experimental set-up called DeepSteward, currently at Het Nieuwe Instituut in Rotterdam, Ian Ingram and I are exploring these questions. While an artificial agent oversees the large pond at HNI, we are tasked with determining how the machine could be given more freedom to interpret the natural world. The first results show, for instance, how the clusters that the machine makes are sometimes impossible for humans to decipher. They can be extremely similar looking. Although we’re working primarily with images at the moment, we intend to radically broaden the scope of learning. This is in line with our more general impression that building a machine that looks at the world has forced us to reflect quite deeply on human perception. The complete naivety of the artificial agent confronts all the perceptual steps we take for granted, somewhat reminiscent of the experiences of British neurologist Dr. Oliver Sacks when confronted with patients suffering from very specific neurological damage.

DeepSteward developed from a broader exploration into environmental machine learning called Random Forests. This was a one-year research program based on fieldwork sessions with selected teams in locations that related to specific research questions. In turn, this research stemmed from a longer research project with a similar approach called Machine Wilderness, which challenged the convention of perceiving machines as assets of the human domain (in the sense that the biosphere and technosphere do not exist as separate realms). This may seem obvious, but it’s hard to find an example of technology that is inclusive of all life. After centuries of designing infrastructure only for humans (and domesticated animals), we are gradually stepping into multispecies design methodologies. Of course, this is because the industrial revolution has decimated other organisms to a level where entire ecosystems are collapsing, but also because it is mind-numbingly boring to include only one specific species of ape.

Fieldwork

I’ll conclude by stating my perception: fieldwork is a crucial ingredient to artistic practice. Fieldwork isn’t just being outside, as stated by eminent landscape thinker Jan de Graaf, but is a method of enquiry that starts from radical non-isolation of participants: perceiving, being and working in full exposure to the complexities and subtleties of an environment; navigating in collaboration with local experts, including artistic or scientific researchers, indigenous tribes or semi-traditional hunter/gatherers. Fieldwork is a multi-sensory exploration based on direct experience, open-ended experimentation and in-situ prototyping that starts from local circumstances, complexities, and relations. Enquiry is an embodied act that seeks, in the words of Jens Hauser, to be “un-split” from environmental processes, natural cycles, climatic conditions, seasons, (non)human cultures; and, collectively, may be captured by the term otherness.