Until recently the ability to make sense of the environment was limited to biological beings. Now machines are starting to blur those lines.
This programme investigates the emerging field of environmental machine learning through fieldwork and critical reflection.
Fieldwork session #1 @ Imrama Terschelling march 19to23/2018
Scenario: an environmental AI starts to act autonomously
With: Jan de Graaf, Jeroen van Westen, Theun Karelse, Michelle Geraerts, Sjef van Gaalen, Sander Turnhout, Paul Seidler, Tivon Rice
Fieldwork session #2 @ Ars Bioarctica Finland may/2018
Theme: the role of AI in artistic and scientific fieldwork / how artificial agents learn from non-humans
With: Antti Tenetz, Ian Ingram, Shah Selbe, Theun Karelse
Debate @ Pakhuis de Zwijger
Theme: what light can art and literature shine on new relations between the artificial and the natural
With: Maxim Februari, Hans Schnitzler, Theun Karelse
Fieldwork session #3 @ Dinacon Thailand June 22/2018
Theme: investigating algorithmic companionspecies in relation to non-humans
With: Sjef van Gaalen
Lab @ Border Sessions festival
Theme: autonomous agents for regenerative ecology
With: Klaas Kuitenbrouwer, Sjef van Gaalen, Theun Karelse
Fieldwork session #4 @ MAAJAAM Estonia Juli 11to25/2018
Theme: digital natives and non-natives
Incl: Brian House, Antti Laitinen, Paula Vitola, Aivar Tõnso, Timo Toots, Taavi Suisalu, Theun Karelse
Random Forests walk @ Sciencepark October 15/2018
Theme: training-forests for autonomous agents
With: Arita Baaijens, Theun Karelse
Seminar @ Climate as Artifact exhibition
Theme: environmental literacy
Incl: Semuel Sahureka, Michelle Gerearts, Sjef van Gaalen, Theun Karelse,.. more soon..
Pioneers like al Jazari already made programmable automata around 1200AD. Complex machines have therefore been part of our environ- ment for many centuries. Technological infrastructures came to really dominate our landscapes since the Industrial Revolution. The word that comes to mind is brutality. Edward O. Wilson described our current age of mass extinction as the ‘Age of Loneliness’ and in many ways our technologies in these shared and biodiverse environments have been technologies of loneliness that serve economies rather than life. Machinery is beying developed within a frame of reference that leaves the needs of nonhumans (and quite possibly humans) out of view.
RandomForests is a fieldwork program interested in the potential of machines for environmental learning. Until very recently the ability to relate to the environment was limited to plants and animals, but now machines are starting to blur those lines. Random Forests explores what environmental machine learning could entail and if an artificial agent could become environmentally literate. What does this emerging 'synthetic world- view' mean for the appreciation of environmental complexity and the power-relations between our technologies and their environment? Could environmental literacy in the artificial agents that populate our environment create any opening towards practices of environmental solidarity, intimacy, affinity, allegiance, reverence, commitment and kinship?
When landscape appeared in European art it emerged first as a landscape of symbols. The Gothic depiction of Earth was populated with features that were primarily there 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.
With Van Eyck the environment first appears as a landscape of fact according to the eminent art-historian Kenneth Clarck. “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 machine learning from domains such as precision agriculture, mining, forestry, autonomous transport and indeed ecology.
In 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 a strange collection of species, including for instance many hundreds of dog breeds. These sets of animals and plants do not refer to any existing ecosystem, but are a hand-picked bunch of species that are of interest to humans in some way. When such an AI is introduced to a real-world terrain these pretrained sets prove about as relevant as cat-videos to an marine-ecologist. In fact when we turned the camera-eye to the direct surroundings of the Kilpisjarvi Biological Research Station during our Ars Bioarctica residency in the Finnish Arctic in 2018, it was looking out across a snow-covered terrain full of hundreds of birch trees, lichen-covered rocks and perhaps some passing birds, but when we asked the AI what it saw it told us it saw snowmobiles. There were none. It was hallucinating. It was hallucinating a landscape full of snow-mobiles. And perhaps more strikingly, it didn't see the trees.
The world view in these platform A.I.s is largely populated by human artifacts, ranging from snowmobiles, to vacuum-cleaners, and even guillotines. The worldview of 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. So, it turns out cyborgs do not dream of electric sheep, but have much more commodified imaginations as platform A.I.s of late capitalism grow up in corporate environments. With much of our current environmental predicament stemming from anthropocentric bias, this raises the question: should our machines learn exclusively from humans, should their natural habitat be corporate, or do intelligent machines need training-forests, like 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?