ARS BIOARCTICA SESSION

Fieldwork outline

Participants

artist and naturalist Antti Tenetz | artist Ian Ingram | conservation technologist Shah Selbe | artist and researcher Theun Karelse
DATES: Arrive 6th May, workdays 7/12, ends 13rd.

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Fieldwork session #2

Some first applications for machine learning are being developed by ecologists as a way to address heterogeneity issues in their data, or to harvest wildlife sightings from the web by identifying species in pixels and soundfiles. EML aims to take a less task oriented view; what the appearance of machine learning in biodiverse environments could mean. What does it mean if machines join animals and plants there on more equal levels of awareness?

This is the second RandomForests fieldwork session at the Kilpisjarvi Biological Research Station in the Finnish polar region. The team consists of people with a landscape practice that is rooted in in-situ experimentation and prototyping. The residency explores the role of AI in artistic and scientific fieldwork. Can AI function as an intermediate in environmental investigation? As a mercurial companion, an interspecies informant, an environmental messenger, a climatic guide guide, ghost or even mentor?

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Thalience

The session at Kilpisjarvi also aims to explore the artificially intelligent agent in its own right. As Karl Schroeder describes in the Hamburg Manifesto in Ventus: We don't want machine copies of our own minds, we want to give the natural world itself a voice. This is his core notion of Thalience, an attempt to give nature a voice without that voice being ours in disguise. It is the only way for an artificial intelligence to be grounded in a self-identity that is truly independent of its creator's. Can machines learn from non-humans? Can they evolve through direct exposure to landscapes? It is a picture in which environment shapes behavior like a psychogeography for robots. The Jungle Book-model of engineering.

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note: Random Forests are a type of analysis in machine learning in which a large number of simpler operations called 'Decision Trees' are combined. This 'Random Forest analysis' only uses portions of the selectors (at random) on the data, so individual trees can vary more, which increases accuracy.

This Ars Bioarctica fieldwork session is organised in collaboration with the Finnish Bioart Society.

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