part-time Terschelling inhabitant and urban planner Jan de Graaf | landscape artist Jeroen van Westen | nature conservationist and researcher Sander Turnhout | Cultural Anthropology student Michelle Geraerts | researcher and designer Sjef van Gaalen | artist and interaction designer Paul Seidler | artist and educator Tivon Rice | primary school pupil Jackson Rice | artist and researcher Theun Karelse
Outcomes / Notes
A glossary of terms
DATES: Arrive 19th March, workdays 20/21/22 ends 23rd.
This first RandomForests fieldwork session starts on the island of Terschelling, surrounded by the Wadden Sea which has a UNESCO natural world heritage status. The fieldwork is based on a speculative scenario: an AI tasked with protecting UNESCO natural world heritage starts to act autonomously. What would be its sources of information, what would be points where it could intervene.
In a way, we study the AI like an organism in this hybrid habitat. From this specific case study we may be able to extract potential interactions and feedback loops between an AI and landscapes more generally. We hope to unearth some of the biases an AI might develop, reveal the roots of those biases and see how they compare to human environmental biases. Some types of environmental knowledge are easy for humans, some more difficult. That is probably true for an AI. It would be interesting to see where they differ, match or complement each other. Could an AI serve as a canary in a coalmine, an environmental guide, or even mentor?
Algorithms underpin the global technological infrastructure that extracts and develops natural resources such as minerals, food, fossil fuels and living marine resources. They facilitate global trade flows and they form the basis of environmental monitoring technologies. These algorithms are becoming more autonomous as Artificial Intelligence emerges. It's a process that deserves more of our attention, because of the potential impact of AI on our landscapes and the way we relate to our environment. This fieldwork aims to investigate some these layers of influence.
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 fieldwork session is organised in collaboration with the Wadgasten programme by Jan de Graaf and Jeroen van Westen.