groworld_hpi > Boskoi 2010 > Augmented Ecology 2014 > Machine Wilderness 2015 > Environmental Machine Learning (2018)
Complex machines have been part of our environment for many centuries. Pioneers like al Jazari already made programmable automata around 1200AD. Machines came to dominate the land, sea and air dramatically since the Industrial Revolution. 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. What does it mean if machines join animals and plants there on more equal levels of awareness? Environmental Machine Learning is a program of fieldwork sessions with experiments as vehicles for materialising questions.
All mayor tech companies have made AI their top priority, some say in a race to file patent applications. In any case these systems are not just reaching into the depths of human society and media, but also root deeply into the remotest mangroves, deserts or reefs. Some first experiments with machine learning have been undertaken by ecologists. EML aims for a fundamental exploration of environmental literacy and how this could be made accessible to / obtained by an AI.
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 with commodities and they form the basis of environmental monitoring technologies. Last but not least, algorithms embedded in devices and services affect our behavior - what we buy and consume and how we travel, with indirect but potentially strong effects on the biosphere. As a result, algorithms deserve more scrutiny.
- Victor Galaz / Stockholm Resilience Centre