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Environmental Machine Learning
research thread:
groworld_hpi > Boskoi 2010 > Augmented Ecology 2014 > Machine Wilderness 2015 > Environmental Machine Learning (2018?)
potential starting questions:
- if/how the concept of the 'umwelt' in biological creatures relates to the 'world view' (as Memo Akten called it) that forms in artificial neural networks during training.
- how do animals, plants or machines learn through experience and exposure?
- (how) could an AI become environmentally literate?
- could such a 'synthetic' worldview deepen the understanding/appreciation of environmental complexity? (bypass human cognitive biases)
- could machine learning expand environmental literacy in technology/society?
- how do strategies of environmental observation compare/relate in AI, choreography, ecology, art, landscaping, traditional practices, etc..
- thalience: how much of us humans is present in the 'robotic eye'? (Or more broadly: In an age of AI do we hold on to us being the humans?)
methods:
- fieldwork: exploration through interactions between man-machine-environment in-situ
- prototyping
- critical reflection
- multimodal and transdisciplinary approach: could the project also give room to explore observation strategies from various domains of human inquiry and probe them in-situ?
program:
- fieldwork session Kilpisjarvi Biological Research Station, Finland?
- fieldwork session GBSanctuary, Kerala, India?
- critical reflection / writing, web or print?
reading:
- “Adoption of Machine Learning Techniques in #Ecology and Earth Science. Thessen [2016]” » https://t.co/D1hOba8AY7
- “Machine Learning without Tears: A primer for Ecologists. Olden et al [2008]” » https://t.co/N1l1JKYqqh
- “Applications of machine learning in animal behaviour studies” > http://www.sciencedirect.com/science/article/pii/S0003347216303360
- “Intrinsic motivations and open-ended development in animals, humans, and robots: an overview” > https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4158798/
framing:
- Panpsychism