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| environmental_machine_learning [2017-07-23 06:45] – theunkarelse | environmental_machine_learning [2017-10-15 09:17] – theunkarelse |
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| === potential starting questions: === | === 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. | * if/how the concept of the 'umwelt' in biological creatures relates to the 'world view' that forms in artificial neural networks during training. |
| * how do animals, plants or machines learn through experience and exposure? | * how do animals, plants or machines learn through experience and exposure? (+cognitive biases) |
| * (how) could an AI become environmentally literate? | * (how) could an AI become environmentally literate? (+ implications) |
| * could such a 'synthetic' worldview deepen the understanding/appreciation of environmental complexity? (bypass human cognitive biases) | * what does a 'synthetic worldview' mean for the understanding/appreciation of environmental complexity? |
| * could machine learning expand environmental literacy in technology/society? | * how do strategies of environmental observation compare/relate (in AI, choreography, ecology, art, landscaping, traditional cultures,..) |
| * how do strategies of environmental observation compare/relate in AI, choreography, ecology, art, landscaping, traditional practices, etc.. | * who is the observer in these experiments? what kind of power-relations come out? (+symbiogenesis) |
| * 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?) | * thalience: can environments be given their own voice? (or one that can reach modern humans) |
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| | === blurb: === |
| | 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. |
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| | === context: === |
| | 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. |
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| === methods: === | === methods: === |
| * fieldwork: exploration through interactions between man-machine-environment in-situ | * fieldwork: exploration through interactions between man-machine-environment in-situ |
| * prototyping: like in Machine Wilderness, not as experimental devices that generate answers; but experimental systems as vehicles for materializing questions | * prototyping: like in Boskoi & Machine Wilderness with experiments as vehicles for materializing questions |
| * critical reflection | * 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? | * 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? |
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| === program: === | === program: (under construction)=== |
| * fieldwork session Kilpisjarvi Biological Research Station, Finland? | * **dec 2017 / nov 2018** > EML Meetup series at MidWest Experimental Station Amsterdam > //theme: Synthetic Environmental Literacy// |
| * fieldwork session GBSanctuary, Kerala, India? | * **may 2018** > fieldwork session Finland > //theme: Rules of Engagement: Machine and Animal interactions// > 4 ppl / 10 days |
| * critical reflection / writing, web or print? | * **sep 2018** > fieldwork session Terschelling > //theme: Random Forests: Environmental observation and perception into algorithm// > 10 ppl / 2 or 3 days |
| | * **nov 2018** > critical reflection / writing, web or print > //Fieldguide to Environmental Machine Learning// |
| | * exhibition (Artis Zoo?) |
| | * //Plain Air Nouveau// EU program |
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| === reading: === | === reading: === |
| * [[https://animalbiotelemetry.biomedcentral.com/articles/10.1186/s40317-017-0123-1|Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry]] | * [[https://animalbiotelemetry.biomedcentral.com/articles/10.1186/s40317-017-0123-1|Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry]] |
| * [[https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Intelligent-Biosphere|Intelligent Biosphere]] | * [[https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Intelligent-Biosphere|Intelligent Biosphere]] |
| | * [[https://www.wired.com/story/elon-forget-killer-robots-focus-on-the-real-ai-problems/|Forget Elon Musk, lets focus on real AI problems]] |
| * [[https://www.nrc.nl/nieuws/2017/06/27/niet-waar-de-robots-bij-zijn-11294270-a1564606|Not In Front Of The Bots (dutch)]] | * [[https://www.nrc.nl/nieuws/2017/06/27/niet-waar-de-robots-bij-zijn-11294270-a1564606|Not In Front Of The Bots (dutch)]] |
| | * [[https://blogs.microsoft.com/on-the-issues/2017/07/12/announcing-ai-earth-microsofts-new-program-put-ai-work-future-planet/|Microsoft: AI for Earth]] |
| | * [[https://thenextweb.com/tq/2017/09/20/we-desperately-need-ethical-algorithms-heres-why/#.tnw_HMT7SwPK|We Desperately Need Ethical Algorithms, Here's Why]] (not that insightful, I just list it to document a quote) |
| | * [[https://www.propublica.org/series/machine-bias|Machine Bias]] |
| | * [[https://medium.com/@memoakten/retune-2016-part-2-algorithmic-decision-making-machine-bias-creativity-and-diversity-3c7cca21ba37|Memo Akten's Medium pages]] |
| | * [[https://www.quantamagazine.org/new-theory-cracks-open-the-black-box-of-deep-learning-20170921| Crack open the black box of deep learning]] |
| | * [[http://nautil.us/issue/52/the-hive/the-perils-of-letting-machines-into-the-hive-mind|The Perils of Letting Machines Into the Hive Mind]] |
| | * [[http://www.karineskoog.com/thehunter-call-of-the-wild-designing-believable-simulated-animal-ai/|AI & Designing Believable Simulated Animals]] |
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| | === Youtube Lectures: === |
| | * introduction AI for Earth: [[https://youtu.be/vDC5T9Wvgeo|https://youtu.be/vDC5T9Wvgeo]] |
| | * [[https://norbertbiedrzycki.pl/en/artificial-brains-save-the-earth/|Artificial Brains Save The Earth]] |
| | * [[https://www.youtube.com/watch?v=i_uL_nCv2g4|Machine Learning in Ecological Science and Environmental Management, Thomas Dietterich]] |
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| === framing: === | === framing: === |
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| === see also: === | === see also: === |
| * [[machine_learning]] | * [[machine learning]] |
| * [[machine_ecology]] | * [[machine ecology]] |
| | * [[Robust Physical Perturbations]] |
| * http://machinewilderness.net | * http://machinewilderness.net |
theunkarelse