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==== Environmental Machine Learning ====

=== research thread: ===
[[groworld_hpi]] > [[http://boskoi.org|Boskoi]] 2010 > [[http://augmentedecology.com|Augmented Ecology]] 2014 > [[http://machinewilderness.net/|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' that forms in artificial neural networks during training.
  * how do animals, plants or machines learn through experience and exposure? (+cognitive biases)
  * (how) could an AI become environmentally literate? (+ implications)
  * what does a 'synthetic worldview' mean for the understanding/appreciation of environmental complexity?
  * how do strategies of environmental observation compare/relate (in AI, choreography, ecology, art, landscaping, traditional cultures,..)
  * who is the observer in these experiments? what kind of power-relations come out? (+symbiogenesis)
  * thalience: can environments be given their own voice? (or one that can reach modern humans on more level power-relations)

=== 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.

=== 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.


=== methods: ===
  * fieldwork: in-situ exploration through interactions between man-machine-environment 
  * prototyping: like in Boskoi & Machine Wilderness with experiments as vehicles for materializing questions
  * 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: (under construction)===
  * **dec 2017 / nov 2018** > EML Meetup series at MidWest Experimental Station Amsterdam > //theme: Synthetic Environmental Literacy//
  * **mar 2018** > fieldwork session Terschelling > //theme: Random Forests: Environmental observation and perception into algorithm// >  8 ppl / 3 or 4 days
  * **may 2018** > fieldwork session Finland > //theme: Rules of Engagement: Machine and Animal interactions// >  4 ppl / 10 days
  * **nov 2018** > critical reflection / writing, web or print >  //Fieldguide to Environmental Machine Learning//
  * exhibition (Artis Zoo?)
  * <del>//Plain Air Nouveau// EU program</del>

=== 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/
  * [[https://jonlefcheck.net/2015/02/06/a-practical-guide-to-machine-learning-in-ecology/|A Practical Guide To Machine Learning In Ecology]]
  * [[https://www.wildlabs.net/resources/thought-pieces/machine-learning-meet-ocean|Machine Learning, Meet Ocean]]
  * [[http://www.cell.com/trends/ecology-evolution/fulltext/S0169-5347(16)30237-3?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0169534716302373%3Fshowall%3Dtrue|Designing Autonomy: Opportunities for New Wildness in the Anthropocene]]
  * [[https://www.theatlantic.com/technology/archive/2017/06/how-do-buddhist-monks-think-about-the-trolley-problem/532092/|"What do Buddist Monks Think about the Trolley Problem"]]
  * [[https://topdata.news/how-the-random-forest-algorithm-works-in-machinelearning/|"How The Random Forest Algorithm Works In Machine Learning"]]
  * [[https://quantdare.com/random-forest-many-is-better-than-one/|"Random Forest: Many Is Better Than One"]]
  * [[https://deepmind.com/blog/understanding-agent-cooperation/Understanding Agent Cooperation]]
  * [[https://blogs.nvidia.com/blog/2017/02/23/ai-rainforest-biodiversity/|Tree’s Company: AI Maps Biological Riches of the Rainforest]]
  * [[https://www.theatlantic.com/amp/article/520713/|Artificial Intelligence: The Park Rangers of the Anthropocene]]
  * [[http://www.sciencedirect.com/science/article/pii/0304380089900665|Artificial intelligence and expert systems in ecology and natural resource management]]
  * [[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://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://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, just listed here 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]]
  * [[https://placesjournal.org/article/mappings-intelligent-agents/|Mapping Intelligent Agents]]
  * [[https://scout.ai/story/the-next-software-revolution-will-rewrite-life-as-we-know-it/|The Next Software Revolution Will Rewrite Life As We Know It]]
  * [[https://futurism.com/the-key-to-understanding-ai-may-be-buried-in-the-laws-of-physics/|Key to AI may be in physics]]
  * [[https://www.theguardian.com/technology/2017/oct/04/google-deepmind-ai-artificial-intelligence-ethics-group-problems| DeepMind announces ethics group]]
  * [[https://medium.com/@daviddao/predictive-smart-contracts-dc15b9986d8c|David Dao (of Gainforest) on Medium]]
  * [[https://www.forbes.com/sites/forbestechcouncil/2017/11/16/why-decentralized-artificial-intelligence-will-reinvent-the-industry-as-we-know-it/#78178535511a| On decentralized AI and Federated Learning ]]
  * [[https://www.nature.com/articles/d41586-017-08675-7|Lucas Joppa in Microsofts AI for Earth programme]]
  * [[https://www.ercim.eu/publication/Ercim_News/enw61/paysley.html|Modelling Ecological Health using AI Techniques]]
  * [[http://ml-playground.com/|Machine Learning Playground ]]
  * [[https://www.technologyreview.com/s/609048/the-seven-deadly-sins-of-ai-predictions/|The Seven Deadly Sins of AI Predictions]]
  * [[https://ml4a.github.io/ml4a/how_neural_networks_are_trained/| Machine Learning For Artists - How neural networks are trained]]
  * [[https://deepindex.org/|DeepIndex - lists what AI can do by now ]]

=== 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]]
  * [[https://www.theguardian.com/commentisfree/video/2016/mar/16/artificial-intelligence-we-should-be-more-afraid-of-computers-than-we-are-video|AI: we should be more afraid of computers than we are]] Guardian video (also [[https://www.theguardian.com/culture/2017/feb/21/what-our-original-drama-the-intelligence-explosion-tells-us-about-ai| Gunter the Robot]])
  * [[https://www.ted.com/talks/stuart_russell_how_ai_might_make_us_better_people#t-589195|TED: Stuart Russell rules for making AI more human compatible]]

=== framing: ===
  * [[http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00058/full#h4|Embodied cognition is not what you think it is]]
  * [[https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer|Your brain does not process information and it is not a computer]]
  * [[thalience]]
  * Panpsychism
  * Sasi / Rahui / Mo     
    * An institutional analysis of SasiLaut in Maluku, Indonesia [[https://www.researchgate.net/publication/23550908_An_institutional_analysis_of_SasiLaut_in_Maluku_Indonesia]]
    * Through a Green Lens: The Construction of Customary Environmental Law and Community in Indonesia's Maluku Islands [[https://institutionalorganizationalecologies.files.wordpress.com/2011/01/zerner-sasi.pdf]] 
    * The Meaning of Mo: Place, Power and Taboo in the Marshall Islands[[https://openresearch-repository.anu.edu.au/bitstream/1885/116113/4/Ahlgren%20Thesis%20final%202017.pdf]] Sections on Cultural Ecology, Ecological Knowledge, Scared ecologies. Broad, detailed discussion of use or traditions of taboo in context of modern conservation & resource management.

<blockquote>
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</blockquote>

=== initiatives: ===
  * [[https://www.microsoft.com/en-us/aiforearth|Microsoft AI for Earth]]
  * [[https://www.zsl.org/conservation/conservation-initiatives/conservation-technology/machine-learning|Zoological Society London]]
  * [[http://www.terra0.org/|Terra0]]
  * [[http://gainforest.org/|Gainforest]]
  * [[https://www.regen.network/|Regen.Network]]
  * [[http://www.handsfreehectare.com/|HandsfreeHectare]]
  * [[https://www.biocarbonengineering.com/|BioCarbonEngineering]]

=== see also: === 
  * [[machine learning]]
  * [[machine ecology]]
  * [[Robust Physical Perturbations]]
  * [[Environmental blockchain]]
\\
Project website: [[http://randomforests.nl/|randomforests.nl/]]