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The Environment is Not a System | a peer-reviewed journal about_
In late 2017, Microsoft’s chief environmental scientist, Lucas Joppa announced AI for Earth, a new initiative to put artificial intelligence in the hands of those who are trying to “monitor, model and manage the earth’s natural systems”. AI for Earth gives environmental researchers access to Microsoft’s cloud platform and AI technologies, and similar to recent initiatives by companies like Google and Planet Labs, it aims to integrate AI into environmental research and management....

He urges: “for every environmental problem, governments, nonprofits, academia and the technology industry need to ask two questions: ‘how can AI help solve this?’ and ‘how can we facilitate the application of AI?’”...

The word eco-system itself stands as a reminder that the history of ecology is enmeshed with systems theory and presupposes that species entanglements are operational or functional. More surreptitiously, a systematic view of the environment connotes it as bounded, knowable and made up of components operating in chains of cause and effect. This framing strongly invokes possibilities of manipulation and control and implicitly asks: what should an ecosystem be optimized for?...

Does AI have any potential to reveal environments in ways that escape the trapping of systems? Critical to my inquiry is the recent work of Anna Tsing and what she calls, “the arts of noticing”. Tsing’s work offers a starting point for thinking outside of both a systems framework and assumptions of progress (17). Her perspective on ecology and the lifeworlds it describes unfolds and emerges through “encounters” (20) which bring together entities, transforming them in indeterminate ways. Might AI operate through modes of environmental encounters or will it simply amplify “an informatics of domination” (Haraway 162)?...

A systems view of the environment reinforced by computation, has numerous precedents, including 18th and 19th century attempts at scientific forest management....

The scientific forest failed by its own criteria: timber yield. However it is worth acknowledging that if yield had remained high while biodiversity declined, this history of sustainable environmental management would be remembered as a success, analogous to industrial agriculture. Tsing calls environments that are simplified and optimized to produce commodities “plantations” (435). ...

Despite this labor, the Grasslands model, like similar large-scale ecological modeling programs of the time, revealed very few new ecological principles. Deemed “too simplified biologically” despite implementing an unprecedented number of variables (Coupland 154), the model was built with an assumption of default equilibrium. Coupland argues that the Biome Model was simply “a sophisticated version of a cybernetic system […] and cast […] the ecologist in the role of systems engineer” ...

How had NASA’s satellites missed such a marked change in ozone composition? One response from NASA suggests that their data processing software was programmed to discard readings that appeared to be outliers, thus ignoring the drastic changes that were occurring in ozone concentration (Farman). In this case, reality itself was an outlier and assumed to be an error...

Machine learning assumes that enough data can be collected to adequately represent and make predictions about reality. In the context of the environment, this is an enormous challenge given the very limited size of our existing datasets. Another significant assumption is that the past is indicative of the future. ...

She continues: “I want to convey the sense of a provisional collection of parts in constant flux as some are added and others lost. The parts are not so tightly bound that transformations are inhibited and not so loosely connected that information cannot flow between parts” (118). Similarly, I take up assemblage as an imperfect descriptor to avoid the hubristic assumptions of a systems view. Stating “I am studying a grasslands assemblage” instead of “I am studying a grasslands system” produces a remarkable shift in expectations and assumptions. This simple substitution dismantles subtle assumptions of fixed categories of knowledge, as well as assumptions that engineering and control are always possible. Instead it foregrounds uncertainty and acknowledges the unknowability of the world....

Ethnography is one possibility. Tsing’s study of the matsutake mushroom explores what can be learnt from a Japanese mushroom, a lifeform that cannot be cultivated and that thrives in highly disturbed forests. Through her ethnography she shows how close attention inevitably facilitates transformation. Tsing calls this “the arts of noticing”, tactics for thinking without either the abstraction produced by quantification or deeply held assumptions of progress....

Could technologies like machine vision and remote sensing be used to amplify environmental encounters and the arts of noticing our ecological entanglements? The rise of digital naturalism sees the development of apps and initiatives that focus attention on the lifeforms in our various bioregions. Initiatives such as iNaturalist, Merlin Bird ID and eBird invite non-scientists to contribute environmental observations and use either crowd-sourced or “assisted identification” to identify species and build biodiversity databases.
artificial_intelligence  environment  sustainability  methodology  systems  ethnography 
3 days ago by shannon_mattern
Don’t take that tone with me!
Dealing with child being dick - give fact re your feeling, try play and connection, if not work then make them let out real upset by setting limits. Try this!
kids  systems  selfimprovement 
3 days ago by berran
System Design Primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
scaling  programming  systems 
5 days ago by amtiskaw
Legal Systems Very Different From Ours
Fascinating. Covers systems like gypsy law, Jewish law, Athenian law, etc.
law  history  legal  systems  overview 
5 days ago by brentfarwick

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