Automating Environmental Interventions

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= “After decades of political indifference toward addressing the climate crisis, a growing number of ecologists have sought ways to automate environmentalism.”

Contextual Quote

On the Post-Anthropocene

“Insofar as the Anthropocene refers to an epoch in which human beings have outsize influence on planetary processes, perhaps we shouldn’t strive for a “good” Anthropocene at all, in which new technologies shore up a human-centered world. Rather, it may be worth hastening the arrival of what theorist Benjamin Bratton has called the “post-Anthropocene,” in which “Homo sapiens is no longer the dominant geological actor.” Along these lines, we can imagine how automated, artificial landscapes might coincide with an amplification of plant and animal agencies.”

- Jason Rhys Parry [1]


Jason Rhys Parry:

“There is a glaring discrepancy between the sophistication of our tools for monitoring earth and the sluggishness with which we respond to the alarming information they provide. As the undersea arrays, data processing centers, satellites, receiving stations, radar platforms, and aerostats bring us ever more refined images of our biosphere’s collapse, they also, in a sense, index the failure of our institutions to react. Beyond collecting climate data, environmental sensors pick up the signs of political paralysis and corruption.

But it hardly takes sophisticated measuring instruments to become concerned about the environmental movement’s political effectiveness. Though the UN’s first Earth Summit was hosted nearly 50 years ago in Stockholm, international action so far has culminated in the Paris Agreement of 2015: an accord which, even if followed to the letter, would bequeath the future a world without coral reefs or a West Antarctic Ice Sheet.

In response to decades of political indifference, if not hostility, toward addressing the climate crisis, a growing number of ecologists, engineers, and landscape architects have sought ways to automate environmentalism, ostensibly bypassing political gridlock and other forms of institutional resistance to urgent change. Their proposed interventions range from undersea drones tasked with destroying coral-reef-killing starfish to devices for mitigating toxic algae blooms. But in general, they aim to establish what they see as a more functional feedback loop between data about the planet and interventions to change it. In effect, they promise to outsource the work of climate adaptation to the sensors themselves and whatever tools are placed at their disposal, taking it out of the hands of politicians and the constituencies they represent. This, they suggest, would ensure a more consistent response to changing environmental conditions. For instance, architect Bradley Cantrell and his co-authors have outlined a scenario in which, as a response to climate change, an “artificially intelligent infrastructure” would “create and sustain nonhuman wildness without the need for continuing human intervention.” The thinking is that because no place is spared from human activity, whether in the form of climate change or the spread of invasive species, undoing human effects will require more intervention in a landscape, not less. In this vision, machinic sensing systems monitor changes in a landscape, cross-reference them with predictive models of the near future, and administer a response they deem appropriate using the technical prostheses of drones and other robots. For example, drones might plant seeds to adjust nitrogen levels in the soil while robots would modulate the course of rivers in time with the slow-motion creep of sea levels. In theory, this system’s artificial intelligence would discover new, more effective approaches to conservation, the way DeepMind invented new Go strategies.

It all calls to mind Richard Brautigan’s vision of “cybernetic ecology” in his 1967 poem “All Watched Over by Machines of Loving Grace”: “I like to think of a cybernetic meadow, where mammals and computers live together in mutually programming harmony.” Brautigan imagined a kind of fully automated luxury primitivism — a world in which flower-like computers populate the woods alongside deer while humans are “free of our labors / and joined back to nature.”

But Cantrell’s proposal for automating conservation has been criticized. Often, the more sophisticated machine learning systems are, the more inscrutable their operating logic becomes. As a result, it could be exceedingly difficult to understand the reasons an automated environmental manager would be making particular decisions. Opacity would be one consequence of the system’s overall efficiency.””