Mission Impossible: Researchers Hope AI Can Solve Worst of World Issues
From global climate change to poverty, certain global problems seem unsolvable and perpetually vexing to those who seek to solve them. With AI and Big Data, however, that may change.
According to an article by scientist Andrew Zolli in the Stanford Social Innovation Review, the ability to collect massive amounts of data, parse it with algorithms and employ machine learning has the potential to tackle the most challenging problems and resolve them over a series of years. Each problem that seems impossible to solve has several common characteristics: they’re “messy,” they are connected to “perverse incentives” which drive behavior that perpetuates them, their dynamics are “opaque,” or not clearly understood, even by longtime researchers and they can be made “worse” by what might seem like rational responses. To top it off, they are often so complicated and broad in nature that no one entity assumes responsibility for solving them.
That’s where AI can change everything.
“For real progress to occur, we’ve got to move these externalities into the global system, so that we can fully assess their costs, and so that we can sufficiently incentivize and reward stakeholders for addressing them and penalize them if they don’t,” Zolli writes. “And that’s going to require a revolution in measurement, reporting, and financial instrumentation—the mechanisms by which we connect global problems with the resources required to address them at scale.”
Zolli asserts that remote sensing and big data, artificial intelligence and cloud computing combined will enable globally based groups to tackle these problems with better abilities to address the issues. Already, one issue–the loss of rainforests–is becoming better understood due to mass scale collection of data.
Nowhere is this opportunity clearer than in the example of protecting vulnerable forests, many of which are large, remote, and located in low- and middle-income countries with limited financial and human resources. The Amazon rainforest, for example, is roughly nine times the size of Texas; it’s far too big to regularly patrol anything more than a fraction of it from the ground or even by air. Meanwhile, the economic pressures to deforest, or to convert land for agriculture, mining, or other purposes, remain intense.
Since timber clearing is the major cause of rainforest destruction and is hard to detect, new technologies will enable governments to more quickly identify loss and stop it, for instance. Satellites can spot illegal roads built before the clearing begins, and forest managers can see deterioration afterward if the signs were missed.
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