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There’s more to artificial intelligence (AI) than basketball-dribbling digital avatars and home robots.
Conservation Metrics, a recipient of Microsoft’s AI for Earth grant program, is using algorithms to analyze a corpus from Cornell University Lab of Ornithology’s Elephant Listening Project, which collects data from acoustic sensors embedded throughout Nouabalé-Ndoki National Park and adjacent logging areas in the Republic of Congo.
In the next few months, the startup plans to migrate its model from local machines to Microsoft’s Azure cloud servers, which McKown said will cut down the amount of time it takes to process a few months of sound data from three months to a single day.
“We’re really interested in speeding up that loop between having equipment monitoring things out in the field and going through this magic process to convert those signals into information you can send into the field where someone can take action,” he said. “Right now, that process can take a really long time.”
Conservation Metrics isn’t the only animal-tracking AI for Earth beneficiary employing machine learning, of course.
The Protection Assistant for Wildlife Security (PAWS), a research program spearheaded by the University of Southern California and Carnegie Mellon University, trained a model on 14 years of patrol data and more than 125,000 data points about poaching activities to predict where poaching might occur in the future.
Seattle’s Snow Leopard Trust, another grantee, worked with Microsoft engineers to build a computer vision algorithm that identifies animals in camera images with 95 percent accuracy. (It’d take a team of humans about 19,500 hours to sift through the database by hand.)
AI for Earth, which launched in 2017 in London with $2 million in funding, is now a five-year, $50 million program with 112 grantees in 27 countries.