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DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that’ll help study the behavior of animal species in Tanzania’s Serengeti National Park. It hopes to expedite the analysis of data from hundreds of motion-detecting field cameras, which have captured millions of images since they were deployed by the Serengeti Lion Research program over nine years ago.

“The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals … As human encroachment around the park becomes more intense, these species are forced to alter their behaviours in order to survive,” wrote DeepMind in a blog post. “Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviors and population dynamics, but these changes have occurred at spatial and temporal scales which are difficult to monitor using traditional research methods.”

For nearly a decade, conservationists have tapped the aforementioned cameras to keep tabs on animals within the park’s core, enabling them to study their distribution and demography. The images aren’t of much use absent annotations, however, which is why it’s fallen to volunteers to identify species by hand using a web-based tool called Zooniverse. The resulting corpora currently features counts for around 50 different species, but it still takes nearly a year from the time a camera is triggered until labels are generated.

DeepMind ecological AI

Above: An ostrich roaming the Serengeti.

Image Credit: DeepMind

That’s why DeepMind used the Snapshot Serengeti dataset to train an AI model that can automatically detect, identify, and count animals. Scientists at the company claim their pretrained system, which will soon be deployed in the field, can perform on par with (or better than) human annotators for most of the hundreds of species in the region. Moreover, they say that it shortens the data processing pipeline by up to nine months, and that it could soon run safely run on “modest” hardware with unreliable internet access.


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“We’ve developed a robust model for detecting and analyzing animal populations in field data, and have helped to consolidate data to enable the growing machine learning community in Africa to build AI systems for conservation which, we hope, will scale to other parks,” wrote DeepMind. “Our hope is to contribute towards making AI research more inclusive — both in terms of the kinds of domains we apply it to, and the people developing it.”

DeepMind is far from the first to apply AI to ecology. Microsoft recently highlighted a Santa Cruz-based startup — Conservation Metrics — that’s leveraging machine learning to track African savanna elephants. Separately, a team of researchers developed a machine learning algorithm trained on Snapshot Serengeti that can identify, describe, and count wildlife with 96.6% accuracy. Intel’s TrailGuard AI system prevents poaching by detecting motion with an embedded camera using an offline, on-device AI algorithm. And scientists at Queensland University used Google’s TensorFlow machine learning framework to train an algorithm that can automatically detect sea cows in ocean images.

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