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A cutting-edge AI technique called deep learning can automatically identify, count and describe animals in their natural habitats. Photographs that are automatically collected by motion-sensor cameras can then be automatically described by deep neural networks.
"This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into 'big data' sciences. This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems," says Jeff Clune, the senior research manager at Uber's Artificial Intelligence Labs.Deep neural networks are a form of computational intelligence inspired by how animal brains see and understand the world. They require vast amounts of training data to work well, and the data must be accurately labeled (each image being correctly tagged with which species of animal is present).
This study obtained the necessary data from Snapshot Serengeti, a citizen science project that has deployed a large number of camera traps (motion-sensor cameras) in Tanzania that collect millions of images of animals in their natural habitat, such as lions, leopards, cheetahs and elephants. Crowd sourced teams of human volunteers were asked to label each image manually. The study harnessed 3.2 million labelled images produced in this manner by more than 50,000 human volunteers over several years.
Not only does the artificial intelligence system tell you which of 48 different species of animal is present, but it also tells you how many there are and what they are doing. It will tell you if they are eating, sleeping, if babies are present, etc, said Margaret Kosmala from Harvard University.
