AI enables a Who's Who of brown bears in Alaska Being able to distinguish individual animals - including their unique history, movement patterns and habits - can help scientists better understand how their species function, and therefore better manage habitats and study population dynamics. Today, most computer vision systems for tracking animals are effective on species with patterns and markings, such as zebras, leopards and giraffes. The task is much more complicated for unmarked species where individual differences are harder to spot. Distinguishing a particular brown bear from its peers in a non-invasive way requires an incredible eye for detail and years of viewing the same bears over time. What's more, these bears emerge from hibernation in the spring with shaggy fur and having lost quite a bit of weight and then substantially increase their body weight feasting on salmon, as well as fully shedding their winter coat - that's enough to throw off experts as well as AI algorithms.

In case you didn't know, it's Fat Bear Week--the annual tournament that pits the brown bears of Katmai National Park against each other to see who gained the most weight over the year. You can vote for your favorite bear in a March Madness-style bracket where one is crowned the biggest bear of the bunch by the week's end. It's a fun and cheeky project hosted by the U.S. National Parks Service and Explore.org, a multimedia organization best known for the live cams of wildlife like the Brooks Falls Brown Bears of Katmai National Park. While the stream enjoys a healthy and devoted following throughout the year--folks who have created fan wikis, forums, and stan communities for individual bears--its viewer count balloons like its eponymous creatures when Fat Bear Week rolls around. And, as it turns out, it's these very same viewers who can help keep the bears alive and thriving in a world that's rapidly endangering their ecosystems. "We have thousands who watch the bear cams, especially right now with Fat Bear Week," Ed Miller, the co-founder of the BearID Project conservation group, told The Daily Beast.

Researchers say the new technology, termed BearID, created a'non-invasive' technique to study the animals. Despite a decade of behavioural research on grizzly bears in B.C.'s Knight Inlet, Melanie Clapham still has trouble telling some individual bears apart. Brown bears, which include grizzly bears, can change dramatically in their appearance during their younger years and, unlike other wildlife that has spots or stripes, they lack distinguishing markings on their bodies. Ms. Clapham, a conservation biologist and postdoctoral research fellow at the University of Victoria, dreamed of technology that could help her individually identify these furry mammals. While she was looking for a tech team to make that idea possible, south of the border, Ed Miller and Mary Nguyen, two Silicon Valley engineers who are also outdoor and wildlife enthusiasts, had started a project to develop machine-learning models that could be adapted to grizzly bears.

Grizzly bears have domed shoulders, tall foreheads, and pale-tipped fur that gives them their grizzled appearance. If you're comparing two bears, one might be lighter or darker in color, or fatter for hibernation. But for the most part, there's no universal, unique marker a person can use to tell two bears apart. This issue is a challenge for scientists like University of Victoria wildlife conservationist Melanie Clapham, whose research on grizzly bear behavior requires her to monitor individual bears over years, Adam van der Zwan reports for CBC. But now, Clapham and her research team have developed a solution: facial recognition for bears. Bears grow and shrink a lot depending on the season, and their appearance changes frequently during their 20- to 25-year-long lifespans.