“Our ecosystem is just unravelling in front of our eyes, and the pace of environmental change can be really overwhelming,” says Loarie. “But in our handbags, there’s another thing that has had the same pace of unbelievable change—the cellphone.” He hopes that the latter can help with the former by acting as a pocket naturalist, a cross between Shazam and an old-fashioned field guide.

The iNaturalist site began in 2008 as the master’s project of three students, and has since blossomed into a thriving community of around 150,000 people. Together, they’ve captured around 5.3 million photos representing 117,000 species. By labeling these images and tagging where they were taken, the site’s users are conducting an inadvertent census of the world’s animals. And sometimes, they make surprising discoveries.

In 2011, Luis Mazariegos, a retired Colombian businessman, uploaded a picture of a striking red-and-black frog, found on the patch of rainforest land that he had recently bought. Frog expert Ted Kahn realized that it was a completely new species, and the duo published a paper describing the amphibian a few years later. In 2014, a wildlife photographer named Scott Trageser uploaded a photo of a snail that he had taken in Vietnam. Twenty months later, mollusc expert Junn Kitt Foon identified the animal as Myxostoma petiverianum—a species that James Cook’s crew had discovered in the 1700s, but that no one had photographed before.

“It’s a rare win-win,” says Loarie. “We’re engaging people but also producing this stream of high-quality data for science. And we’re sitting on the biggest pile of accurately labeled images for living things that’s out there.” But iNaturalist could become a victim of its own exponential success. Around 20,000 new photos are uploaded every day, threatening to overwhelm the community of expert identifiers. Already, it takes an average of 18 days to get an identification.

Loarie and his colleagues realized that the only way of avoiding an inevitable backlog of unidentified critters was to train a computer in the art of taxonomy. They could feed a neural network—a computer system modeled on the brain—with images from the iNaturalist collection, and allow it to learn the distinctive features of each species. “The expectation, even a year ago, was that this stuff was light years away and unrealistic,” says Alex Shepard. But now, this kind of machine learning is increasingly powerful and user-friendly. Computers learned to program prosthetic arms, reverse-engineer smells, identify galaxies, or devise funny new names for colors.

Artificial intelligence is only as intelligent as the data you use to train it. Shepard only used “research-grade” photos that have been vetted by the iNaturalist community, and he only trained his neural network on the 13,730 species that were represented by at least 20 such photos. Using these photos, and after training himself using online tutorials, Shepard built a “training wheels” prototype that was good enough to identify visually distinctive things like monkeyflowers—and to impress his bosses at the California Academy of Sciences.