Software for wildlife research is often developed in isolation and rarely evolves beyond a single project. Compounding this problem is a lack of software development skills in the wildlife research community. Wild Incubator is an on-site, staffed software incubator in Portland, Oregon. The incubator allows code school graduates and undergraduate computer science students to hone their skills on directed, open source wildlife software problems (e.g., develop a new feature for Wildbook to help whale shark researchers). Students work in an environment where professional engineers can guide their experience and focus their efforts on real world problems.

Machine learning will not only usher in the automation of monitoring endangered species from camera trap data but also the automation of spotting poachers, something Morris describes as being right on the cusp of practicality: "You can't put people everywhere but you can put cameras in a lot of places." The potential for computer vision to help the planet encompasses everything from analyzing aerial imagery in arctic and savannah landscapes to monitor large animals to tracking forest recovery and loss from satellite imagery, to even monitoring plastic pollution using AI drones. "Computer vision can offer a lot not just for wildlife conservation applications but also sustainability more broadly," says Morris. "Fundamentally it's really about leveraging AI to save the planet."

The ability of computers to automatically identify individual giraffes from their distinct coat patterns provides scientists with an affordable and efficient way to track population numbers. A software program developed by the conservation technology nonprofit Wild ME automatically identifies individual animals by their unique coat patterns or other distinguishing features. The nonprofit Giraffe Conservation Foundation and San Diego Zoo researcher Jenna Stacy-Dawes used the Wildbook software to take dozens of photos of a giraffe population over two days, uploaded the images and location data to the GiraffeSpotter database, and assessed giraffe numbers across three wildlife conservancies in Northern Kenya. GiraffeSpotter will be publicly accessible by the end of the year, allowing all interested parties to upload their giraffe photos and location data to the online database. GiraffeSpotter is the latest example of how artificial intelligence is being used in service of conservation.