With the advancement of technology, machine learning is playing an ever growing role in our lives. From targeted ads on Instagram to algorithmic driven music recommendations, it has certainly established its significance. Ornithologists have also started to make use of ML to further their research on bird migration and conservation of birds. This task has been made easier thanks to the bird banding laboratory that we have been utilizing to track birds’ paths, plus the Next Generation Weather radar which can now detect even the smallest signals from birds.
Thankfully, applied ML algorithms have enabled automation in gathering, interpreting and analysing data on bird migration creating BirdCast. It allows people to appreciate the magnificence of bird migration, moreover, ML forecasts from BirdCast can be useful in conserving migratory birds. This can be further enhanced by integrating an algorithm that can identify the species of birds, much like how Shazam identifies songs.
Moreover, the Merlin app, equipped with ML, helps simplify the process of bird identification to the public, which makes bird-spotting easier and attracts more public awareness towards conservation. In order to make the most out of ML technology in the field of ornithology and further better utilize its applications, having professionals from various fields to collaborate together and share their expertise is key.
The company, Cornell Lab of Ornithology, is an interactive worldwide organization active in research, conservation, education, and citizen science in the field of birds and ornithological studies. It is a unit of Cornell University’s College of Agriculture and Life Sciences and part of the university’s Department of Ecology and Evolutionary Biology. Cornell Lab of Ornithology was founded in 1915 by Professor Arthur Augustus Allen and has since developed into one of the leading research institutes for birds in the world. As an independent nonprofit organization, it is home to over 250 staff members and is located in Ithaca, New York.
The person mentioned in the article is Grant Van Horn, a staff researcher at the Cornell Lab of Ornithology who helped develop the algorithm behind the “Sound ID” feature. He and Ali Khalighifar, a postdoctoral researcher with a strong background in machine learning, have collaborated to enhance the model behind the Birdcast application. Their expertise and complementary set of skills have maximally accentuated the various possibilities that can be realized through the application of machine learning in ornithology.
