It's a Bird

In an exciting demonstration of the power of artificial intelligence (AI) and the diversity of species, a team composed of two programmers and an ornithologist (an expert on birds) created a map of visualized bird sounds.

Coders Manny Tan and Kyle McDonald worked with ornithologist Jessie Barry to create this visually euphonious interactive map of bird sounds. Tan and McDonald used machine learning to organize thousands of bird sounds from a collection by Cornell University. They didn't supply their algorithm with tags or even names of the bird sounds. Instead, they wanted to see how it would learn to organize all the data by listening to the bird sounds.

The results were amazing. Their algorithm was able to group similar sounds together. It generated visualizations of the sound — an image that served as the sound's fingerprint — using a machine learning technique called t-distributed stochastic neighbor embedding (t-SNE), which allowed it to group together sounds with similar fingerprints.

AI Collaborations

What's fascinating about this project is the work that AI can do for various disciplines, apart from just biology and ornithology. Deep learning algorithms are beginning to transform the fields of medicine and medical research, most recently with a retina-scanning program that can help prevent blindness. AI has also ventured into the realm of psychology, being able to identify patients with depression and suicidal tendencies.

While medical research might be an obvious application, AI isn't limited to just this field. AI has also ventured into law and governance, and even to defense and security.

AI isn't just allowing us to understand our world better, it's also changing how we interact with it. The prevalence of automated vehicles or unmanned transportation technology is proof of this, with AI learning to become better car and truck drivers, pilots, and even sailors (in a manner of speaking). AI might even venture into space ahead of us.

Obviously, we're still far from perfecting AI. In as much as deep neural networks are continually learning, we're also in the process of developing better systems.

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