AI Accurately Predicts Bird Migrations
Cornell’s eBird online project lets bird watchers around the world report data on bird sightings. Researchers now have a massive amount of data on where to find specific birds at specific times of the year, but much less is known about the actual movements in between.
In a recent study, scientists at Umass Amherst and Cornell’s eBird team have developed an AI-based tool called BirdFlow that predicts migration patterns based on eBird data.
The team trained an AI with the eBird data, then used data from another team of scientists to verify that the predictions were accurate. The project used the Bridges-2 supercomputer at the Pittsburg Supercomputing Center to help manage the massive amounts of data.
According to project co-leader Adriaan Dokter, “We’ll be able to unravel the routes that birds take, from their breeding grounds to stopover points to wintering grounds and back without having to capture birds and attach tracking devices. Understanding these connections is critical to learning why some populations are doing poorly and some are doing well.”
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Heisenberg's Uncertainty Principle states that you can know either the velocity or the direction of an electron, but not both at the same time – at least not in a precise way, you can't. The Hess Uncertainty Principle states that you can't know (precisely) both the speed and the direction of computing. I thought of this during my recent trip to Austin, Texas, where I attended Dell World 2014. Dell turned 30 years old this year, and I never would have predicted, with any accuracy, the new direction that Dell has taken. Nor would I have predicted its current velocity. How could I have known both simultaneously? The short answer is that I couldn't. I'm not even sure that Michael Dell could have predicted, as little as five years ago, the speed and the direction of his company today.