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In 2007, a team of astrophysicists had a (good) problem on their hands — they had more data than they knew what to do with. The groundbreaking Sloan Digital Sky Survey had captured roughly a million images of galaxies from an observatory in New Mexico. It would have taken the scientists up to five years to catalog the images had they gone with a more traditional approach to data cataloging. But a traditional method wasn’t the only option anymore, and the Zooniverse began with a bang.
With a cadre of citizen volunteers powering the project, it only took nine months to catalog images from the New Mexico observatory. This first project was called Galaxy Zoo; volunteers could sign up and evaluate the images, classifying the galaxies by shape (a basic task that is actually key to understanding how galaxies form). The collective of volunteers even made a major astronomical discovery in the process when they flagged what looked like green peas in the photos. Those “green peas” turned out to be a new type of galaxy — and even in the 21st century, it’s something a machine alone would likely have missed.
This is what makes the Zooniverse so invaluable to scientific communities that rely on this type of photo catalog research. While artificial intelligence excels at many repetitive tasks, identifying patterns is not one it’s well-equipped for (yet). But the human eye, having evolved for millennia, is more than capable. For example, a current Zooniverse project, Penguin Watch, involves counting the number of penguins in photographs from studies of penguin populations in Antarctica. To machine learning, a rock shaped like a penguin can be indistinguishable from an actual penguin, or even the shadow of a penguin.
By optimizing the collaboration between technology and human volunteers, the Zooniverse is revolutionizing the way that scientific research can be conducted. Man versus robot? More like man in partnership with robot for better intelligence.