Galaxy-Sorting Artificial Intelligence

"Computer Program Learns to Sort Galaxies Like a Human"
Space.com (June 1, 2010)

"A computer algorithm modeled after the human brain has learned how to recognize different galaxy types ranging from spiral to elliptical, and can now help flesh-and-blood stargazers with the daunting task of classifying billions of galaxies.

"The machine-learning codes have proven reliable enough to agree with human classifications of galaxies 90 percent of the time, according to scientists at University College London and the University of Cambridge in the UK.

"That should help astronomers keep up with a deluge of galaxy imagery from observational projects such as the Sloan Digital Sky Survey and the Galaxy Zoo. The billions of galaxies in the known universe include a wide range of shapes such as spiral, elliptical, barred and irregular...."

It's a good thing that AI (Artificial Intelligence) is getting up to speed on this task, because there's an appallingly huge amount of data to sort through already - with much more 'in the pipeline.'

The article gives us a very quick look at how this galaxy-sorting AI was developed:

"...More than 250,000 people have helped astronomers classify 60 million galaxies in the online Galaxy Zoo project. Astronomers then used the Galaxy Zoo classifications to train their computer algorithm, known as an artificial neural network, as part of the process for learning to recognize galaxy types.

"The artificial neural network can analyze the complex relationships between different variables such as shape, size and color of astrophysical objects, and then come up with the appropriate galaxy type. That process mimics the biological neural network found in living creatures.

"Astronomers first trained the computer algorithm on 75,000 astrophysical objects from the Sloan Digital Sky Survey included in the Galaxy Zoo project, before testing its abilities in classifying 1 million objects. They also fiddled with the weighted parameters that the algorithm used so that they could finally achieve the 90 percent success rate...."

It's still no HAL 9000, or C3PO - but that's still impressive performance.

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