This Wired article makes me think of @starstryder and helps me understand in retrospect why she and I have chatted so much about programming and tech over the years despite our very different fields. Given the data intensive nature of modern astronomy, this pressure to bring more capabilities from the realm of software development, from big data to machine learning, is hardly surprising. Kind of makes me want to find some volunteer opportunities to help where I can.
Slashdot links to a Singularity Hub article describing a project that is forehead slappingly obvious in hindsight.
Scientists are teaching an artificial intelligence how to classify galaxies imaged by telescopes like the Hubble. Manda Banerji at the University of Cambridge along with researchers at University College London, Johns Hopkins and elsewhere, has succeeded in getting the program to agree with human analysis at an impressive rate of more than 90%.
The article goes on to explain how the team used data from Galaxy Zoo to train the AI. Galaxy Zoo is a crowd sourced effort to aggregate small bits of highly distributed human effort to classify galaxies in astronomical imagery. It has produced some startlingly good results due to efforts at cross verification. It makes perfect sense as a training set for a directed learning program.
The AI will be used to alleviate the more trivial tasks involved in many coming astronomical projects so that human input can be applied for best effect, on the harder problems inherent in sifting through the reams of data.