AI Astronomer Aids Effort To Analyze Galaxies

kkleiner writes "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%. Banerji used data from Galaxy Zoo, a massive online project that has used more than 250,000 volunteers to analyze more than 60 million galaxies. The new automated astronomer will help with even larger analytical projects on the horizon, taking care of trivial classifications and leaving the tough cases to humans."

Original paper

[JoshuaZ]

The paper discussing this work is http://arxiv.org/abs/0908.2033 [arxiv.org]. They appear to be using a pretty standard neural network approach (disclaimer: I don't have much background in neural nets at all. I'm just going off of how they were described in the last class I took that discussed them.) This is part of a very general pattern where programs have done a lot of work that we would think could only be done by people. Other examples include the computerized proof of the Robbins conjecturehttp://en.wikipedia.org/wiki/Robbins_conjecture [wikipedia.org]. TFA lists a few examples as well which are in more applied areas.

pros and cons of this approach

[Anonymous Coward]

PRO:

Using neural networks allows for graceful degradation when classifying galaxies by indicating to what degree it believes this galaxy is similar to other galaxies of this type (that it has been trained on). A threshold can be set so that if confidence falls below this threshold, the image is flagged for human intervention.

CONS:

Neural nets are largely black boxes. They use learned statistical relationships to classify images, but they're unable to provide an explanation as to why they made the decision that they did.