For the most part, the first tasks to be outsourced to machines are the most routine, since these jobs are relatively easy to reduce to quantifiable parameters that can be used to train an algorithm. The upshot of this was that artists and other creative types can rest easy since quantifying creativity and aesthetics is much more difficult than, say, quantifying the rules of the road for a self-driving Uber .

Nevertheless, AI researchers are hard at work tackling the problem of artificial creativity, and machines are slowly learning how to write poems, novels and even movie scripts. But according to a new paper by the Google researchers Hui Fang and Meng Zhang, professional photographers might be the next causalities of the creative AI revolution.

Posted to arXiv earlier this month, the paper describes an artificial neural network called Creatism that is a "system for artistic content creation." The idea was to break down photo aesthetics into quantifiable parameters that could be taught to a machine using professional artistic examples. The result, as Fang and Zhang write in their paper, is an algorithm that "mimics the workflow of a landscape photographer, from framing for the best composition to carrying out various post-processing operations."

To make this happen, Fang and Zhang first used formulas to define different aesthetic aspects of photography such as image saturation, composition, and the level of detail based on examples by professional photographers.