A public domain photo refactored as a piece of digital art by a deep learning network, on my laptop
Painting is not my favorite art. Nevertheless, I do enjoy it more than most people (apart from people who actually practice the art perhaps), since it’s easy on the eyes and the meaning it tries to convey is far easier to grasp than any other art. Creating something in this art form is very time-consuming though, which is why I admire those who have the patience to make something beautiful out of their canvases and their paints. Also, it takes a special kind of intelligence to be able to create in this domain. Could it be that artificial intelligence can emulate that? The answer is yes!
Over the years, machines have been used in a variety of creative tasks, particularly music. This is obvious for those who have delved into this art but I don’t want to get into a tangent here. Doing something creative with A.I. in the painting domain is whole different kind of challenge though, especially if you don’t know much about the art, like most A.I. people. Of course everyone can do some rudimentary kind of drawing but does that qualify as art? I doubt it and I’m sure anyone who has indulged into the fascinating history of art would agree. There is something else when it comes to making a painting, something that has eluded A.I. algorithms… up until now.
So, what is A.I.-based painting? Well, it is digital for starters. It’s not like the A.I. picks up a palette and a brush and starts coloring a canvas (although I wouldn’t be surprised if there were robots out there equipped with such an A.I. doing just that). Most A.I. systems that can paint do so with a deep learning system that has been trained in a particular style of painting. As an input, such an A.I. system usually takes a digital image, which is the equivalent of the idea or subject that usually fuels such creative endeavors in human beings. What the A.I. does after that is create a new image that makes use of the primary features of the original image (the quintessence of the subject, if you will). These features, which correspond to a particular color palette, shapes, locations of these shapes, and other relevant information, are then processed by the deep learning network they employ. The output of that network is then mapped into a form akin to the original image or corresponding to a set of specifications regarding the resolution of the art piece. The output, naturally, is an image of that resolution. Of course, the A.I. doesn’t have a clue of what it is doing, but given enough training data in its deep learning network, it can perform the task quite creatively.
Although most such synthetic artistic products are very interesting, not all of them are particularly pleasing or even worth the wait of the whole creation process (which is non-trivial when undertaken by a single computer, even if the deep learning networks are already trained beforehand). So, if you are an artist committed to this particular art form, you shouldn’t worry about your work becoming outsourced to machines any time soon! Whatever the case, applications like this are by far more meaningful than other, less thoughtful uses of computational resources for A.I. purposes. This, however, is probably the topic of a future post on the subject…
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.