Analysis

The above images are actually the result of a crude form of manual grid search. That is, I tried a bunch of stuff and picked what I liked. Tuning the hyperparameters of an algorithm of artistic style is an intrinsically challenging problem. There is no objective function that can be applied to any of these output images that will assess its similarity to my largely qualitative assessment of artistic style.

One could conceive of an approach that involved a bunch of human labelling of these images, but for a concept as complex as artistic style, I shudder to think of the average discordance in concept label values amongst human labellers.

That said, this technique is interesting. It does not often produce the output that I was expecting. But given a not-too-bland source image and some great art, it can consistently produce images that are less boring than their source photographs.

Turing Testing Art

The highest measure of any AI-related technique is generally considered to be human-equivalent capabilities. In the Turing test, the interactions of the AI software must be indistinguishable from interactions with a human. My old boss, Ben Goertzel, founded a branch of AI known as artificial general intelligence which aims to build systems that can achieve direct human equivalence in rich, complicated contexts such as obtaining higher education in the same way that a human might.

This leads me to the larger question that I have about algorithms for artistic style:

What is the Turing test for an AI forger?

Copying paintings is not the highest and best use of human intelligence. The history of imitating famous art makes for engaging reading but is not a record of mankind at its noblest. However, it is an instructive example of what natural general intelligence can achieve. A skilled forger can produce a “new” Kandinsky that experts on Kandinsky will attest was produced by the master himself.

So, a truly human equivalent AI forger should be able to produce a “new” Kandinsky that I (and people far more knowledgeable than me) believe is a new Kandinsky.

I suspect that the initial implementations of the style net algorithm are nowhere close to this capability. When I look at most of my results, I find them to be amusing photo filters, not novel compositions. “Instagram on Steroids” is not quite a new Composition VII.

People seem to be particularly enamored with using Van Gogh for their experimentation with neural art. I think this is because most of what impacts us when we see a Van Gogh is his radical approach to the texture of paintings. The style net technique seems to be at its best when its working with textures, rather than full compositions.

In the interest of giving the algorithm a sporting chance, I decide to throw it a softball. This is my favorite picture of my dog, one that I’ve used in several contexts before.

Couture dress (Kansas City)

I used Van Gogh’s Starry Night as a source image (like everyone else seems to do).

Starry Night, Van Gogh

In the Turing test formulation of this exercise, the algorithm should be able to produce a new image which I would believe is a “new” Van Gogh.

Here’s what I was able to produce.

Starry nom

This is a pleasing result. I like this picture. But, of course, I like the source picture. The technique merely added the texture of Van Gogh’s coarse brush strokes to accentuate my dog’s comparatively fine fur. This isn’t a bad image, but it is in no sense novel art. No one would mistake this for a “new” Van Gogh.

Contrast that image with this truly original composition by Dawn Verbrigghe, based on the same photograph.

Sunday Morning in Meatpacking, Verbrigghe

Though my photograph of the painting doesn’t do it justice, you can still see far more evidence of general intelligence applied in the creation of this image. Like the neural Van Gogh, the artist has chosen to use a coarser, more expressive texture than the purely representative source photo.

But Verbrigghe has gone far beyond merely applying a texture to an image. Subtle changes have been made to simplify the background and focus the viewer’s eye on the subject. Color has been used in ways that don’t so much originate from the image as they do in the artist’s feelings about the image. All of the edges are loosened up so that they are mere indications of rough furry and frilly edges that make sense in a way that is fundamentally absent from the source image.

The Future of AI Art

Let’s be clear: I’m not an AI pessimist. I work on an artificial intelligence that runs the gauntlet of the Turing test all day, every day. Artificial intelligence is here, and it’s starting to take over some of the truly crappy jobs that humans used to have to do.

But I think that, as a proponent of investment in AI, we as a community do not overstate our progress or our near-term expectations. That’s how AI winters come to pass. I’ve not seen or been able to produce neural art that passes my personal Turing test. But when I walk down the right street in Manhattan, I can find plenty of examples natural general intelligence that is more than capable of whipping me up a new Van Gogh.

The Deep Forger is no Wolfgang Beltracchi. Art forgery, like chess in the early 90s, remains a game that humans can still best machines at.

AI might get there someday. Perhaps making novel art is just a bit easier than the substantial challenge of doing laundry. A priori, it’s hard to say. Such is the path of progress in AI.

However, I think that the style net technique is already a powerful new tool for artistic intelligence augmentation. Kyle McDonald’s studies using this technique are fascinating, even if they show a fair number of algorithmic wrong turns. The process of grid search that I executed could be used to far greater creative effect than I’ve achieved, in the hands of a competent artist.

I can’t say that I’m disappointed. It’s great news when AI takes over some tiresome chore. But the creation of art is anything but a chore. Art is territory that I’d prefer not to cede to the machines just yet. Capable artists exhibit far more general artistic intelligence in the composition of novel pieces of art than I’ve seen algorithms achieve thus far.