Apprentice Work

In July 2013, an up-and-coming artist had an exhibition at the Galerie Oberkampf in Paris. It lasted for a week, was attended by the public, received press coverage, and featured works produced over a number of years, including some created on the spot in the gallery. Altogether, it was a fairly typical art-world event. The only unusual feature was that the artist in question was a computer program known as “The Painting Fool.”

Even that was not such a novelty. Art made with the aid of artificial intelligence has been with us for a surprisingly long time. Since 1973, Harold Cohen—a painter, a professor at the University of California, San Diego, and a onetime representative of Britain at the Venice Biennale—has been collaborating with a program called AARON. AARON has been able to make pictures autonomously for decades; even in the late 1980s Cohen was able to joke that he was the only artist who would ever be able to have a posthumous exhibition of new works created entirely after his own death.

The unresolved questions about machine art are, first, what its potential is and, second, whether—irrespective of the quality of the work produced—it can truly be described as “creative” or “imaginative.” These are problems, profound and fascinating, that take us deep into the mysteries of human art-making.

The Painting Fool is the brainchild of Simon Colton, a professor of computational creativity at Goldsmiths College, London, who has suggested that if programs are to count as creative, they’ll have to pass something different from the Turing test. He suggests that rather than simply being able to converse in a convincingly human manner, as ­Turing proposed, an artificially intelligent artist would have to behave in ways that were “skillful,” “appreciative,” and ­“imaginative.”

Collage by the Painting Fool, inspired by news from Afghanistan.

In the opening image, one of AARON’s compositions.

Thus far, the Painting Fool—described as “an aspiring painter” on its website—has made progress on all three fronts. By “appreciative,” Colton means responsive to emotions. An early work consisted of a mosaic of images in a medium resembling watercolor. The program scanned an article in the Guardian on the war in Afghanistan, extracted keywords such as “NATO,” “troops,” and “British,” and found images connected with them. Then it put these together to make a composite image reflecting the “content and mood” of the newspaper article.

The software had been designed to duplicate various painting and drawing media, to select the appropriate one, and also to evaluate the results. “This is a miserable failure,” it commented about one effort. A skeptic might doubt whether this and other statements are anything more than skillful digital ventriloquism. But the writing of poetry is mentioned on the website as a current project—so the Painting Fool apparently aspires to be an author as well as a painter.

In the Paris exhibition, the sitters for portraits faced not a human artist but a laptop, on whose screen the “painting” took place. The Painting Fool executed pictures of visitors in different moods, responding to emotional keywords derived from 10 articles read—once again—in the Guardian. If the tally of negativity was too great (always a danger with news coverage), Colton programmed the software to enter a state of despondency in which it refused to paint at all, a virtual equivalent of the artistic temperament.

Arguably, the images unveiled in June 2015 by Google’s Brain AI research team also display at least one aspect of human imagination: the ability to see one thing as something else. After some training in identifying objects from visual clues, and being fed photographs of skies and random-shaped stuff, the program began generating digital images suggesting the combined imaginations of Walt Disney and Pieter Bruegel the Elder, including a hybrid “Pig-Snail,” “Camel-Bird” and “Dog-Fish.”

Here is a digital equivalent of the mental phenomenon to which Mark Antony referred in Shakespeare’s Antony and Cleopatra: “Sometime we see a cloud that’s dragonish/A vapour sometime like a bear or lion.”

Leonardo da Vinci recommended gazing at stains on a wall or similar random marks as a stimulus to creative fantasy. There, an artist trying to “invent some scene” would find the swirling warriors of a battle or a landscape with “mountains, rivers, rocks, trees, great plains, valleys and hills.” This capacity might have been one of the triggers for prehistoric cave art. Quite often a painting or rock engraving seems to use a natural feature—a pebble in the wall that looks like an eye, for example. Perhaps the Cro-Magnon artist first discerned a lion or a bison in random marks, then made that resemblance clearer with paint or incised line.

Come to that, all representational pictures—not only paintings and drawings but also photographs—depend on a capacity to see one thing, shapes on a flat surface, as something else: something in the three-dimensional world. The artificial-­intelligence systems developed by the Google team are good at that. The images were created using an artificial neural net, software that emulates the way layers of neurons in the brain process information. The software is trained, through analyzing millions of examples, to recognize objects in photos: a dumbbell, a dog, or a dragon.

The Google researchers discovered they could turn such systems into artists by doing something like what Leonardo suggested. The neural net is provided with an image made up of a blizzard of blotches and spots, and is asked to tweak the image to bring out any faint resemblance it detects in the noise to objects that the software has been trained to recognize. A sea of noise can become a tangle of ants or starfish. The technique can also be applied to photos, populating blue skies with ghostly dogs or reworking images in stylized strokes.

Google’s neural networks take unadorned landscape images and “hallucinate” patterns and objects on top of them.

Google’s neural networks take unadorned landscape images and “hallucinate” patterns and objects on top of them.

Google’s neural networks take unadorned landscape images and “hallucinate” patterns and objects on top of them.

Google’s neural networks take unadorned landscape images and “hallucinate” patterns and objects on top of them.

Google’s neural networks take unadorned landscape images and “hallucinate” patterns and objects on top of them.

Google’s neural networks take unadorned landscape images and “hallucinate” patterns and objects on top of them.

The neural networks often gravitate toward animal faces, as seen in this image.

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