Robots venture into deep learning and imagination

The rate of investment flowing into the robotics industry could be considered a boom; by 2019, the robot market is predicted to be worth $135bn. Asia is noted as a key growth area, particularly Japan and China, with China accounting for 35% of all the patents filed. The world of robots is said to be moving from "dumb intelligence" to deep learning, whereby robots improve and add to their own capabilities. MIT's Technology Review also considers imagination or creativity; Google has experimented by building programmes that might replicate human imagination, feeding its software with visual clues to create an 'artificial neural net' emulating the human brain. The painting and photographs this artificial intelligence produced were dubbed "Inceptionism." The images produced vary from disturbing to hallucinogenic.



Another composition by AARON.

One aspect of Cohen’s earlier work was crucial to his taking an interest in artificial intelligence. He felt that “making art didn’t have to require ongoing, minute-by-­minute decision making ... that it should be possible to devise a set of rules and then, almost without thinking, make the painting by following the rule.”

This approach is characteristic of a certain type of artist. The classic abstractions of Piet Mondrian from the 1920s and 1930s are a case in point. These were made according to a set of self-imposed regulations: only straight lines were allowed, which could meet only at right angles and could be depicted only in a palette of red, blue, and yellow (plus black and white).

In a rare example of an art-historical experiment, the late art critic Tom Lubbock attempted to paint some Mondrians himself by following this recipe. He duly produced several abstractions that looked quite like Mondrian’s works, just not so good. The conclusion appeared to be that Mondrian was adding extra qualities—perhaps subtleties of visual balance and weighting of color—that weren’t formulated in the rules.

It is unusual for art critics to try anything as practical as ­Lubbock’s research. But lots of other people do the same kind of thing: they are called forgers, copyists, and pupils. A great deal of art consists, and always has consisted, of imitations of other work: pictures done in the manner of ­Mondrian, Monet, or some other great originator. Art historians spend their lives classifying artists into “circle of Botticelli,” “follower of ­Caravaggio,” etc. Already, it is clear that machines can work on this level: they can produce derivative art (which is all that 99.9 percent of human artists do). But can they do more than that?

Understandably, Cohen has thought a great deal about this. In a lecture from 2010, he posed it the other way around. Wasn’t it obvious that AARON is creative? After all, he went on, “with no further input from me, it can generate unlimited numbers of images, it’s a much better colorist than I ever was myself, and it typically does it all while I’m tucked up in bed.” What, in fact, he asked, was his own contribution? “Well, of course, I wrote the program. It isn’t quite right to say that the program simply follows the rules I gave it. The program is the rules.”

In a way, then, AARON is functioning like a Renaissance or Baroque studio. Under Cohen’s direction, it has developed to the point where it is equivalent to Rubens’s studio in autonomous mode—and perhaps more. In the early years, AARON was confined to drawing outlines; Cohen then selected and sometimes added color by hand. In the ’80s, Cohen began to teach it to work in color. Eventually, he developed a series of rules to enable it to compose coloristic harmonies, but he found this unsatisfactory. His first solution consisted of a long list of instructions based on what a human artist would do in certain situations. But this did not always work, partly because inevitably the list was open-ended.

Harold Cohen and his apprentice AARON making art.

Eventually, he found a way to teach AARON to use colors with a simple algorithm. We have limited ability to imagine differing chromatic arrangements, but our feedback system is terrific. A human artist can look at a picture as it evolves and decide exactly, say, what shade of yellow to add to a picture of sunflowers. AARON does not have a visual system at all, but Cohen devised a formula by which it can balance such factors as hue and saturation in any given image.

Can a machine ever be as creative as a Rembrandt or Picasso? To do that, Cohen argues, a robot would have to develop a sense of self. That may or may not happen, and “if it doesn’t, it means that machines will never be creative in the same sense that humans are creative.” The processes of such an artist involve an interplay between social, emotional, historical, psychological, and physiological factors that are dauntingly difficult to analyze, let alone replicate. This is what can give an image made by such an artist a deep level of meaning to a

human eye.

One day, Cohen suggests, a machine might develop an equivalent sensibility, but even if that never comes to pass, “it doesn’t mean that machines have no part to play with respect to creativity.” As his own experience shows, artificial intelligence offers the artist something beyond an assistant or pupil: a new creative collaborator.

A new, expanded version of A Bigger Message, Martin Gayford’s book of conversations with David Hockney, will be published in May. His last story for MIT Technology Review was “Motion Pictures” (September/October 2015).