Painting might be the last thing you'd expect computers to excel at. It's abstract, expressive, and tied to cultures, psychology, and subjectivity, whereas computers are objective, precise, and governed by the rules of mathematics. Painting, with its emotional reasoning and unclear meanings, appears to be the antithesis of a feeling, logical computer. But they aren't so far apart as they seem. Painting and other forms of visual art owe much to areas of mathematics such as geometry and perspective, and the algorithms that computers adhere to can in fact be made to generate images as varied and subtle as a human painter.

Much like its musical counterpart, algorithmic art dates back to the time before computers were commonplace and in its purest sense requires no artificial intelligence whatsoever. You've probably seen examples of fractal art, which replicates patterns in a recursive, algorithmic way to often-stunning results that vary in appearance from geometric to organic to alien.

Traditionally, algorithmic art involves a human coming up with a concept that an algorithm then generates or visualizes either from scratch or based on existing material. An extreme example of this is Nagoya University researchers Yasuhiro Suzuki and Tomohiro Suzuki's evolutionary painting algorithm, which takes example paintings of a given style and progressively mutates them cutting and splicing and flipping elements, throwing out at each evolution any images that don't match the user's initial stylistic choices. But algorithmic art is more commonly used in the sense of images that are generated by computer code written by people like Dextro, who is one of the leading practitioners of algorithmic/generative art.

As with music, game development, and writing, much of the attention from artists and scientists has been placed upon algorithms and intelligent tools that augment the artist's creativity. The Processing programming language was designed as an electronic sketchbook for artists and designers, while some of the better-known apps for algorithmic artists include Ultra Fractal, Scribble, and Fragmentarium.

There are now over a dozen separate kinds of algorithmically-based art, including fractal art, genetic art, cellular automata, proceduralism, and transhumanist art. And there are multitudes of websites such as The Algorists, Algorithmic Worlds, and The compArt database Digital Art that celebrate the work of artists who use algorithms.

Harold Cohen watches AARON paint in 1995

But there are some who would teach computers to paint like humans, to push them beyond the point of being an extension of the artist and into the territory of artist themselves. The pioneer in this regard is a former artist and University of California San Diego professor called Harold Cohen. He started working on an art-creating program called AARON in 1973, while a visiting scholar at Stanford University's Artificial Intelligence Lab.

AARON's capacity to paint improved year after year as its maker taught it more difficult or complex techniques. It learned to situate objects or people in 3D space in the 1980s, and could paint in color from 1990 onwards. In time its paintings found their way into many of the world's major art museums and onward into the hands of private collectors who paid hundreds or even thousands of dollars for AARON's art.

AARON paints not with pixels, we should note, but with real paint on an actual canvas. Cohen built a painting machine for his painting AI. He taught it to mix paint (fabric dyes, not oil), and even gave it an imagination of sorts. Enough of one, at least, that it can paint still life and portraits of human figures without photos or other human input as reference.

AARON learned to use color in a decorative motif in 1992 (Photo: Becky Cohen)

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Creative AI: The robots that would be painters