The rise of digital technology has given an entirely new meaning to what art is, and how it can be experienced. New artwork has emerged designed by Artificial Intelligence using Machine learning. With it the dilemma has been born – is Artificial intelligence-designed artwork real creative art?

Art and AI technology combine forces to create works of art that interact with people.  Principles of design are used in AI art projects that allow the creator and the viewer to explore their creativity through play.

Art always looked for creative innovation

New schools and tendencies came to life over the decades, and so AI comes into play with full rights. Each new approach has the key goal to challenge the way people think about the world, by using art as a medium for change. With AI technology maturing it enters all fields of life at an exponential rate. It will have a significant impact on changing our everyday lives and so the art human beings create. AI questions the future of creativity.

When artificial intelligence creates artworks, can they be considered valuable?

Artificial Intelligence Art can be created autonomously by AI systems and in collaboration between a human and an AI system. The creation process is the most important area where artificial intelligence can make a difference. Using deep machine learning technology, it can create visual stories that have never been seen before. So, no one has not to worry, that someone before already has created something similar.


AI artists often use a Generative Adversarial Network (GAN) to create a new masterpiece. The method was developed by Obvious, a Paris-based collective consisting of Hugo Caselles-Dupré, Pierre Fautrel, and Gauthier Vernier. They are engaged in exploring the interface between art and artificial intelligence, and their method goes by the acronym GAN, which stands for ‘generative adversarial network’. ‘The algorithm is composed of two parts,’ says Caselles-Dupré. ‘On one side is the Generator, on the other the Discriminator. We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th. The Generator makes a new image based on the set, then the Discriminator tries to spot the difference between a human-made image, and one created by the Generator. The aim is to fool the Discriminator into thinking that the new images are real-life portraits. Then we have a result.’

Portrait of Edmond Belamy

This portrait was not the product of a human mind. Created by artificial intelligence, with the help of formula and still, it went under the hammer in the Prints & Multiples sale at Christie’s was sold for  $432,500, 7-10 times higher than estimated, so being the first AI art in the world auction stage. And it is for sure original, but a result of a machine learning process using GAN. The question is, who is the author?

Humans develop AI systems to look at potential innovative art outcomes

Ahmed Elgammal, director of the Art and Artificial Intelligence Lab at Rutgers University in New Jersey, is working with a system that he calls CAN — a ‘creative’ rather than a ‘generative’ network. The basic logic is the same. CAN is programmed though to produce novelty, a different outcome from what it sees in the data set. For example, when paintings from the 14th century were fed into the system, the AI system had a shocking outcome:

A collaboration between two artists – one being human – one a machine

Why does AI produce abstract artwork?

’I think it is because the algorithm has grasped that art progresses in a certain trajectory. If it wants to make something novel, then it cannot go back and produce figurative works as existed before the 20th century. It must move forward. The network has learned that it finds more solutions when it tends toward abstraction: that is where there is the space for novelty.’ says Dr. Elgammal.

AI algorithms do not only generate pictures. They also model the course of art history. It seems that human intelligence moved from figurative to abstract art in its search for novelty on purpose. This was part of a program running in the collective unconscious for half a millennium. Visual development and abstract art were simply an inevitability.

In 75% of the cases, viewers thought the algorithm-generated images had been produced by a human artist.

The difference in responses towards human and machine art was measured. Very little difference in reaction was detected. People cannot decide easily which artwork is human and which is machine-generated. Often they are more inspired by AI-generated art.

And here comes the question of authorship

Depending on how we define art – as an image produced by intelligence with aesthetic intent – or broadly as an attempt to make a statement about the wider world, and to express feelings, then AI art must fall short, compared to human-generated art.

Human designed AI

During the past 50 years, several artists have written computer programs to generate art—called also algorithmic art. The process requires the artist to write detailed code with a desired visual outcome in mind. So, in the end, the Human mind generates the basics for the outcome. The algorithms teach the machine, not to follow a set of rules, but to “learn” a specific aesthetic by analyzing the huge amount of images. The algorithm then tries to generate new images.

The artists are very involved in pre-and post-curation. They might also change the algorithm as needed to generate the desired outputs. Psychologist Daniel E. Berlyne has studied the psychology of aesthetics and found that novelty, surprise, complexity, ambiguity, and eccentricity are the most powerful drivers in successful works of art.

AI can generate outcomes with this pre-requisite in an even more secure way

AI-produced pieces are conceptual art, dating back to the 1960s, in which the idea behind the work and the process are more important than the outcome. It requires an artist and a machine to collaborate to explore new visual forms in innovative ways.


Imagine machines were programmed to create art on their own, with little to no human involvement. Dr. Ahmed Elgammal in his lab created AICAN (artificial intelligence creative adversarial network). This is a program that could be thought of as a nearly autonomous artist. It has learned existing styles and aesthetics and so can generate innovative images of its own.

People tend to like AICAN’s work and can’t distinguish it from that of human artists. Its pieces have been exhibited worldwide, and one even recently sold for $16,000 at an auction.

When programming AICAN, an algorithm called the creative adversarial network, compels AICAN to contend with two opposing forces. It learns the aesthetics of existing works of art at first, then it will be penalized if when the created work is too close to an established style.

Letting AICAN Loose

AICAN is fed with 80,000 images that represent the Western art canon over the previous five centuries. It’s somewhat like an artist taking an art history survey course, with no particular focus on a style or genre.

Then at the click of a button, the machine creates an image that can then be printed.

The works are often surprising in terms of range, colors, and sophistication. Here again, the machine can understand the evolution of art history and because it aims to create something new, AICAN is likely building off more recent trends in art history, such as abstract art.

Missing story and context

Though the algorithm might create uniquely beautiful images, it is still isolated and lacks context or story. Human artists are inspired by people, places, stories, and politics and translate that into an art pieces to make sense of the world.

AI art is a bit like photography in the early days when it wasn’t considered art. Today, photography became an established fine art genre and AI art will arrive there too. 

It is for sure that the market sees a future in it, especially with NFT getting more popular.

AICAN partners with SuperRare to bring Traditional Art and AI to NFT

Besides AICAN, one of the earliest AI artists is Harold Cohen, who wrote the program AARON in 1973 to produce drawings that followed a set of rules he created.

The list of prominent artists using AI in their creative practice grows and here we mention some of the established names



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