A critique of contemporary AI art.

Not only we need cultural artifacts as a critique of industrialized use of Artificial Intelligence, but also a strict criteria in order to review contemporary art made with Machine Learning techniques…

Those that argue against the potential for Computational Creativity believe that “simulating artistic techniques means also simulating human thinking and reasoning” (…) and  conclude that “this is impossible to do using algorithms or information processing systems”. Regardless of this viewpoint, art made by Artificial Intelligence expands the exploration started by Dadaism and Pop-art in territories like authorship, ownership of the work, and whether process is more important than the finished product.

For example Sol Lewitt was experimenting with instruction based artworks in his artwork series called “Wall Drawings” started in 1968, which eventually and until his death, would usealgorithms for the design of such creations and teams of assistants for the execution of these pieces. LeWitt neutralised the materiality and took the further step of virtually denying an independent object existence to his art. And it’s not coincidence that AI artist Robbie Barrat -who uses Deep Learning and Generative Adversarial Networks in his works- states that his role as a creator is like that of artist Sol Lewitt, best known for writing out instructions or rule sets for creating drawings and then having others execute the rules to create his artwork. Unlike with traditional generative art where the artist establishes the code and the computer has no room for interpretation, with this kind of AI generated artworks the artist is like giving imagination to the machines.

A century ago Marcel Duchamp, pioneer of conceptual art, stated that the artist’s idea was the key element in the artistic creation in clear opposition to the classical view that emphasizes a perspective centered around aesthetics. But this new art created by Generative Adversarial Networks radically flips this idea, not just creating a new thought for an object, but apparently creating an artifact capable of doing the “thinking” and “painting” for us. And the same way as Robbie Barrat was upset that Obvious used his open-source code to generate the much hyped “Edmond the Bellamy” painting, it’s important to remember that the mentioned code which actually trains and generates the images is from Machine Learning developer Soumith Chintala, and the data to train the agent is accesible online.

“The human is indissolubly linked with imitation: a human being only becomes human at all by imitating other human beings”, Theodor Adorno.

Ramón López de Mántaras, director of the spanish Artificial Intelligence Research Institute, writes that the fact that we are not conscious of how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. Therefore creativity is not some mystical gift that is beyond scientific study but rather something that can be investigated, simulated, and harnessed for the good of society. Existing computer programs lack too many relevant causal connections to exhibit intentionality while manipulating symbols and learning from that but that doesn’t mean that perhaps in a near future a new kind of “embodied” Artificial Intelligences while interacting with their environment may be able to give meaning to their work.

We humans are reluctant to accept that non-anthropomorphic and nonbiological agents can be creative because accepting it would have important moral, ethical and social implications. As science journalist Andrea Morris writes,  we have no criteria for recognizing sentience in beings without biological brains and nervous systems. So maybe, Artificial Intelligence -like the Animal Liberation movement decades ago- will question assumptions about the legal and moral state of these intelligent machines, and evidently this new kind of society will therefore have to work on granting rights and acquiring responsibilities towards their respective human, post-human and ecological communities.

As PhD candidates Fabian Offert and Andrey Kurenkov remind us in their fantastic article “The Past, Present, and Future of AI Art” in Alan Turing’s view, and paraphrasing Adorno, we could say that artists follow an intuitive logic, a process like any other rule-bound activity. As pioneer AI artist Harnold Cohen thought when designing his expert system AARON 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 like the classic abstractions of Piet Mondrian from the 1920s and 1930s which 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).

Therefore the question if can machines be creative arises from a distorted and romantic notion of creativity, because let’s not forget that only during the Renaissance the concepts of freedom and creativity gained momentum until the 19th century when art took its revenge because it started to be considered an act of creativity by the mere fact of being. Later on -at the turn of the 20th century- a transference of the qualities of creativity started towards to the sciences and to nature, concepts which previously were only related to art. Offert and Kurenkow also remind us that contemporary art has not been about image making for a long time, and while most people admire the accomplished craftsmanship of representational artists, works made with Generative Adversarial Networks have the problem that they are always essentially mimetic. While this artifacts will certainly produce interesting variations, a neural network can never distance itself from the world of data it operates on, so they might never produce an image that reflects on the art historical context and shakes our notion of aesthetics.

Already in 1843 Ada Lovelace, now widely considered to be the first programmer, wrote that computers can never be as intelligent as humans because they simply can only do what we program them to do, “only when computers originate things should they be believed to have minds” wrote the british mathematician. The Lovelace Test, designed in the early 2000s by a team of computer scientists that developed Watson computer for IBM, looks for genuine autonomous creativity instead of simply manipulating syntax. In Mark Riedl, from Georgia Institute of Technology designed the Lovelace Test 2.0, which is an upgrade from the original version and demands that the AI create art. So we might argue that Deep Learning, as a class of Artificial Intelligence architecture that makes computers learn from experience, might be a candidate to pass the above test because the machine gathers knowledge from the experience of processing different data-sets without the need for a human operator to formally specify all the information. This is a major breakthrough since most of the business and scientific systems developed for the first 50 years of Artificial Intelligence research were all based on rule-based systems inspired in our deductive reasoning, while we as humans usually learn by inductive reasoning which makes broad generalizations from specific observations.

According to Blaise Agüera y Arcas -founder of the Artists and Machine Intelligence program at Google– the transformation of artistic practice and theory that attended the 19th century photographic revolution is equal to the current revolution in machine intelligence, which promises to democratize the means of reproduction and production. While many artists seem to laugh at the idea that an AI can be creative, they certainly create things but with no intent and with no sense of what’s relevant. According to Mario Klingemann we the humans are who interpret and examine what is most important form their output. For now, art made with Artificial Intelligence will be a driver of innovation but, exactly as proposed by pioneer computer artist Frieder Nake fifty years ago, not so much a driver of aesthetic change but instead of a critical revolution.

What we can for sure learn while reverse engineering our brain, creating this Artificial Neural Networks and programming them to become image making artists is the great sophistication of our creativity which seems to be a glorious accidental product of natural selection… And maybe this way we will someday be in charge of our own evolution!

*(Picture: painting by AARON, forbes.com & nationalgeographic.org).

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.