In just a In the past few years, the number of artworks produced by self-described AI artists has increased dramatically. Some of these works were sold by major auction houses at sky-high prices and found their way into prestigious curated collections. Initially spearheaded by a few tech-savvy artists who adopted computer programming as part of their creative process, AI art has recently been embraced by the masses as image-generating technology has become more efficient and easier to use without coding skills.
The AI art movement is on the trail of technical advances in computer vision, a research field dedicated to designing algorithms that can process meaningful visual information. A subclass of computer vision algorithms, called generative models, takes center stage in this story. Generative models are artificial neural networks that can be “trained” on large data sets containing millions of images and learn to encode their statistically salient features. After training, they can produce entirely new images not contained in the original data set, often guided by textual queries that explicitly describe the desired results. Until recently, the images produced by this approach lacked coherence or detail, although they possessed an undeniable surrealist charm that captured the attention of many serious artists. However, earlier this year the technology company Open AI introduced a new model – dubbed DALL·E 2 – that can generate remarkably consistent and relevant images from almost any text query. DALL·E 2 can even produce images in certain styles and mimic famous artists quite convincingly, as long as the desired effect is adequately specified in the query. A similar tool has been released to the public for free under the name Craiyon (formerly “DALL·E mini”).
The coming of age of artificial intelligence art raises a number of interesting questions, some of which – such as whether artificial artificial art is really art, and if so, to what extent it is really created by AI – are not particularly original. These questions reflect similar concerns once raised by the invention of photography. Just by pressing a button on the camera, someone with no painting skills could suddenly capture a realistic rendering of the scene. Today, a person can press a virtual button to launch a generative model and produce images of almost any scene in any style. But cameras and algorithms do not make art. People do that. AI art is art made by artists who use algorithms as another tool in their creative arsenal. While both technologies have lowered the barrier to entry for artistic creation—which calls for celebration, not care—the amount of skill, talent, and intentionality involved in creating interesting works of art should not be underestimated.
Like any new tool, generative models bring significant changes to the art-making process. In particular, AI expands the multifaceted notion of curation and continues to blur the line between curation and creation.
There are at least three ways in which making art with artificial intelligence can involve curatorial actions. The first, and the least original, has to do with the curation of results. Any generative algorithm can produce an unlimited number of images, but not all of them will usually achieve artistic status. The process of curating results is very familiar to photographers, some of whom routinely shoot hundreds or thousands of shots of which few, if any, could be carefully selected for display. Unlike painters and sculptors, photographers and artificial intelligence artists have to deal with an abundance of (digital) objects, the curation of which is an integral part of the artistic process. In AI research in general, the act of “cherry-picking” particularly good results is considered bad scientific practice, a way to falsely increase the perceived performance of a model. However, when it comes to artificial intelligence, cherry picking may be the name of the game. Artistic intentions and artistic sensibilities can be expressed in the very act of promoting specific results to the status of works of art.
Second, curation can also happen before the images are generated. In fact, while “curation” as applied to art generally refers to the process of selecting existing work for display, curating in AI research colloquially refers to the work that goes into creating a dataset on which to train an artificial neural network. This work is crucial because if the dataset is poorly designed, the network will often not learn how to represent the desired features and perform adequately. Furthermore, if a data set is biased, the network will tend to reproduce, or even reinforce, such bias – including, for example, harmful stereotypes. As they say, garbage in, garbage out. The saying also applies to artificial intelligence, except that “trash” takes on an aesthetic (and subjective) dimension.