In just one few years, the number of works of art produced by self-written artificial intelligence artists has increased dramatically. Some of these works have been sold by large auction houses at staggering prices and has found its way in prestigious curated collections. Originally led by a couple of technologically savvy artists who adopted computer programming as part of their creative process, AI art has recently been embraced by the masses, as image generation technology has become both more efficient and easier to use without coding skills.
The AI art movement rides on the technical development in computer vision, a field of research dedicated to designing algorithms that can process meaningful visual information. A subclass of computer vision algorithms, called generative models, occupies the center of this story. Generative models are artificial neural networks that can be “trained” on large datasets containing millions of images and learn to code their statistically prominent features. After training, they can produce brand new images that are not included in the original data set, often controlled by text prompts that explicitly describe the desired results. Until recently, images produced through this approach lacked anything in context or detail, although they possessed an undeniable surreal charm that caught the attention of many serious artists. However, earlier this year, technology company Open AI unveiled a new model – nicknamed DALL · E 2– which can generate remarkably consistent and relevant images from virtually any text prompt. DALL · E 2 can even produce images in certain styles and mimic famous artists quite convincingly, as long as the desired effect is sufficiently specified in the prompt. A similar tool has been released for free to the public under the name Craiyon (formerly “DALL · E mini”).
The growing age of AI art raises a number of interesting questions, some of which – such as whether AI art is real artand, if so, to what extent it really is made by AI– are not very original. These questions repeat similar concerns that were once raised by the invention of photography. By simply pressing a button on a camera, a person without painting skills could suddenly capture a realistic depiction of a scene. Today, a person can press a virtual button to run a generative model and produce images of virtually any scene in any style. But cameras and algorithms do not make art. People do. AI art is art, made by human artists who use algorithms as yet another tool in their creative arsenal. Although both technologies have lowered the barrier of entry for artistic creation – which requires celebration rather than concern – one should not underestimate the amount of skill, talent and intentionality involved in making interesting works of art.
Like any new tool, generative models introduce significant changes in the art-making process. AI art in particular expands the multifaceted concept of curation and continues to blur the line between curation and creation.
There are at least three ways in which making art with AI can involve curatorial actions. The first, and least original, has to do with the curation of output. Any generative algorithm can produce an indefinite number of images, but not all of these will typically be assigned artistic status. The process of curing output is very familiar to photographers, some of whom routinely capture hundreds or thousands of images, from which a few, if any, can be carefully selected for viewing. Unlike painters and sculptors, photographers and artificial intelligence artists have to deal with an abundance of (digital) objects whose curation is part of the artistic process. In AI research in general, the act of “picking” particularly good results is seen as poor scientific practice, a way of misleadingly inflating a model’s perceived performance. However, when it comes to AI art, cherry-picking may be the name of the game. The artist’s intentions and artistic sensibilities can be expressed in the very act of promoting specific outputs to the status of works of art.
Second, curing can also take place before any images are generated. In fact, while “curation” applied to art generally refers to the process of selecting existing works for display, curation in AI research in everyday speech refers to the work that goes into making a dataset that one can train an artificial neural network on. This work is crucial because if a dataset is poorly designed, the network will often fail to learn how to represent desired features and perform adequately. Furthermore, if a data set is skewed, the network will tend to reproduce or even amplify such skews – including, for example, harmful stereotypes. As the saying goes, “trash in, trash out.” The proverb also applies to artificial intelligence, except that “garbage” takes on an aesthetic (and subjective) dimension.