AI Art is challenging the boundaries of care

AI Art is challenging the boundaries of care

In just a within a few years, the number of works of art produced by self-styled artificial intelligence artists has increased dramatically. Some of these works have been sold by large auction houses at dizzying prices and have found their way into prestigious curated collections. Initially led by some technologically savvy artists who have adopted computer programming as part of their creative process, the art of AI has recently been welcomed by the masses, as image generation technology has become both more effective and easier to use. without coding skills.

The art movement of artificial intelligence rides in the wake of technical progress in computer vision, a research area dedicated to the design of algorithms capable of processing 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 completely new images that are not contained in the original dataset, often guided by text messages that explicitly describe the desired results. Until recently, images produced through this approach have remained somewhat lacking in coherence or detail, although they possessed an undeniable surrealist appeal that has captured the attention of many serious artists. However, earlier this year tech firm Open AI unveiled a new model, dubbed the DALL · E 2, capable of generating remarkably consistent and relevant images from virtually any text prompt. DALL · E 2 can also produce images in specific styles and imitate famous artists quite convincingly, as long as the desired effect is properly specified in the prompt. A similar tool was released free to the public under the name Craiyon (formerly “DALL · E mini”).

Coming of age of AI art raises a number of interesting questions, some of which, for example, whether AI art is really art and, if so, to what extent is it actually made by AI, they are not particularly original. These questions echo similar concerns once raised by the invention of photography. By simply pressing a button on a camera, someone with no painting skills could suddenly capture a lifelike representation 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 don’t make art. People do. AI art is art, made by human artists who use algorithms as another tool in their creative arsenal. While both technologies have lowered the barrier to 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 artistic creation process. In particular, AI art expands the multifaceted notion of curating and continues to blur the line between curating and creation.

There are at least three ways in which making art with AI can involve curatorial acts. The first, and less original, has to do with the care of the releases. Any generative algorithm can produce an indefinite number of images, but not all are generally given artistic status. The process of curating the results is very familiar to photographers, some of whom regularly capture hundreds or thousands of shots from which some, if any, could be carefully selected for viewing. Unlike painters and sculptors, AI photographers and artists have to contend 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 “collecting particularly good results” is seen as bad scientific practice, a way of misleading a model’s perceived performance. When it comes to the art of AI, however, cherry picking may be the name of the game. The artist’s intentions and artistic sensibility can be expressed in the very act of promoting specific productions to the status of works of art.

Second, the cure can take place even before images are generated. Indeed, while the “cure” applied to art generally refers to the process of selecting existing work for visualization, the cure in AI research colloquially refers to the work it takes to create a dataset on which to train a artificial neural network. This work is critical, because if a data set is poorly designed, the network often fails to learn to represent the desired characteristics and function properly. Furthermore, if a data set is distorted, the network will tend to reproduce, or even amplify, that bias, including, for example, harmful stereotypes. As they say, “garbage inside, garbage outside”. The adage also applies to the art of AI, except that “junk” takes on an aesthetic (and subjective) dimension.

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