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AI Art Is Challenging the Boundaries of Curation


in just one The number of works by self-proclaimed AI artists has increased dramatically over the past few years. Some of these works have been sold at dizzying prices by major auction houses and into prestigious curated collections. Originally spearheaded by a few tech-savvy artists who used computer programming as part of their creative process, AI art has recently gained popularity as image-generating techniques become more effective and easier to use without coding skills .

The artificial intelligence art movement has closely followed the technological advances in computer vision, a field of research devoted 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 datasets containing millions of images and learn to encode their statistically significant features. After training, they can generate completely new images not included in the original dataset, often guided by textual prompts that clearly describe the desired outcome. Until recently, the images produced by this method lacked coherence or detail, despite their undeniable surreal charm that attracted the attention of many serious artists. Earlier this year, however, tech company Open AI unveiled a new model called DALL·E 2 that can generate remarkably consistent and relevant images from almost any text prompt. DALL·E 2 can even generate style-specific images and imitate famous artists fairly convincingly, as long as the desired effect is sufficiently specified in the prompts. A similar tool has been released to the public for free, called Craiyon (previously known as “DALL·E mini”).

The maturation of AI art raises a number of interesting questions, some of which—like whether AI art is really art, and if so, to what extent it is truly created by AI—not particularly original sex. These questions echo similar concerns once raised by the invention of photography. With the press of a button on the camera, someone with no drawing skills can suddenly capture a realistic depiction of a scene. Today, one can press a virtual button to run a generative model and generate images of almost any scene in any style. But cameras and algorithms don’t create art. People will. AI art is art, created by human artists who use algorithms as another tool in their creative arsenal. While both techniques lower the bar for artistic creation—which requires celebration rather than attention—one should not underestimate the skill, talent, and intent involved in making interesting artwork.

As with any new tool, generative models introduce significant changes in the artistic creation process. In particular, AI art expands the multifaceted concept of curation and continues to blur the lines between curation and creation.

There are at least three ways in which art can be made through artificial intelligence involving the act of curation. The first, and least primitive, has to do with the management of output. Any generative algorithm can produce an infinite number of images, but not all of them are usually given artistic status. Photographers are very familiar with the process of collating output, and some of them typically capture hundreds or thousands of photos, some of which (if any) may be handpicked for display. Unlike painters and sculptors, photographers and AI artists have to deal with large numbers of (digital) objects, and their curation is an important part of the artistic process. Throughout AI research, the act of “picking” particularly good outputs is seen as poor scientific practice and a way of misleadingly exaggerating the perceived performance of a model. However, picking a cherry might be the name of the game when it comes to AI art. The artist’s intent and artistic sensibility may manifest in the act of elevating a particular output to the status of a work of art.

Second, curation may also occur before any image generation. In fact, while “curation” as applied to art generally refers to the process of selecting existing works for display, curation in AI research colloquially refers to the work of producing datasets for training artificial neural networks. This work is critical because if the dataset is poorly designed, the network will often fail to learn how to represent the desired features and function adequately. Furthermore, if the dataset is biased, the network will tend to reproduce and even amplify that bias—including harmful stereotypes. As the saying goes, “garbage in, garbage out”. This adage applies to AI art as well, except that “junk” has an aesthetic (and subjective) dimension.

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