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AI is challenging the limits of art curation

एक महिला OBVIOUS नामक फ्रांसीसी सामूहिक द्वारा एल्गोरिथम द्वारा बनाई गई कला के एक काम को देखती है, जो 22 अक्टूबर, 2018 को न्यूयॉर्क के क्रिस्टीज में आर्टिफिशियल इंटेलिजेंस का उपयोग करके कला का निर्माण करती है, जिसका शीर्षक <em>Portrait of Edmund de Bellamy</em> Is.  The piece sold for $432,500.”/><figcaption class=

in great shape , A woman looks at a work of art algorithmically created by a French collective called OBVIOUS, which uses artificial intelligence to create art, titled Portrait of Edmund de Bellamy On October 22, 2018 at Christie’s in New York. This piece sold for $432,500.

In just a few short years, the number of artworks created by self-described AI artists has increased dramatically. Some of these works have been sold by big auction houses for exorbitant prices and have found their way into prestigious curated collections. Initially led by a few technically savvy artists who adopted computer programming as part of their creative process, AI art has only recently been adopted by the public, as image creation techniques are more effective and used without coding skills. I have become easier.

The AI ​​art movement rides on the coattails of technological advances in computer vision, a research area 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 dominant features. After training, they can produce entirely new images that are not included in the original dataset, often guided by text cues that clearly describe the desired results. Until recently, images produced through this approach lacked some degree of coherence or detail, although they had an undeniable Surrealist charm that caught the attention of many serious artists. However, earlier this year the tech company Open AI unveiled a new model – codenamed DALL·E 2 – that can produce remarkably consistent and relevant images from virtually any text prompt. DALL·E 2 can also draw in specific styles and imitate famous artists, as long as the desired effect is adequately specified in the sign. A similar device has been released to the public for free under the name Crayon (formerly “DALL·E Mini”).

The coming age of AI art raises many interesting questions, some of which—such as is AI art really art, and if so, to what extent it is actually created by AI—are not particularly original. These questions echo similar concerns raised by the invention of photography. With just the push of a button on the camera, someone without painting skills can suddenly capture a realistic depiction of a scene. Today, one can press the virtual button to drive a generative model and create images of virtually any scene in any style. But cameras and algorithms don’t make art. people do. AI art is art, created by human artists who use algorithms as another tool in their creative arsenal. While both technologies have lowered the barrier of entry for artistic creation – which calls for celebration rather than concern – the amount of skill, talent and intentionality involved in creating interesting artworks should not be underestimated.

Like any new tool, creative models bring about a significant change in the art-making process. In particular, AI art expands the multidimensional notion 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 acts. The first, and least original, has to do with the curation of the output. Any generative algorithm can produce an indefinite number of images, but not all of them will usually be given artistic status. The process of curating output is all too familiar to photographers, some of whom routinely capture hundreds or thousands of shots, of which few, if any, may be carefully selected for exposure. Unlike painters and sculptors, photographers and AI artists have to deal with an abundance of (digital) objects, whose duration is part and parcel of the artistic process. In large-scale AI research, “cherry-picking” particularly good outputs is seen as bad scientific practice, a way of misleading the perceived performance of a model. When it comes to AI art, cherry-picking can be the name of the game. The artist’s intentions and artistic sensibility can be expressed in the act of promoting outputs specific to the situation in the artwork.

Second, curation can happen before any image is even generated. In fact, while “curation” applied to art generally refers to the process of selecting existing work for display, curation in AI research colloquially refers to the work that goes into producing a dataset. on which an artificial neural network is trained. This task is important, because if a dataset is poorly designed, the network will often fail to learn how to represent the desired features and perform adequately. Furthermore, if a dataset is biased, the network will reproduce, or even amplify, such bias—including, for example, lossy stereotypes. As the saying goes, “garbage in, garbage out.” This adage holds true for AI art as well, except that “garbage” takes on an aesthetic (and subjective) dimension.

For his own work Memories of passers-by (2018), German artist Mario Kinglemann, one of the pioneers of AI art, carefully prepared a dataset of thousands of paintings from the 17th to 19th centuries. They then used this dataset to train a generative algorithm that could produce an infinite stream of novel portraits sharing similar aesthetic characteristics, displayed on two screens in real time (one for female portraits). , for a male portrait). This is an example of AI artwork that does not include output curation. Nevertheless, careful curation of the training data played a fundamental role in its conception. Here, the “bias” is a blessing: the dataset was heavily biased according to the artist’s personal aesthetic preferences and tastes, and this aesthetic bias is reflected in the final artwork, albeit through the distorted lens of a computer-driven generative process.

Another innovation driven by recent advances in generative algorithms is the ability to produce images by describing the desired result in natural language. This is known as “prompting”, or guiding the algorithm with text signals as opposed to sampling random output. Consider the illustration accompanying this article: In the collage inspired DALL·E 2 with the phrases “AI image generation algorithm, conceptual art,” “Collage with images created by a generative AI model, illustration from Wired magazine” There are multiple images generated. and “artist curating artworks created with AI algorithmic, conceptual art.”

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