Artificial intelligence art

Artists began to create artificial intelligence art in the mid to late 20th century, when the discipline was founded. Throughout its history, artificial intelligence art has raised many philosophical concerns related to the human mind, artificial beings, and what can be considered art in a human–AI collaboration. Since the 20th century, artists have used AI to create art, some of which has been exhibited in museums and won awards.


The increased availability of AI art tools to the general public in the 2020s AI boom provided opportunities for creating AI generated images outside of academia and professional artists. Commentary about AI art in the 2020s has often focused on issues related to copyright, deception, defamation, and its impact on more traditional artists, including technological unemployment.

Analysis of existing art using AI[edit]

In addition to the creation of original art, research methods that use AI have been generated to quantitatively analyze digital art collections. This has been made possible due to the large-scale digitization of artwork in the past few decades. According to CETINIC and SHE (2022), using artificial intelligence to analyse already-existing art collections can provide new perspectives on the development of artistic styles and the identification of artistic influences.^[112][113]


Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art.^[114] Close reading focuses on specific visual aspects of one piece. Some tasks performed by machines in close reading methods include computational artist authentication and analysis of brushstrokes or texture properties. In contrast, through distant viewing methods, the similarity across an entire collection for a specific feature can be statistically visualized. Common tasks relating to this method include automatic classification, object detection, multimodal tasks, knowledge discovery in art history, and computational aesthetics.^[113] Synthetic images can also be used to train AI algorithms for art authentication and to detect forgeries.^[115]


Researchers have also introduced models that predict emotional responses to art such as ArtEmis, a large-scale dataset with machine learning models that contain emotional reactions to visual art as well as predictions of emotion from images or text.^[116]

Other forms of art[edit]

Some prototype cooking robots can dynamically taste.^[117]


There is also AI-assisted writing beyond copy editing^[118] (such as helping with writer's block, inspiration, or rewriting segments).^{[119][120][121][122]} Generative AI has been used in video game production beyond imagery, especially for level design (e.g., for custom maps) and creating new content (e.g., quests or dialogue) or interactive stories in video games.^[123][124] Some AI can also generate videos, either from text, an image, or a video. This is known as a text-to-video model. Examples of this are Runway's Gen-2, OpenAI's Sora, and Google's VideoPoet.