Artificial Intelligence Visual Art

Artificial intelligence visual art means visual artwork generated (or enhanced) through the use of artificial intelligence (AI) programs.
Artists began to create AI art in the mid to late 20th century, when the discipline was founded. Throughout its history, AI has raised many philosophical concerns related to the human mind, artificial beings, and also what can be considered ''art'' in human–AI collaboration. Since the 20th century, people have used AI to create art, some of which has been exhibited in museums and won awards.

During the AI boom of the 2020s, text-to-image models such as Midjourney, DALL-E, Stable Diffusion, and FLUX.1 became widely available to the public, allowing users to quickly generate imagery with little effort. 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.

History

Early history

Automated art dates back at least to the automata of ancient Greek civilization, when inventors such as Daedalus and Hero of Alexandria were described as designing machines capable of writing text, generating sounds, and playing music. Creative automatons have flourished throughout history, such as Maillardet's automaton, created around 1800 and capable of creating multiple drawings and poems.

Also in the 19th century, Ada Lovelace, writes that "computing operations" could be used to generate music and poems, now referred to as "The Lovelace Effect," where a computer's behavior is viewed as creative. Lovelace also discusses a concept known as "The Lovelace Objection," where she argues that a machine has "no pretensions whatever to originate anything."

In 1950, with the publication of Alan Turing's paper "Computing Machinery and Intelligence", there was a shift from defining machine intelligence in abstract terms to evaluating whether a machine can mimic human behavior and responses convincingly. Shortly after, the academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956. Since its founding, researchers in the field have explored philosophical questions about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction, and philosophy since antiquity.

Artistic history

Since the founding of AI in the 1950s, artists have used artificial intelligence to create artistic works. These works were sometimes referred to as algorithmic art, computer art, digital art, or new media art.

One of the first significant AI art systems is AARON, developed by Harold Cohen beginning in the late 1960s at the University of California at San Diego. AARON uses a symbolic rule-based approach to generate technical images in the era of GOFAI programming, and it was developed by Cohen with the goal of being able to code the act of drawing. AARON was exhibited in 1972 at the Los Angeles County Museum of Art. From 1973 to 1975, Cohen refined AARON during a residency at the Artificial Intelligence Laboratory at Stanford University. In 2024, the Whitney Museum of American Art exhibited AI art from throughout Cohen's career, including re-created versions of his early robotic drawing machines.

Karl Sims has exhibited art created with artificial life since the 1980s. He received an M.S. in computer graphics from the MIT Media Lab in 1987 and was artist-in-residence from 1990 to 1996 at the supercomputer manufacturer and artificial intelligence company Thinking Machines. In both 1991 and 1992, Sims won the Golden Nica award at Prix Ars Electronica for his videos using artificial evolution. In 1997, Sims created the interactive artificial evolution installation ''Galápagos'' for the NTT InterCommunication Center in Tokyo. Sims received an Emmy Award in 2019 for outstanding achievement in engineering development.

In 1999, Scott Draves and a team of several engineers created and released ''Electric Sheep'' as a free software screensaver. ''Electric Sheep'' is a volunteer computing project for animating and evolving fractal flames, which are distributed to networked computers which display them as a screensaver. The screensaver used AI to create an infinite animation by learning from its audience. In 2001, Draves won the Fundacion Telefónica Life 4.0 prize for ''Electric Sheep''.

In 2014, Stephanie Dinkins began working on ''Conversations with Bina48''. For the series, Dinkins recorded her conversations with BINA48, a social robot that resembles a middle-aged black woman. In 2019, Dinkins won the Creative Capital award for her creation of an evolving artificial intelligence based on the "interests and culture(s) of people of color."

In 2015, Sougwen Chung began ''Mimicry (Drawing Operations Unit: Generation 1)'', an ongoing collaboration between the artist and a robotic arm. In 2019, Chung won the Lumen Prize for her continued performances with a robotic arm that uses AI to attempt to draw in a manner similar to Chung. In 2018, an auction sale of artificial intelligence art was held at Christie's in New York where the AI artwork ''Edmond de Belamy'' sold for , which was almost 45 times higher than its estimate of –10,000. The artwork was created by Obvious, a Paris-based collective.

In 2024, Japanese film ''generAIdoscope'' was released. The film was co-directed by Hirotaka Adachi, Takeshi Sone, and Hiroki Yamaguchi. All video, audio, and music in the film were created with artificial intelligence.

In 2025, Japanese anime television series '' Twins Hinahima'' was released. The anime was produced and animated with AI assistance during the process of cutting and conversion of photographs into anime illustrations and later retouched by art staff. Most of the remaining parts such as characters and logos were hand-drawn with various software.

Technical history

Deep learning, characterized by its multi-layer structure that attempts to mimic the human brain, first came about in the 2010s and causing a significant shift in the world of AI art. During the deep learning era, there are mainly these types of designs for generative art: autoregressive models, diffusion models, GANs, normalizing flows.

In 2014, Ian Goodfellow and colleagues at Université de Montréal developed the generative adversarial network (GAN), a type of deep neural network capable of learning to mimic the statistical distribution of input data such as images. The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images.

In 2015, a team at Google released DeepDream, a program that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia. The process creates deliberately over-processed images with a dream-like appearance reminiscent of a psychedelic experience. Later, in 2017, a conditional GAN learned to generate 1000 image classes of ImageNet, a large visual database designed for use in visual object recognition software research. By conditioning the GAN on both random noise and a specific class label, this approach enhanced the quality of image synthesis for class-conditional models.

Autoregressive models were used for image generation, such as PixelRNN (2016), which autoregressively generates one pixel after another with a recurrent neural network. Immediately after the Transformer architecture was proposed in ''Attention Is All You Need'' (2018), it was used for autoregressive generation of images, but without text conditioning.

The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN to allow users to generate and modify images such as faces, landscapes, and paintings.

In the 2020s, text-to-image models, which generate images based on prompts, became widely used, marking yet another shift in the creation of AI generated artworks.

In 2021, using the influential large language generative pre-trained transformer models that are used in GPT-2 and GPT-3, OpenAI released a series of images created with the text-to-image AI model DALL-E 1. It was an autoregressive generative model with essentially the same architecture as GPT-3. Along with this, later in 2021, EleutherAI released the open source VQGAN-CLIP based on OpenAI's CLIP model. Diffusion models, generative models used to create synthetic data based on existing data, were first proposed in 2015, but they only became better than GANs in early 2021. Latent diffusion model was published in December 2021 and became the basis for the later Stable Diffusion (August 2022).

In 2022, Midjourney was released, followed by Google Brain's Imagen and Parti, which were announced in May 2022, Microsoft's NUWA-Infinity, and the source-available Stable Diffusion, which was released in August 2022. DALL-E2, a successor to DALL-E, was beta-tested and released (with the further successor DALL-E3 being released in 2023). Stability AI has a Stable Diffusion web interface called DreamStudio, plugins for Krita, Photoshop, Blender, and GIMP, and the Automatic1111 web-based open source user interface. Stable Diffusion's main pre-trained model is shared on the Hugging Face Hub.

Ideogram was released in August 2023, this model is known for its ability to generate legible text.

In 2024, Flux was released. This model can generate realistic images and was integrated into Grok, the chatbot used on X (formerly Twitter), and ''Le Chat'', the chatbot of Mistral AI. Flux was developed by Black Forest Labs, founded by the researchers behind Stable Diffusion. Grok later switched to its own text-to-image model Aurora in December of the same year. Several companies, along with their products, have also developed an AI model integrated with an image editing service. Adobe has released and integrated the AI model Firefly into Premiere Pro, Photoshop, and Illustrator. Microsoft has also publicly announced AI image-generator features for Microsoft Paint. Along with this, some examples of text-to-video models of the mid-2020s are Runway's Gen-2, Google's VideoPoet, and OpenAI's Sora, which was released in December 2024.

In 2025, several models were released. GPT Image 1 from OpenAI, launched in March 2025, introduced new text rendering and multimodal capabilities, enabling image generation from diverse inputs like sketches and text. MidJourney v7 debuted in April 2025, providing improved text prompt processing. In May 2025 Flux.1 Kontext by Black Forest Labs emerged as an efficient model for high-fidelity image generation, while Google’s Imagen 4 was released with improved photorealism.

Tools and processes

Approaches

There are many approaches used by artists to develop AI visual art. When text-to-image is used, AI generates images based on textual descriptions, using models like diffusion or transformer-based architectures. Users input prompts and the AI produces corresponding visuals. When image-to-image is used, AI transforms an input image into a new style or form based on a prompt or style reference, such as turning a sketch into a photorealistic image or applying an artistic style. When image-to-video is used, AI generates short video clips or animations from a single image or a sequence of images, often adding motion or transitions. This can include animating still portraits or creating dynamic scenes. When text-to-video is used, AI creates videos directly from text prompts, producing animations, realistic scenes, or abstract visuals. This is an extension of text-to-image but focuses on temporal sequences.

Imagery

There are many tools available to the artist when working with diffusion models. They can define both positive and negative prompts, but they are also afforded a choice in using (or omitting the use of) VAEs, LoRAs, hypernetworks, IP-adapter, and embedding/textual inversions. Artists can tweak settings like guidance scale (which balances creativity and accuracy), seed (to control randomness), and upscalers (to enhance image resolution), among others. Additional influence can be exerted during pre-inference by means of noise manipulation, while traditional post-processing techniques are frequently used post-inference. People can also train their own models.

In addition, procedural "rule-based" generation of images using mathematical patterns, algorithms that simulate brush strokes and other painted effects, and deep learning algorithms such as generative adversarial networks (GANs) and transformers have been developed. Several companies have released apps and websites that allow one to forego all the options mentioned entirely while solely focusing on the positive prompt. There also exist programs which transform photos into art-like images in the style of well-known sets of paintings.

There are many options, ranging from simple consumer-facing mobile apps to Jupyter notebooks and web UIs that require powerful GPUs to run effectively. Additional functionalities include "textual inversion," which refers to enabling the use of user-provided concepts (like an object or a style) learned from a few images. Novel art can then be generated from the associated word(s) (the text that has been assigned to the learned, often abstract, concept) and model extensions or fine-tuning (such as DreamBooth).

Impact and applications

AI has the potential for a societal transformation, which may include enabling the expansion of noncommercial niche genres (such as cyberpunk derivatives like solarpunk) by amateurs, novel entertainment, fast prototyping, increasing art-making accessibility, and artistic output per effort or expenses or time—e.g., via generating drafts, draft-definitions, and image components (inpainting). Generated images are sometimes used as sketches, low-cost experiments, inspiration, or illustrations of proof-of-concept-stage ideas. Additional functionalities or improvements may also relate to post-generation manual editing (i.e., polishing), such as subsequent tweaking with an image editor.

Prompt engineering and sharing

Prompts for some text-to-image models can also include images and keywords and configurable parameters, such as artistic style, which is often used via keyphrases like "in the style of ame of an artist in the prompt /or selection of a broad aesthetic/art style. There are platforms for sharing, trading, searching, forking/refining, or collaborating on prompts for generating specific imagery from image generators. Prompts are often shared along with images on image-sharing websites such as Reddit and AI art-dedicated websites. A prompt is not the complete input needed for the generation of an image; additional inputs that determine the generated image include the output resolution, random seed, and random sampling parameters.

Related terminology

Synthetic media, which includes AI art, was described in 2022 as a major technology-driven trend that will affect business in the coming years. Harvard Kennedy School researchers voiced concerns about synthetic media serving as a vector for political misinformation soon after studying the proliferation of AI art on the X platform. ''Synthography'' is a proposed term for the practice of generating images that are similar to photographs using AI.

Impact

Bias

A major concern raised about AI-generated images and art is sampling bias within model training data leading towards discriminatory output from AI art models. In 2023, University of Washington researchers found evidence of racial bias within the Stable Diffusion model, with images of a "person" corresponding most frequently with images of males from Europe or North America.

Looking more into the sampling bias found within AI training data, in 2017, researchers at Princeton University used AI software to link over 2 million words, finding that European names were viewed as more "pleasant" than African-Americans names, and that the words "woman" and "girl" were more likely to be associated with the arts instead of science and math, "which were most likely connected to males." Generative AI models typically work based on user-entered word-based prompts, especially in the case of diffusion models, and this word-related bias may lead to biased results.

Along with this, generative AI can perpetuate harmful stereotypes regarding women. For example, Lensa, an AI app that trended on TikTok in 2023, was known to lighten black skin, make users thinner, and generate hypersexualized images of women. Melissa Heikkilä, a senior reporter at ''MIT Technology Review'', shared the findings of an experiment using Lensa, noting that the generated avatars did not resemble her and often depicted her in a hypersexualized manner. Experts suggest that such outcomes can result from biases in the datasets used to train AI models, which can sometimes contain imbalanced representations, including hypersexual or nude imagery.

In 2024, Google's chatbot Gemini's AI image generator was criticized for perceived racial bias, with claims that Gemini deliberately underrepresented white people in its results. Users reported that it generated images of white historical figures like the Founding Fathers, Nazi soldiers, and Vikings as other races, and that it refused to process prompts such as "happy white people" and "ideal nuclear family". Google later apologized for "missing the mark" and took Gemini's image generator offline for updates. This prompted discussions about the ethical implications of representing historical figures through a contemporary lens, leading critics to argue that these outputs could mislead audiences regarding actual historical contexts. In addition to the well-documented representational issues such as racial and gender bias, some scholars have also pointed out deeper conceptual assumptions that shape how we perceive AI-generated art. For instance, framing AI strictly as a passive tool overlooks how cultural and technological factors influence its outputs. Others suggest viewing AI as part of a collaborative creative process, where both human and machine contribute to the artistic result.

Copyright

Legal scholars, artists, and media corporations have considered the legal and ethical implications of artificial intelligence art since the 20th century. Some artists use AI art to critique and explore the ethics of using gathered data to produce new artwork.

In 1985, intellectual property law professor Pamela Samuelson argued that US copyright should allocate algorithmically generated artworks to the user of the computer program. A 2019 ''Florida Law Review'' article presented three perspectives on the issue. In the first, artificial intelligence itself would become the copyright owner; to do this, Section 101 of the US Copyright Act would need to be amended to define "author" as a computer. In the second, following Samuelson's argument, the user, programmer, or artificial intelligence company would be the copyright owner. This would be an expansion of the "work for hire" doctrine, under which ownership of a copyright is transferred to the "employer." In the third situation, copyright assignments would never take place, and such works would be in the public domain, as copyright assignments require an act of authorship.

In 2022, coinciding with the rising availability of consumer-grade AI image generation services, popular discussion renewed over the legality and ethics of AI-generated art. A particular topic is the inclusion of copyrighted artwork and images in AI training datasets, with artists objecting to commercial AI products using their works without consent, credit, or financial compensation. In September 2022, Reema Selhi, of the Design and Artists Copyright Society, stated that "there are no safeguards for artists to be able to identify works in databases that are being used and opt out." Some have claimed that images generated with these models can bear resemblance to extant artwork, sometimes including the remains of the original artist's signature. In December 2022, users of the portfolio platform ArtStation staged an online protest against non-consensual use of their artwork within datasets; this resulted in opt-out services, such as "Have I Been Trained?" increasing in profile, as well as some online art platforms promising to offer their own opt-out options. According to the US Copyright Office, artificial intelligence programs are unable to hold copyright, a decision upheld at the Federal District level as of August 2023 followed the reasoning from the monkey selfie copyright dispute.

OpenAI, the developer of DALL-E, has its own policy on who owns generated art. They assign the right and title of a generated image to the creator, meaning the user who inputted the prompt owns the image generated, along with the right to sell, reprint, and merchandise it.

In January 2023, three artists— Sarah Andersen, Kelly McKernan, and Karla Ortiz—filed a copyright infringement lawsuit against Stability AI, Midjourney, and DeviantArt, claiming that it is legally required to obtain the consent of artists before training neural nets on their work and that these companies infringed on the rights of millions of artists by doing so on five billion images scraped from the web.James Vincent "AI art tools Stable Diffusion and Midjourney targeted with copyright lawsuit" The Verge, 16 January 2023.
/ref> In July 2023, U.S. District Judge William Orrick was inclined to dismiss most of the lawsuits filed by Andersen, McKernan, and Ortiz, but allowed them to file a new complaint. Also in 2023, Stability AI was sued by Getty Images for using its images in the training data. A tool built by Simon Willison allowed people to search 0.5% of the training data for Stable Diffusion V1.1, i.e., 12 million of the 2.3 billion instances from LAION 2B. Artist Karen Hallion discovered that her copyrighted images were used as training data without their consent.

In March 2024, Tennessee enacted the ELVIS Act, which prohibits the use of AI to mimic a musician's voice without permission. A month later in that year, Adam Schiff introduced the Generative AI Copyright Disclosure Act which, if passed, would require that AI companies to submit copyrighted works in their datasets to the Register of Copyrights before releasing new generative AI systems. In November 2024, a group of artists and activists shared early access to OpenAI’s unreleased video generation model, Sora, via Huggingface. The action, accompanied by a statement, criticized the exploitative use of artists’ work by major corporations.'

On June 11, 2025, Universal Pictures (owned by Comcast) and The Walt Disney Company filed a copyright infringement lawsuit against Midjourney. The suit described Midjourney as "a bottomless pit of plagiarism."

Deception

As with other types of photo manipulation since the early 19th century, some people in the early 21st century have been concerned that AI could be used to create content that is misleading and can be made to damage a person's reputation, such as deepfakes. Artist Sarah Andersen, who previously had her art copied and edited to depict Neo-Nazi beliefs, stated that the spread of hate speech online can be worsened by the use of image generators. Some also generate images or videos for the purpose of catfishing.

AI systems have the ability to create deepfake content, which is often viewed as harmful and offensive. The creation of deepfakes poses a risk to individuals who have not consented to it. This mainly refers to deepfake pornography which is used as revenge porn, where sexually explicit material is disseminated to humiliate or harm another person. AI-generated child pornography has been deemed a potential danger to society due to its unlawful nature.
File:EldagsenElectrician.jpg, ''Pseudomnesia: The Electrician'' won one of the categories in the Sony World Photography Awards competition.
File:Pope Francis in puffy winter jacket.jpg, A 2023 AI-generated image of Pope Francis wearing a puffy winter jacket fooled some viewers into believing it was an actual photograph. It went viral on social media platforms.
File:Trump’s arrest (2).jpg, Journalist Eliot Higgins' Midjourney-generated image depicts former President Donald Trump getting arrested. The image was posted on Twitter and went viral.
File:AI generated figure published in a Frontiers journal.png, One of the seven AI-generated images that were used for figures in the now-retracted paper ''Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway''. Figure 1, "Spermatogonial stem cells, isolated, purified and cultured from rat testes".

After winning the 2023 "Creative" "Open competition" Sony World Photography Awards, Boris Eldagsen stated that his entry was actually created with artificial intelligence. Photographer Feroz Khan commented to the BBC that Eldagsen had "clearly shown that even experienced photographers and art experts can be fooled". Smaller contests have been affected as well; in 2023, a contest run by author Mark Lawrence as Self-Published Fantasy Blog-Off was cancelled after the winning entry was allegedly exposed to be a collage of images generated with Midjourney.

In May 2023, on social media sites such as Reddit and Twitter, attention was given to a Midjourney-generated image of Pope Francis wearing a white puffer coat. Additionally, an AI-generated image of an attack on the Pentagon went viral as part of a hoax news story on Twitter.

In the days before March 2023 indictment of Donald Trump as part of the Stormy Daniels–Donald Trump scandal, several AI-generated images allegedly depicting Trump's arrest went viral online. On March 20, British journalist Eliot Higgins generated various images of Donald Trump being arrested or imprisoned using Midjourney v5 and posted them on Twitter; two images of Trump struggling against arresting officers went viral under the mistaken impression that they were genuine, accruing more than 5 million views in three days. According to Higgins, the images were not meant to mislead, but he was banned from using Midjourney services as a result. As of April 2024, the tweet had garnered more than 6.8 million views.

In February 2024, the paper ''Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway'' was published using AI-generated images. It was later retracted from '' Frontiers in Cell and Developmental Biology'' because the paper "does not meet the standards".

To mitigate some deceptions, OpenAI developed a tool in 2024 to detect images that were generated by DALL-E 3. In testing, this tool accurately identified DALL-E 3-generated images approximately 98% of the time. The tool is also fairly capable of recognizing images that have been visually modified by users post-generation.

Income and employment stability

As generative AI image software such as Stable Diffusion and DALL-E continue to advance, the potential problems and concerns that these systems pose for creativity and artistry have risen. In 2022, artists working in various media raised concerns about the impact that generative artificial intelligence could have on their ability to earn money, particularly if AI-based images started replacing artists working in the illustration and design industries. In August 2022, digital artist R. J. Palmer stated that "I could easily envision a scenario where using AI, a single artist or art director could take the place of 5–10 entry level artists... I have seen a lot of self-published authors and such say how great it will be that they don’t have to hire an artist." Scholars Jiang et al. state that "Leaders of companies like Open AI and Stability AI have openly stated that they expect generative AI systems to replace creatives imminently." A 2022 case study found that AI-produced images created by technology like DALL-E caused some traditional artists to be concerned about losing work, while others use it to their advantage and view it as a tool.

AI-based images have become more commonplace in art markets and search engines because AI-based text-to-image systems are trained from pre-existing artistic images, sometimes without the original artist's consent, allowing the software to mimic specific artists' styles. For example, Polish digital artist Greg Rutkowski has stated that it is more difficult to search for his work online because many of the images in the results are AI-generated specifically to mimic his style. Furthermore, some training databases on which AI systems are based are not accessible to the public.

The ability of AI-based art software to mimic or forge artistic style also raises concerns of malice or greed. Works of AI-generated art, such as '' Théâtre D'opéra Spatial'', a text-to-image AI illustration that won the grand prize in the August 2022 digital art competition at the Colorado State Fair, have begun to overwhelm art contests and other submission forums meant for small artists. The Netflix short film '' The Dog & the Boy'', released in January 2023, received backlash online for its use of artificial intelligence art to create the film's background artwork. Within the same vein, Disney released ''Secret Invasion'', a Marvel TV show with an AI-generated intro, on Disney+ in 2023, causing concern and backlash regarding the idea that artists could be made obsolete by machine-learning tools.

AI art has sometimes been deemed to be able to replace traditional stock images. In 2023, Shutterstock announced a beta test of an AI tool that can regenerate partial content of other Shutterstock's images. Getty Images and Nvidia have partnered with the launch of Generative AI by iStock, a model trained on Getty's library and iStock's photo library using Nvidia's Picasso model.

Power usage

Researchers from Hugging Face and Carnegie Mellon University reported in a 2023 paper that generating one thousand 1024×1024 images using Stable Diffusion's XL 1.0 base model requires 11.49 kWh of energy and generates of carbon dioxide, which is roughly equivalent to driving an average gas-powered car a distance of . Comparing 88 different models, the paper concluded that image-generation models used on average around 2.9kWh of energy per 1,000 inferences.

Analysis of existing art using AI

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 analyze already-existing art collections can provide new perspectives on the development of artistic styles and the identification of artistic influences.

Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art. 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. Synthetic images can also be used to train AI algorithms for art authentication and to detect forgeries.

Researchers have also introduced models that predict emotional responses to art. One such model is ArtEmis, a large-scale dataset paired with machine learning models. ArtEmis includes emotional annotations from over 6,500 participants along with textual explanations. By analyzing both visual inputs and the accompanying text descriptions from this dataset, ArtEmis enables the generation of nuanced emotional predictions.

Other forms of AI art

AI has also been used in arts outside of visual arts. Generative AI has been used to create music, as well as 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. AI has also been used in the literary arts, such as helping with writer's block, inspiration, or rewriting segments. In the culinary arts, some prototype cooking robots can dynamically taste, which can assist chefs in analyzing the content and flavor of dishes during the cooking process.

See also

References

{{Western art movements

20th-century introductions
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