AI art

{{Short description|none}} {{hatnote group|{{Distinguish|Generative art}}{{about|AI-generated visual art|other forms of AI-generated content|natural language generation|and|music and artificial intelligence|and|text-to-video model}}}} {{Use dmy dates|date=March 2026}}{{Use American English|date=May 2026}} [[File:Théâtre D’opéra Spatial.png|thumb|upright=1.2|''Théâtre D'opéra Spatial'' (Space Opera Theater; 2022) won the 2022 Colorado State Fair's annual fine art competition in the "emerging artist" (non-professional) division of the "Digital Arts/Digitally-Manipulated Photography" category. |alt=Impressionistic image of figures in a futuristic opera scene]] {{Artificial intelligence}}

'''Artificial intelligence visual art''', or '''AI art''', is visual artwork generated or enhanced through the implementation of artificial intelligence (AI) programs, most commonly using text-to-image models. The process of automated art-making has existed since antiquity. The field of artificial intelligence was founded in the 1950s, and artists began to create art with artificial intelligence shortly after the discipline's founding. A select number of these creations have been showcased in museums and have been recognized with awards.<ref>{{cite journal |last1=Todorovic |first1=Milos |title=AI and Heritage: A Discussion on Rethinking Heritage in a Digital World |journal=International Journal of Cultural and Social Studies |year=2024 |volume=10 |issue=1 |pages=1–11 |doi=10.46442/intjcss.1397403 |url=https://www.academia.edu/121596389 |access-date=4 July 2024 |archive-date=18 August 2024 |archive-url=https://web.archive.org/web/20240818164727/https://www.academia.edu/121596389 |url-status=live }}</ref> Throughout its history, AI has raised many philosophical questions related to the human mind, artificial beings, and the nature of ''art'' in human–AI collaboration.

During the AI boom of the 2020s, text-to-image models such as Midjourney, DALL-E and Stable Diffusion became widely available to the public, allowing users to quickly generate imagery with little effort.<ref name="imagen-verge">{{cite news | last1 = Vincent | first1 = James | title = All these images were generated with Google's latest text-to-image AI | url = https://www.theverge.com/2022/5/24/23139297/google-imagen-text-to-image-ai-system-examples-paper | access-date = 28 May 2022 | work = The Verge | date = 24 May 2022 | archive-date = 15 February 2023 | archive-url = https://web.archive.org/web/20230215224340/https://www.theverge.com/2022/5/24/23139297/google-imagen-text-to-image-ai-system-examples-paper | url-status = live }}</ref><ref name="Edwards">{{Cite web |last=Edwards |first=Benj |date=2 August 2024 |title=FLUX: This new AI image generator is eerily good at creating human hands |url=https://arstechnica.com/information-technology/2024/08/flux-this-new-ai-image-generator-is-eerily-good-at-creating-human-hands/ |access-date=17 November 2024 |website=Ars Technica |language=en-US |archive-date=12 May 2025 |archive-url=https://web.archive.org/web/20250512234400/https://arstechnica.com/information-technology/2024/08/flux-this-new-ai-image-generator-is-eerily-good-at-creating-human-hands/ |url-status=live }}</ref> 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.

In August 2023, the US Supreme Court ruled that AI art is ineligible for copyright due to failure to meet human authorship.<ref>{{Cite web |date=20 August 2023 |title=AI-generated art can't be copyrighted, US court rules |url=https://www.jpost.com/business-and-innovation/all-news/article-755483 |access-date=3 March 2026 |website=The Jerusalem Post |language=en}}</ref><ref>{{Cite web |title=Court Finds AI-Generated Work Is Not Copyrightable |url=https://www.jonesday.com/en/insights/2023/08/court-finds-aigenerated-work-not-copyrightable-for-failure-to-meet-human-authorship-requirementbut-questions-remain |access-date=3 March 2026 |website=jonesday.com |language=en}}</ref> In March 2026, it declined to hear a case over whether AI-generated art can be subject to copyright.<ref>{{Cite web |last=Brittain |first=Blake |date=3 March 2026 |title=US Supreme Court declines to hear dispute over copyrights for AI-generated material |url=https://www.reuters.com/legal/government/us-supreme-court-declines-hear-dispute-over-copyrights-ai-generated-material-2026-03-02/ |access-date=7 March 2026 |website=Reuters}}</ref><ref>{{Cite web |last=Guy |first=Zoe |date=21 August 2023 |title=AI-Generated Art Is Not Copyrightable, Judge Rules |url=https://www.vulture.com/2023/08/ai-art-copyright-ineligible.html |access-date=3 March 2026 |website=Vulture |language=en |archive-date=23 August 2025 |archive-url=https://web.archive.org/web/20250823173436/https://www.vulture.com/2023/08/ai-art-copyright-ineligible.html |url-status=live }}</ref><ref>{{Cite web |last=Roth |first=Emma |date=2 March 2026 |title=AI-generated art can't be copyrighted after Supreme Court declines to review the rule |url=https://www.theverge.com/policy/887678/supreme-court-ai-art-copyright |access-date=3 March 2026 |website=The Verge |language=en-US |archive-date=2 March 2026 |archive-url=https://web.archive.org/web/20260302225410/https://www.theverge.com/policy/887678/supreme-court-ai-art-copyright |url-status=live }}</ref>

== {{anchor|Awards and recognition}}History == {{See also|History of artificial intelligence|Timeline of artificial intelligence}}

=== Early history === [[File:Maillardet's_automaton_at_the_Franklin_Institute.webm|thumb|Maillardet's automaton drawing a picture]] 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.<ref>{{citation | author = Noel Sharkey | title = A programmable robot from 60 AD | date = 4 July 2007 | url = https://www.newscientist.com/blog/technology/2007/07/programmable-robot-from-60ad.html | volume = 2611 | access-date = 22 October 2019 | archive-url = https://web.archive.org/web/20180113090903/https://www.newscientist.com/blog/technology/2007/07/programmable-robot-from-60ad.html | url-status = live | work = New Scientist | archive-date = 13 January 2018 }}</ref><ref>{{Citation | last = Brett | first = Gerard | title = The Automata in the Byzantine "Throne of Solomon" | date = July 1954 | journal = Speculum | volume = 29 | issue = 3 | pages = 477–487 | postscript = . | doi = 10.2307/2846790 | issn = 0038-7134 | jstor = 2846790 | s2cid = 163031682 }}</ref> Creative automatons have flourished throughout history, such as Maillardet's automaton, created around 1800 and capable of creating multiple drawings and poems.<ref>{{Cite web | date = 8 March 2014 | title = Maillardet's Automaton | url = https://www.fi.edu/en/history-resources/automaton | access-date = 24 August 2023 | website = The Franklin Institute | language = en | archive-date = 24 August 2023 | archive-url = https://web.archive.org/web/20230824140212/https://www.fi.edu/en/history-resources/automaton | url-status = live }}</ref>

Also in the 19th century, Ada Lovelace, wrote that "computing operations" could potentially be used to generate music and poems.<ref>{{Cite journal |last1=Natale |first1=Simone |last2=Henrickson |first2=Leah |date=4 March 2022 |title=The Lovelace effect: Perceptions of creativity in machines |url=https://doi.org/10.1177/14614448221077278 |journal=New Media & Society |volume=26 |issue=4 |pages=1909–1926 |doi=10.1177/14614448221077278 |issn=1461-4448 |archive-date=27 January 2022 |access-date=7 March 2026 |archive-url=https://web.archive.org/web/20220127000000/https://doi.org/10.1177/14614448221077278 |url-status=live |url-access=subscription }}</ref><ref>Lovelace, A. (1843). Notes by the translator. Taylor's Scientific Memoirs, 3, 666-731.</ref> In 1950, Alan Turing's paper "Computing Machinery and Intelligence" focused on whether machines can mimic human behavior convincingly.<ref>{{Cite web |last=Turing |first=Alan |date=October 1950 |title=Computing Machinery and Intelligence |url=https://courses.cs.umbc.edu/471/papers/turing.pdf |access-date=16 September 2024}}</ref> Shortly after, the academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956.<ref>{{Cite book | last = Crevier | first = Daniel | title = AI: The Tumultuous Search for Artificial Intelligence. | publisher = BasicBooks | year = 1993 | isbn = 0-465-02997-3 | location = New York, NY | page = 109 }}</ref>

Since its founding, AI researchers 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.<ref>{{Cite book | last = Newquist | first = HP | title = The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think | publisher = Macmillan/SAMS | year = 1994 | isbn = 978-0-672-30412-5 | location = New York | pages = 45–53 }}</ref>

=== Artistic history === [[File:Galapagos-icc-2.jpg|thumb|Karl Sims' ''Galápagos'' installation allowed visitors to evolve 3D animated forms.]]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,<ref name="elgammalblurring">{{Cite journal | last = Elgammal | first = Ahmed |year = 2019 | title = AI Is Blurring the Definition of Artist | journal = American Scientist | volume = 107 | issue = 1 | page = 18 | doi = 10.1511/2019.107.1.18 | s2cid = 125379532 | issn = 0003-0996 }}</ref> computer art, digital art, or new media art.<ref>{{Cite journal | last = Greenfield | first = Gary | date = 3 April 2015 | title = When the machine made art: the troubled history of computer art, by Grant D. Taylor | url = http://www.tandfonline.com/doi/full/10.1080/17513472.2015.1009865 | journal = Journal of Mathematics and the Arts | language = en | volume = 9 | issue = 1–2 | pages = 44–47 | doi = 10.1080/17513472.2015.1009865 | s2cid = 118762731 | issn = 1751-3472 | url-access = subscription }}</ref>

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.<ref>{{Cite book | last = McCorduck | first = Pamela | title = AARONS's Code: Meta-Art. Artificial Intelligence, and the Work of Harold Cohen | publisher = W. H. Freeman and Company | year = 1991 | isbn = 0-7167-2173-2 | location = New York | page = 210 | language = English }}</ref> 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.<ref>{{Cite book | last1 = Poltronieri | first1 = Fabrizio Augusto | last2 = Hänska | first2 = Max | title = Proceedings of the 9th International Conference on Digital and Interactive Arts | chapter = Technical Images and Visual Art in the Era of Artificial Intelligence | date = 23 October 2019 | chapter-url = https://dl.acm.org/doi/10.1145/3359852.3359865 | language = en | location = Braga Portugal | publisher = ACM | pages = 1–8 | doi = 10.1145/3359852.3359865 | isbn = 978-1-4503-7250-3 | s2cid = 208109113 | archive-date = 29 September 2022 | access-date = 10 May 2022 | archive-url = https://web.archive.org/web/20220929044731/https://dl.acm.org/doi/10.1145/3359852.3359865 | url-status = live }}</ref> AARON was exhibited in 1972 at the Los Angeles County Museum of Art.<ref>{{Cite web | date = 9 May 2016 | title = HAROLD COHEN (1928–2016) | url = https://www.artforum.com/news/harold-cohen-1928-2016-59932 | access-date = 19 September 2023 | website = Art Forum | language = en-US }}</ref> From 1973 to 1975, Cohen refined AARON during a residency at the Artificial Intelligence Laboratory at Stanford University.<ref name="Diehl">{{Cite news |last=Diehl |first=Travis |date=15 February 2024 |title=A.I. Art That's More Than a Gimmick? Meet AARON |url=https://www.nytimes.com/2024/02/15/arts/design/aaron-ai-whitney.html |access-date=1 June 2024 |work=The New York Times |language=en-US |issn=0362-4331}}</ref> 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.<ref name="Diehl" />

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.<ref>{{Cite web |date=20 August 2017 |title=Karl Sims - ACM SIGGRAPH HISTORY ARCHIVES |url=https://history.siggraph.org/person/karl-sims/ |access-date=9 June 2024 |website=history.siggraph.org |language=en-US}}</ref><ref>{{Cite web |title=Karl Sims |url=https://cap.csail.mit.edu/engage/spotlights/karl-sims |access-date=9 June 2024 |website=CSAIL Alliances |language=en |archive-date=9 June 2024 |archive-url=https://web.archive.org/web/20240609211703/https://cap.csail.mit.edu/engage/spotlights/karl-sims |url-status=live }}</ref><ref>{{Cite web |title=Karl Sims |url=https://www.macfound.org/fellows/class-of-1998/karl-sims |access-date=9 June 2024 |website=macfound.org |language=en |archive-date=9 June 2024 |archive-url=https://web.archive.org/web/20240609212310/https://www.macfound.org/fellows/class-of-1998/karl-sims |url-status=live }}</ref> In both 1991 and 1992, Sims won the Golden Nica award at Prix Ars Electronica for his videos using artificial evolution.<ref>{{Cite web |title = Golden Nicas |url = https://ars.electronica.art/center/en/golden-nicas/ |access-date = 26 February 2023 |website = Ars Electronica Center |language = en-US |archive-date = 26 February 2023 |archive-url = https://web.archive.org/web/20230226175530/https://ars.electronica.art/center/en/golden-nicas/ }}</ref><ref>{{Cite web | title = Panspermia by Karl Sims, 1990 | url = http://www.karlsims.com/panspermia.html | access-date = 26 February 2023 | website = karlsims.com | archive-date = 26 November 2023 | archive-url = https://web.archive.org/web/20231126095130/https://www.karlsims.com/panspermia.html | url-status = live }}</ref><ref>{{Cite web | title = Liquid Selves by Karl Sims, 1992 | url = http://www.karlsims.com/liquid-selves.html | access-date = 26 February 2023 | website = karlsims.com }}</ref> In 1997, Sims created the interactive artificial evolution installation ''Galápagos'' for the NTT InterCommunication Center in Tokyo.<ref>{{Cite web |title="Galápagos" - Karl SIMS (1997) |url=https://www.ntticc.or.jp/en/archive/works/galapagos/ |access-date=14 June 2024 |website=NTT InterCommunication Center [ICC] |language=en |archive-date=14 June 2024 |archive-url=https://web.archive.org/web/20240614165733/https://www.ntticc.or.jp/en/archive/works/galapagos/ |url-status=live }}</ref> Sims received an Emmy Award in 2019 for outstanding achievement in engineering development.<ref>{{Cite web |title=Winners |url=https://www.emmys.com/awards/engineering-emmys/winners |access-date=26 June 2022 |website=Television Academy |language=en |archive-date=1 July 2020 |archive-url=https://web.archive.org/web/20200701234947/https://www.emmys.com/awards/engineering-emmys/winners |url-status=live }}</ref>

[[File:Electricsheep-0-1000.jpg|thumb|Example of ''Electric Sheep'' by Scott Draves]]In 1999, Scott Draves and a team of several engineers created and released ''Electric Sheep'' as a free software screensaver.<ref>{{Cite book |last=Draves |first=Scott |title=Applications of Evolutionary Computing |date=2005 |publisher=Springer |isbn=978-3-540-32003-6 |editor-last=Rothlauf |editor-first=Franz |series=Lecture Notes in Computer Science |volume=3449 |location=Berlin, Heidelberg |pages=458–467 |language=en |chapter=The Electric Sheep Screen-Saver: A Case Study in Aesthetic Evolution |doi=10.1007/978-3-540-32003-6_46 |editor2-last=Branke |editor2-first=Jürgen |editor3-last=Cagnoni |editor3-first=Stefano |editor4-last=Corne |editor4-first=David Wolfe |editor5-last=Drechsler |editor5-first=Rolf |editor6-last=Jin |editor6-first=Yaochu |editor7-last=Machado |editor7-first=Penousal |editor8-last=Marchiori |editor8-first=Elena |editor9-last=Romero |editor9-first=Juan |chapter-url=https://link.springer.com/chapter/10.1007/978-3-540-32003-6_46 |s2cid=14256872 |archive-date=7 October 2024 |access-date=17 July 2024 |archive-url=https://web.archive.org/web/20241007102424/https://link.springer.com/chapter/10.1007/978-3-540-32003-6_46 |url-status=live }}</ref> ''Electric Sheep'' is a volunteer computing project for animating and evolving fractal flames, which are distributed to networked computers that 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''.<ref>{{Cite web |title=Entrevista Scott Draves - Primer Premio Ex-Aequo VIDA 4.0 |via=YouTube |date=17 July 2012 |url=https://www.youtube.com/watch?v=wybvI279EQ4 |access-date=26 February 2023 |archive-date=28 December 2023 |archive-url=https://web.archive.org/web/20231228143143/https://www.youtube.com/watch?v=wybvI279EQ4 |url-status=live }}</ref>{{Unreliable source?|date=April 2025}}

In 2014, Stephanie Dinkins began working on ''Conversations with Bina48''.<ref>{{Cite web |date=7 November 2017 |title=Robots, Race, and Algorithms: Stephanie Dinkins at Recess Assembly |url=http://magazine.art21.org/2017/11/07/robots-race-and-algorithms-stephanie-dinkins-at-recess-assembly/ |access-date=25 February 2020 |website=Art21 Magazine |archive-date=16 February 2020 |archive-url=https://web.archive.org/web/20200216175116/http://magazine.art21.org/2017/11/07/robots-race-and-algorithms-stephanie-dinkins-at-recess-assembly/ |url-status=live }}</ref> For the series, Dinkins recorded her conversations with BINA48, a social robot that resembles a middle-aged black woman.<ref>{{Cite web |last=Small |first=Zachary |date=7 April 2017 |title=Future Perfect: Flux Factory's Intersectional Approach to Technology |url=https://www.artnews.com/art-in-america/features/future-perfect-a-call-for-intersectional-technology-at-flux-factory-58475/ |access-date=4 May 2020 |website=ARTnews |language=en-US |archive-date=12 September 2024 |archive-url=https://web.archive.org/web/20240912181958/https://www.artnews.com/art-in-america/features/future-perfect-a-call-for-intersectional-technology-at-flux-factory-58475/ |url-status=live }}</ref><ref>{{Cite web |last=Dunn |first=Anna |date=11 July 2018 |title=Multiply, Identify, Her |url=https://brooklynrail.org/2018/07/artseen/Multiply-Identify-Her |website=The Brooklyn Rail |access-date=25 February 2025 |archive-date=19 March 2023 |archive-url=https://web.archive.org/web/20230319174504/https://brooklynrail.org/2018/07/artseen/Multiply-Identify-Her |url-status=live }}</ref> 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."<ref>{{Cite web |title=Not the Only One |url=https://creative-capital.org/projects/not-the-only-one/ |access-date=26 February 2023 |website=Creative Capital |language=en |archive-date=16 February 2020 |archive-url=https://web.archive.org/web/20200216181144/https://creative-capital.org/projects/not-the-only-one/ |url-status=live }}</ref>

In 2015, Sougwen Chung began ''Mimicry (Drawing Operations Unit: Generation 1)'', an ongoing collaboration between the artist and a robotic arm.<ref>{{Cite web |title=Drawing Operations (2015) – Sougwen Chung (愫君) |date=4 November 2019 |url=https://sougwen.com/project/drawing-operations |access-date=25 February 2025 |language=en-US}}</ref> 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.<ref>{{Cite web |title=Sougwen Chung |url=https://www.lumenprize.com/2019-winners/sougwen-chung |access-date=26 February 2023 |website=The Lumen Prize |language=en-GB |archive-date=26 February 2023 |archive-url=https://web.archive.org/web/20230226191508/https://www.lumenprize.com/2019-winners/sougwen-chung |url-status=live }}</ref> [[File:Edmond de Belamy.png|thumb|''Edmond de Belamy'', created with a generative adversarial network in 2018]]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 {{Currency|432,500|USD}}, which was almost 45 times higher than its estimate of {{Currency|7,000|USD|linked=no}}–10,000. The artwork was created by Obvious, a Paris-based collective.<ref>{{cite web |date=12 December 2018 |title=Is artificial intelligence set to become art's next medium? |url=https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx |access-date=21 May 2019 |website=Christie's |archive-date=5 February 2023 |archive-url=https://web.archive.org/web/20230205003326/https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx |url-status=live }}</ref><ref>{{Cite news |last=Cohn |first=Gabe |date=25 October 2018 |title=AI Art at Christie's Sells for $432,500 |url=https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html |url-status=live |archive-url=https://web.archive.org/web/20190505102713/https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html |archive-date=5 May 2019 |access-date=26 May 2024 |newspaper=The New York Times |language=en-US |issn=0362-4331}}</ref><ref>{{Cite web |last=Turnbull |first=Amanda |date=6 January 2020 |title=The price of AI art: Has the bubble burst? |url=http://theconversation.com/the-price-of-ai-art-has-the-bubble-burst-128698 |url-status=live |archive-url=https://web.archive.org/web/20240526121500/http://theconversation.com/the-price-of-ai-art-has-the-bubble-burst-128698 |archive-date=26 May 2024 |access-date=26 May 2024 |website=The Conversation |language=en-US}}</ref>

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.<ref>{{Cite web |last=Cayanan |first=Joanna |date=13 July 2024 |title=Novelist Otsuichi Co-Directs generAIdoscope, Omnibus Film Produced Entirely With Generative AI |url=https://www.animenewsnetwork.com/news/2024-07-13/novelist-otsuichi-co-directs-generaidoscope-omnibus-film-produced-entirely-with-generative-ai/.213069 |access-date=4 March 2025 |website=Anime News Network |language=en |archive-date=4 March 2025 |archive-url=https://web.archive.org/web/20250304090900/https://www.animenewsnetwork.com/news/2024-07-13/novelist-otsuichi-co-directs-generaidoscope-omnibus-film-produced-entirely-with-generative-ai/.213069 |url-status=live }}</ref>

In 2025, the 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.<ref>{{Cite web |last=Hodgkins |first=Crystalyn |date=28 February 2025 |title=Frontier Works, KaKa Creation's Twins Hinahima AI Anime Reveals March 29 TV Debut |url=https://www.animenewsnetwork.com/news/2025-02-28/frontier-works-kaka-creation-twins-hinahima-ai-anime-reveals-march-29-tv-debut/.221769 |access-date=4 March 2025 |website=Anime News Network |language=en |archive-date=28 February 2025 |archive-url=https://web.archive.org/web/20250228124525/https://www.animenewsnetwork.com/news/2025-02-28/frontier-works-kaka-creation-twins-hinahima-ai-anime-reveals-march-29-tv-debut/.221769 |url-status=live }}</ref><ref>{{Cite web |title=サポーティブAIとは - アニメ「ツインズひなひま」公式サイト |trans-title=What's Supportive AI? - Twins Hinahima Anime Official Website |url=https://anime-hinahima.com/supportive-ai/ |access-date=4 March 2025 |website=anime-hinahima.com |language=ja |archive-date=4 March 2025 |archive-url=https://web.archive.org/web/20250304081318/https://anime-hinahima.com/supportive-ai/ |url-status=live }}</ref>

=== Technical history === Deep learning, characterized by its multi-layer structure that attempts to mimic the human brain, first came about in the 2010s, causing a significant shift in the world of AI art.<ref>{{Cite web |date=17 June 2024 |title=What Is Deep Learning? |url=https://www.ibm.com/topics/deep-learning |access-date=13 November 2024 |website=IBM |language=en}}</ref> 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.<ref>{{cite conference | last1 = Goodfellow | first1 = Ian | last2 = Pouget-Abadie | first2 = Jean | last3 = Mirza | first3 = Mehdi | last4 = Xu | first4 = Bing | last5 = Warde-Farley | first5 = David | last6 = Ozair | first6 = Sherjil | last7 = Courville | first7 = Aaron | last8 = Bengio | first8 = Yoshua | year = 2014 | title = Generative Adversarial Nets | url = https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf | conference = Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014) | pages = 2672–2680 | access-date = 26 January 2022 | archive-date = 22 November 2019 | archive-url = https://web.archive.org/web/20191122034612/http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf | url-status = live }}</ref> Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images.<ref name="elgammalblurring" />

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.<ref name="codexample">{{cite web | last1 = Mordvintsev | first1 = Alexander | last2 = Olah | first2 = Christopher | last3 = Tyka | first3 = Mike | year = 2015 | title = DeepDream - a code example for visualizing Neural Networks | url = http://googleresearch.blogspot.co.uk/2015/07/deepdream-code-example-for-visualizing.html | archive-url = https://web.archive.org/web/20150708233542/http://googleresearch.blogspot.co.uk/2015/07/deepdream-code-example-for-visualizing.html | archive-date = 8 July 2015 | publisher = Google Research }}</ref><ref>{{cite web | last1 = Mordvintsev | first1 = Alexander | last2 = Olah | first2 = Christopher | last3 = Tyka | first3 = Mike | year = 2015 | title = Inceptionism: Going Deeper into Neural Networks | url = http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html | archive-url = https://web.archive.org/web/20150703064823/http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html | archive-date = 3 July 2015 | publisher = Google Research }}</ref><ref>{{cite conference | last1 = Szegedy | first1 = Christian | last2 = Liu | first2 = Wei | last3 = Jia | first3 = Yangqing | last4 = Sermanet | first4 = Pierre | last5 = Reed | first5 = Scott E. | last6 = Anguelov | first6 = Dragomir | last7 = Erhan | first7 = Dumitru | last8 = Vanhoucke | first8 = Vincent | last9 = Rabinovich | first9 = Andrew | arxiv = 1409.4842 | contribution = Going deeper with convolutions | doi = 10.1109/CVPR.2015.7298594 | pages = 1–9 | publisher = IEEE Computer Society | title = IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7–12, 2015 | year = 2015 | isbn = 978-1-4673-6964-0 }}</ref> The process creates deliberately over-processed images with a dream-like appearance reminiscent of a psychedelic experience.<ref name="codexample" /> 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.<ref>{{Cite web |last=Reynolds |first=Matt |date=7 April 2017 |title=New computer vision challenge wants to teach robots to see in 3D |url=https://www.newscientist.com/article/2127131-new-computer-vision-challenge-wants-to-teach-robots-to-see-in-3d/ |access-date=15 November 2024 |website=New Scientist |language=en-US |archive-date=30 October 2018 |archive-url=https://web.archive.org/web/20181030033114/https://www.newscientist.com/article/2127131-new-computer-vision-challenge-wants-to-teach-robots-to-see-in-3d/ |url-status=live }}</ref><ref>{{Cite web |last=Markoff |first=John |date=19 November 2012 |title=Seeking a Better Way to Find Web Images |website=The New York Times |url=https://www.nytimes.com/2012/11/20/science/for-web-images-creating-new-technology-to-seek-and-find.html?smid=url-share |access-date=15 November 2024 |archive-date=28 December 2024 |archive-url=https://web.archive.org/web/20241228111230/https://www.nytimes.com/2012/11/20/science/for-web-images-creating-new-technology-to-seek-and-find.html?smid=url-share |url-status=live }}</ref> 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.<ref>{{Cite journal |last1=Odena |first1=Augustus |last2=Olah |first2=Christopher |last3=Shlens |first3=Jonathon |date=17 July 2017 |title=Conditional Image Synthesis with Auxiliary Classifier GANs |url=https://proceedings.mlr.press/v70/odena17a.html |journal=International Conference on Machine Learning |language=en |publisher=PMLR |pages=2642–2651 |arxiv=1610.09585 |archive-date=16 September 2024 |access-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916224134/https://proceedings.mlr.press/v70/odena17a.html |url-status=live }}</ref>

Autoregressive models were used for image generation, such as PixelRNN (2016), which autoregressively generates one pixel after another with a recurrent neural network.<ref>{{Cite journal |last1=Oord |first1=Aäron van den |last2=Kalchbrenner |first2=Nal |last3=Kavukcuoglu |first3=Koray |date=11 June 2016 |title=Pixel Recurrent Neural Networks |url=https://proceedings.mlr.press/v48/oord16.html |journal=Proceedings of the 33rd International Conference on Machine Learning |language=en |publisher=PMLR |pages=1747–1756 |archive-date=9 August 2024 |access-date=16 September 2024 |archive-url=https://web.archive.org/web/20240809193053/https://proceedings.mlr.press/v48/oord16.html |url-status=live }}</ref> 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.<ref>{{Cite journal |last1=Parmar |first1=Niki |last2=Vaswani |first2=Ashish |last3=Uszkoreit |first3=Jakob |last4=Kaiser |first4=Lukasz |last5=Shazeer |first5=Noam |last6=Ku |first6=Alexander |last7=Tran |first7=Dustin |date=3 July 2018 |title=Image Transformer |url=https://proceedings.mlr.press/v80/parmar18a.html |journal=Proceedings of the 35th International Conference on Machine Learning |language=en |publisher=PMLR |pages=4055–4064 |archive-date=16 September 2024 |access-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916071412/https://proceedings.mlr.press/v80/parmar18a.html |url-status=live }}</ref>

The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN<ref>{{cite web | url = https://www.artbreeder.com/about | title = About | access-date = 3 March 2021 | last = Simon | first = Joel | archive-url = https://web.archive.org/web/20210302075357/https://www.artbreeder.com/about | archive-date = 2 March 2021 | url-status = live }}</ref><ref>{{Cite book | first1 = Binto | last1 = George | first2 = Gail | last2 = Carmichael | title = Artificial Intelligence Simplified: Understanding Basic Concepts -- the Second Edition | pages = 7–25 | url = https://books.google.com/books?id=duQaEAAAQBAJ | editor-first = Susan | editor-last = Mathai | isbn = 978-1-944708-04-7 | date = 2021 | publisher = CSTrends LLP }}</ref> to allow users to generate and modify images such as faces, landscapes, and paintings.<ref>{{cite web | url = https://www.digitalartsonline.co.uk/news/creative-software/will-this-creepy-ai-platform-put-artists-out-of-job/ | title = Will this creepy AI platform put artists out of a job? | access-date = 3 March 2021 | last = Lee | first = Giacomo | date = 21 July 2020 | archive-url = https://web.archive.org/web/20201222214934/https://www.digitalartsonline.co.uk/news/creative-software/will-this-creepy-ai-platform-put-artists-out-of-job/ | archive-date = 22 December 2020 | website = Digital Arts Online | url-status = live }}</ref>

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.<ref name="imagen-verge" />

{{Multiple image | direction = vertical | total_width = 300 | image1 = Scenic Valley in the Afternoon Artistic (VQGAN+CLIP).jpg | alt1 = | caption1 = Example of an image made with VQGAN-CLIP (NightCafe Studio, March 2023) | image2 = Sunset Valley (FLUX 1.1 Pro Raw).webp | caption2 = Example of an image made with Flux 1.1 Pro in Raw mode (November 2024); this mode is designed to generate photorealistic images }}

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.<ref>{{cite arXiv |last1=Ramesh |first1=Aditya |last2=Pavlov |first2=Mikhail |last3=Goh |first3=Gabriel |last4=Gray |first4=Scott |last5=Voss |first5=Chelsea |last6=Radford |first6=Alec |last7=Chen |first7=Mark |last8=Sutskever |first8=Ilya |eprint=2102.12092 |title=Zero-Shot Text-to-Image Generation |class=cs.LG |date=24 February 2021}}</ref> It is 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<ref>{{cite web | last1 = Burgess | first1 = Phillip | title = Generating AI "Art" with VQGAN+CLIP | url = https://learn.adafruit.com/generating-ai-art-with-vqgan-clip | access-date = 20 July 2022 | website = Adafruit | archive-date = 28 September 2022 | archive-url = https://web.archive.org/web/20220928164033/https://learn.adafruit.com/generating-ai-art-with-vqgan-clip | url-status = live }}</ref> based on OpenAI's CLIP model.<ref>{{cite arXiv |last1=Radford |first1=Alec |last2=Kim |first2=Jong Wook |last3=Hallacy |first3=Chris |last4=Ramesh |first4=Aditya |last5=Goh |first5=Gabriel |last6=Agarwal |first6=Sandhini |last7=Sastry |first7=Girish |last8=Askell |first8=Amanda |last9=Mishkin |first9=Pamela |last10=Clark |first10=Jack |last11=Krueger |first11=Gretchen |last12=Sutskever |first12=Ilya |title=Learning Transferable Visual Models From Natural Language Supervision |year=2021 |class=cs.CV |eprint=2103.00020}}</ref> Diffusion models, generative models used to create synthetic data based on existing data,<ref>{{Cite web |date=4 April 2024 |title=What Are Diffusion Models? |url=https://www.coursera.org/articles/diffusion-models |access-date=13 November 2024 |website=Coursera |language=en |archive-date=27 November 2024 |archive-url=https://web.archive.org/web/20241127053231/https://www.coursera.org/articles/diffusion-models |url-status=live }}</ref> were first proposed in 2015,<ref>{{Cite journal |last1=Sohl-Dickstein |first1=Jascha |last2=Weiss |first2=Eric |last3=Maheswaranathan |first3=Niru |last4=Ganguli |first4=Surya |date=1 June 2015 |title=Deep Unsupervised Learning using Nonequilibrium Thermodynamics |url=http://proceedings.mlr.press/v37/sohl-dickstein15.pdf |journal=Proceedings of the 32nd International Conference on Machine Learning |language=en |publisher=PMLR |volume=37 |pages=2256–2265 |arxiv=1503.03585 |archive-date=21 September 2024 |access-date=16 September 2024 |archive-url=https://web.archive.org/web/20240921065319/http://proceedings.mlr.press/v37/sohl-dickstein15.pdf |url-status=live }}</ref> but they only became better than GANs in early 2021.<ref>{{Cite journal |last1=Dhariwal |first1=Prafulla |last2=Nichol |first2=Alexander |year=2021 |title=Diffusion Models Beat GANs on Image Synthesis |url=https://proceedings.neurips.cc/paper/2021/hash/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html |journal=Advances in Neural Information Processing Systems |publisher=Curran Associates, Inc. |volume=34 |pages=8780–8794 |arxiv=2105.05233 |archive-date=16 September 2024 |access-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916071412/https://proceedings.neurips.cc/paper/2021/hash/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html |url-status=live }}</ref> Latent diffusion model was published in December 2021 and became the basis for the later Stable Diffusion (August 2022), developed through a collaboration between Stability AI, CompVis Group at LMU Munich, and Runway.<ref>{{Citation |last1=Rombach |first1=Robin |title=High-Resolution Image Synthesis with Latent Diffusion Models |date=20 December 2021 |arxiv=2112.10752 |last2=Blattmann |first2=Andreas |last3=Lorenz |first3=Dominik |last4=Esser |first4=Patrick |last5=Ommer |first5=Björn}}</ref>

In 2022, Midjourney<ref>{{cite news |last1=Rose |first1=Janus |title=Inside Midjourney, The Generative Art AI That Rivals DALL-E |url=https://www.vice.com/en/article/inside-midjourney-the-generative-art-ai-that-rivals-dall-e/ |work=Vice |date=18 July 2022 |archive-date=1 September 2022 |access-date=13 May 2025 |archive-url=https://web.archive.org/web/20220901124218/https://www.vice.com/en/article/wxn5wn/inside-midjourney-the-generative-art-ai-that-rivals-dall-e |url-status=live }}</ref> was released, followed by Google Brain's Imagen and Parti, which were announced in May 2022, Microsoft's NUWA-Infinity,<ref>{{Cite web | title = NUWA-Infinity | url = https://nuwa-infinity.microsoft.com/#/ | access-date = 10 August 2022 | website = nuwa-infinity.microsoft.com | archive-date = 6 December 2022 | archive-url = https://web.archive.org/web/20221206074305/https://nuwa-infinity.microsoft.com/#/ | url-status = live }}</ref><ref name="imagen-verge" /> and the source-available Stable Diffusion, which was released in August 2022.<ref>{{Cite web | title = Diffuse The Rest - a Hugging Face Space by huggingface | url = https://huggingface.co/spaces/huggingface/diffuse-the-rest | access-date = 5 September 2022 | website = huggingface.co | archive-date = 5 September 2022 | archive-url = https://web.archive.org/web/20220905141431/https://huggingface.co/spaces/huggingface/diffuse-the-rest | url-status = live }}</ref><ref>{{cite web | author-last = Heikkilä | author-first = Melissa | date = 16 September 2022 | title = This artist is dominating AI-generated art. And he's not happy about it. | url = https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/ | access-date = 2 October 2022 | work = MIT Technology Review | archive-date = 14 January 2023 | archive-url = https://web.archive.org/web/20230114125952/https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/ | url-status = live }}</ref><ref>{{cite web | date = 15 September 2022 | title = Stable Diffusion | url = https://github.com/CompVis/stable-diffusion | access-date = 15 September 2022 | publisher = CompVis - Machine Vision and Learning LMU Munich | archive-date = 18 January 2023 | archive-url = https://web.archive.org/web/20230118183342/https://github.com/CompVis/stable-diffusion | url-status = live }}</ref> DALL-E{{Nbsp}}2, a successor to DALL-E, was beta-tested and released (with the further successor DALL-E{{Nbsp}}3 being released in 2023). Stability AI has a Stable Diffusion web interface called DreamStudio,<ref>{{cite news | date = 18 October 2022 | title = Stable Diffusion creator Stability AI accelerates open-source AI, raises $101M | work = VentureBeat | url = https://venturebeat.com/ai/stable-diffusion-creator-stability-ai-raises-101m-funding-to-accelerate-open-source-ai/ | access-date = 10 November 2022 | archive-date = 12 January 2023 | archive-url = https://web.archive.org/web/20230112202229/https://venturebeat.com/ai/stable-diffusion-creator-stability-ai-raises-101m-funding-to-accelerate-open-source-ai/ | url-status = live }}</ref> plugins for Krita, Photoshop, Blender, and GIMP,<ref>{{cite web | last1 = Choudhary | first1 = Lokesh | date = 23 September 2022 | title = These new innovations are being built on top of Stable Diffusion | url = https://analyticsindiamag.com/these-new-innovations-are-being-built-on-top-of-stable-diffusion/ | access-date = 9 November 2022 | website = Analytics India Magazine | archive-date = 9 November 2022 | archive-url = https://web.archive.org/web/20221109155920/https://analyticsindiamag.com/these-new-innovations-are-being-built-on-top-of-stable-diffusion/ | url-status = live }}</ref> and the Automatic1111 web-based open source user interface.<ref>{{cite news | author1 = Dave James | date = 27 October 2022 | title = I thrashed the RTX 4090 for 8 hours straight training Stable Diffusion to paint like my uncle Hermann | language = en | work = PC Gamer | url = https://www.pcgamer.com/nvidia-rtx-4090-stable-diffusion-training-aharon-kahana/ | access-date = 9 November 2022 | archive-date = 9 November 2022 | archive-url = https://web.archive.org/web/20221109154310/https://www.pcgamer.com/nvidia-rtx-4090-stable-diffusion-training-aharon-kahana/ | url-status = live }}</ref><ref>{{cite web | last1 = Lewis | first1 = Nick | title = How to Run Stable Diffusion Locally With a GUI on Windows | url = https://www.howtogeek.com/832491/how-to-run-stable-diffusion-locally-with-a-gui-on-windows/ | access-date = 9 November 2022 | website = How-To Geek | date = 16 September 2022 | archive-date = 23 January 2023 | archive-url = https://web.archive.org/web/20230123161452/https://www.howtogeek.com/832491/how-to-run-stable-diffusion-locally-with-a-gui-on-windows/ | url-status = live }}</ref><ref>{{cite news | last1 = Edwards | first1 = Benj | date = 4 October 2022 | title = Begone, polygons: 1993's Virtua Fighter gets smoothed out by AI | language = en-us | work = Ars Technica | url = https://arstechnica.com/gaming/2022/10/begone-polygons-1993s-virtua-fighter-gets-smoothed-out-by-ai/ | access-date = 9 November 2022 | archive-date = 1 February 2023 | archive-url = https://web.archive.org/web/20230201210427/https://arstechnica.com/gaming/2022/10/begone-polygons-1993s-virtua-fighter-gets-smoothed-out-by-ai/ | url-status = live }}</ref> Stable Diffusion's main pre-trained model is shared on the Hugging Face Hub.<ref>{{cite news | last1 = Mehta | first1 = Sourabh | date = 17 September 2022 | title = How to Generate an Image from Text using Stable Diffusion in Python | work = Analytics India Magazine | url = https://analyticsindiamag.com/how-to-generate-an-image-from-text-using-stable-diffusion-on-python/ | access-date = 16 November 2022 | archive-date = 16 November 2022 | archive-url = https://web.archive.org/web/20221116134915/https://analyticsindiamag.com/how-to-generate-an-image-from-text-using-stable-diffusion-on-python/ | url-status = live }}</ref>

Ideogram was released in August 2023, this model is known for its ability to generate legible text.<ref>{{cite web |title=Announcing Ideogram AI |url=https://ideogram.ai/launch |access-date=13 June 2024 |website=Ideogram |archive-date=10 June 2024 |archive-url=https://web.archive.org/web/20240610012410/https://ideogram.ai/launch |url-status=live }}</ref><ref>{{Cite news |last=Metz |first=Rachel |date=3 October 2023 |title=Ideogram Produces Text in AI Images That You Can Actually Read |url=https://www.bloomberg.com/news/articles/2023-10-03/ideogram-produces-text-in-ai-images-that-you-can-actually-read |access-date=18 November 2024 |work=Bloomberg News |language=en |archive-date=12 November 2025 |archive-url=https://web.archive.org/web/20251112093952/https://www.bloomberg.com/news/articles/2023-10-03/ideogram-produces-text-in-ai-images-that-you-can-actually-read |url-status=live }}</ref>

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.<ref name="Edwards" /><ref>{{Cite web |title=Flux.1 – ein deutscher KI-Bildgenerator dreht mit Grok frei |url=https://www.handelsblatt.com/technik/ki/xai-kooperation-flux1-deutscher-ki-bildgenerator-dreht-mit-grok-frei/100059178.html |access-date=17 November 2024 |website=Handelsblatt |date=15 August 2024 |language=de |archive-date=30 August 2024 |archive-url=https://web.archive.org/web/20240830103514/https://www.handelsblatt.com/technik/ki/xai-kooperation-flux1-deutscher-ki-bildgenerator-dreht-mit-grok-frei/100059178.html |url-status=live }}</ref><ref>{{Cite web |last=Zeff |first=Maxwell |date=14 August 2024 |title=Meet Black Forest Labs, the startup powering Elon Musk's unhinged AI image generator |url=https://techcrunch.com/2024/08/14/meet-black-forest-labs-the-startup-powering-elon-musks-unhinged-ai-image-generator/ |access-date=17 November 2024 |website=TechCrunch |language=en-US |archive-date=17 November 2024 |archive-url=https://web.archive.org/web/20241117003305/https://techcrunch.com/2024/08/14/meet-black-forest-labs-the-startup-powering-elon-musks-unhinged-ai-image-generator/ |url-status=live }}</ref><ref>{{Cite web |last=Franzen |first=Carl |date=18 November 2024 |title=Mistral unleashes Pixtral Large and upgrades Le Chat into full-on ChatGPT competitor |url=https://venturebeat.com/ai/mistral-unleashes-pixtral-large-and-upgrades-le-chat-into-full-on-chatgpt-competitor/ |access-date=11 December 2024 |website=VentureBeat |language=en-US |archive-date=4 April 2025 |archive-url=https://web.archive.org/web/20250404104151/https://venturebeat.com/ai/mistral-unleashes-pixtral-large-and-upgrades-le-chat-into-full-on-chatgpt-competitor/ |url-status=live }}</ref> Flux was developed by Black Forest Labs, founded by the researchers behind Stable Diffusion.<ref>{{Cite web |last=Growcoot |first=Matt |date=5 August 2024 |title=AI Image Generator Made by Stable Diffusion Inventors on Par With Midjourney and DALL-E |url=https://petapixel.com/2024/08/05/ai-image-generator-made-by-stable-diffusion-inventors-on-par-with-midjourney-and-dall-e-flux1-black-forest-labs/ |access-date=17 November 2024 |website=PetaPixel |language=en |archive-date=7 March 2025 |archive-url=https://web.archive.org/web/20250307194114/https://petapixel.com/2024/08/05/ai-image-generator-made-by-stable-diffusion-inventors-on-par-with-midjourney-and-dall-e-flux1-black-forest-labs/ |url-status=live }}</ref> Grok later switched to its own text-to-image model Aurora in December of the same year.<ref>{{Cite web |last=Davis |first=Wes |date=7 December 2024 |title=X gives Grok a new photorealistic AI image generator |url=https://www.theverge.com/2024/12/7/24315644/grok-x-aurora-ai-image-generator-xai |access-date=10 December 2024 |website=The Verge |language=en |archive-date=12 December 2024 |archive-url=https://web.archive.org/web/20241212193314/https://www.theverge.com/2024/12/7/24315644/grok-x-aurora-ai-image-generator-xai |url-status=live }}</ref> 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.<ref>{{Cite web |last=Clark |first=Pam |date=14 October 2024 |title=Photoshop delivers powerful innovation for Image Editing, Ideation, 3D Design, and more |url=https://blog.adobe.com/en/publish/2024/10/14/photoshop-delivers-powerful-innovation-for-image-editing-ideation-3d-design-more |access-date=8 February 2025 |website=Adobe Blog |archive-date=30 January 2025 |archive-url=https://web.archive.org/web/20250130101938/https://blog.adobe.com/en/publish/2024/10/14/photoshop-delivers-powerful-innovation-for-image-editing-ideation-3d-design-more |url-status=live }}</ref><ref>{{Cite web |last=Chedraoui |first=Katelyn |date=19 October 2024 |title=Every New Feature Adobe Announced in Photoshop, Premiere Pro and More |url=https://www.cnet.com/tech/services-and-software/every-new-feature-adobe-announced-in-photoshop-premiere-pro-and-more/ |access-date=8 February 2025 |publisher=CNET |language=en |archive-date=5 February 2025 |archive-url=https://web.archive.org/web/20250205031858/https://www.cnet.com/tech/services-and-software/every-new-feature-adobe-announced-in-photoshop-premiere-pro-and-more/ |url-status=live }}</ref> Microsoft has also publicly announced AI image-generator features for Microsoft Paint.<ref>{{Cite web |last=Fajar |first=Aditya |date=28 August 2023 |title=Microsoft Paint will use AI in Windows update 11 |url=https://gizmologi.id/en/application/microsoft-paint-gunakan-ai/ |access-date=8 February 2025 |website=gizmologi.id |language=en}}</ref> Along with this, some examples of text-to-video models of the mid-2020s are Runway's Gen-4, Google's VideoPoet, OpenAI's Sora, which was released in December 2024, and LTX-2 which was released in 2025.<ref>{{Cite web |date=15 February 2024 |title=OpenAI teases 'Sora,' its new text-to-video AI model |url=https://www.nbcnews.com/tech/tech-news/openai-sora-video-artificial-intelligence-unveiled-rcna139065 |access-date=28 October 2024 |publisher=NBC News |language=en |archive-date=15 February 2024 |archive-url=https://web.archive.org/web/20240215235542/https://www.nbcnews.com/tech/tech-news/openai-sora-video-artificial-intelligence-unveiled-rcna139065 |url-status=live }}</ref><ref>{{Cite web |title=Sora |url=https://sora.com/ |access-date=27 December 2024 |website=Sora |language=en |archive-date=27 December 2024 |archive-url=https://web.archive.org/web/20241227045217/https://sora.com/ |url-status=live }}</ref><ref>{{Cite news |last1=Shahaf |first1=Tal |last2=Shahaf |first2=Tal |date=23 October 2025 |title=Lightricks unveils powerful AI video model challenging OpenAI and Google |url=https://www.ynetnews.com/tech-and-digital/article/hklbzavrgx |access-date=22 December 2025 |work=Ynetglobal |language=en}}</ref>

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.<ref>{{Cite web |last=Mehta |first=Ivan |date=1 April 2025 |title=OpenAI's new image generator is now available to all users |url=https://techcrunch.com/2025/03/31/openais-new-image-generator-is-now-available-to-all-users/ |access-date=12 June 2025 |website=TechCrunch |language=en-US |archive-date=10 June 2025 |archive-url=https://web.archive.org/web/20250610182108/https://techcrunch.com/2025/03/31/openais-new-image-generator-is-now-available-to-all-users/ |url-status=live }}</ref> MidJourney v7 debuted in April 2025, providing improved text prompt processing.<ref>{{Cite web |date=4 April 2025 |title=Midjourney launches its new V7 AI image model that can process text prompts better |url=https://www.engadget.com/ai/midjourney-launches-its-new-v7-ai-image-model-that-can-process-text-prompts-better-134546883.html |access-date=12 June 2025 |website=Engadget |language=en-US}}</ref> In May 2025, Flux.1 Kontext by Black Forest Labs emerged as an efficient model for high-fidelity image generation,<ref>{{Cite web |date=29 May 2025 |title=Introducing FLUX.1 Kontext and the BFL Playground |url=https://bfl.ai/announcements/flux-1-kontext |access-date=12 June 2025 |website=Black Forest Labs |language=en |archive-date=13 June 2025 |archive-url=https://web.archive.org/web/20250613112859/https://bfl.ai/announcements/flux-1-kontext |url-status=live }}</ref> while Google's Imagen 4 was released with improved photorealism.<ref>{{Cite web |last=Wiggers |first=Kyle |date=20 May 2025 |title=Imagen 4 is Google's newest AI image generator |url=https://techcrunch.com/2025/05/20/imagen-4-is-googles-newest-ai-image-generator/ |access-date=12 June 2025 |website=TechCrunch |language=en-US |archive-date=20 May 2025 |archive-url=https://web.archive.org/web/20250520181047/https://techcrunch.com/2025/05/20/imagen-4-is-googles-newest-ai-image-generator/ |url-status=live }}</ref> Flux.2 debuted in November 2025 with improved image reference, typography, and prompt understanding.<ref>{{cite web |last=Franzen |first=Carl |date=26 November 2025 |title=Black Forest Labs launches Flux.2 AI image models to challenge Nano Banana Pro and Midjourney |url=https://venturebeat.com/ai/black-forest-labs-launches-flux-2-ai-image-models-to-challenge-nano-banana |archive-url= |archive-date= |access-date=26 November 2025 |website=VentureBeat}}</ref>

== 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.<ref>{{Cite web |last=Wu |first=Yue |date=6 February 2025 |title=A Visual Guide to How Diffusion Models Work |url=https://towardsdatascience.com/a-visual-guide-to-how-diffusion-models-work/ |access-date=12 June 2025 |website=Towards Data Science |language=en-US |archive-date=13 March 2025 |archive-url=https://web.archive.org/web/20250313011437/https://towardsdatascience.com/a-visual-guide-to-how-diffusion-models-work/ |url-status=live }}</ref><ref>{{Cite web |date=30 April 2024 |title=Text-to-image: latent diffusion models |url=https://nicd.org.uk/knowledge-hub/image-to-text-latent-diffusion-models |access-date=12 June 2025 |website=nicd.org.uk |language=en}}</ref> 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.<ref>{{Cite web |title=Image-to-Image Translation |url=https://dataforest.ai/glossary/image-to-image-translation |access-date=12 June 2025 |website=dataforest.ai |language=en |archive-date=19 May 2025 |archive-url=https://web.archive.org/web/20250519114744/https://dataforest.ai/glossary/image-to-image-translation |url-status=live }}</ref><ref>{{Cite web |title=What Is Image-to-Image Translation? |url=https://www.techtarget.com/searchenterpriseai/definition/image-to-image-translation |access-date=12 June 2025 |website=Search Enterprise AI |language=en}}</ref> 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.<ref>{{Cite web |date=26 May 2025 |title=Unlocking AI: The Evolution of Image to Video Technology |url=https://jmcomms.com/2025/05/26/honors-new-image-to-video-service-powered-by-google-may-change-the-way-we-view-photographs-forever/ |access-date=13 June 2025 |website=JMComms |language=en-GB}}</ref><ref>{{Cite web |last=Digital |first=Hans India |date=3 June 2025 |title=The Small Business Advantage: Leveraging Image-to-Video AI for Big Impact |url=https://www.thehansindia.com/tech/ai/the-small-business-advantage-leveraging-image-to-video-ai-for-big-impact-977667 |access-date=13 June 2025 |website=thehansindia.com |language=en}}</ref> 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.<ref>{{Cite web |title=AI Video Generation: What Is It and How Does It Work? |url=https://www.colossyan.com/posts/ai-video-generation-what-is-it-and-how-does-it-work |access-date=12 June 2025 |website=colossyan.com |language=en |archive-date=18 April 2025 |archive-url=https://web.archive.org/web/20250418002852/https://www.colossyan.com/posts/ai-video-generation-what-is-it-and-how-does-it-work |url-status=live }}</ref>

=== Imagery === [[File:Generic SDXL ComfyUI Nodes (2024) (cropped).png|thumb|Example of a usage of ComfyUI for Stable Diffusion XL. People can adjust variables (such as CFG, seed, and sampler) needed to generate image.]] 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" image generation techniques have been developed, utilizing mathematical patterns, algorithms that simulate brush strokes and other painterly effects, as well as deep learning models such as generative adversarial networks (GANs) and transformers. Several companies have released applications and websites that allow users to focus exclusively on positive prompts, bypassing the need for manual configuration of other parameters. There are also programs capable of transforming photographs into stylized images that mimic the aesthetics of well-known painting styles.<ref>{{cite news | title = A.I. photo filters use neural networks to make photos look like Picassos | url = https://www.digitaltrends.com/mobile/best-ai-based-photo-apps/ | access-date = 9 November 2022 | work = Digital Trends | date = 18 November 2019 | language = en | archive-date = 9 November 2022 | archive-url = https://web.archive.org/web/20221109154316/https://www.digitaltrends.com/mobile/best-ai-based-photo-apps/ | url-status = live }}</ref><ref>{{cite news | last1 = Biersdorfer | first1 = J. D. | title = From Camera Roll to Canvas: Make Art From Your Photos | url = https://www.nytimes.com/2019/12/04/technology/personaltech/turn-photos-into-paintings.html | access-date = 9 November 2022 | work = The New York Times | date = 4 December 2019 | archive-date = 5 March 2024 | archive-url = https://web.archive.org/web/20240305142732/http://www.nytimes.com/2019/12/04/technology/personaltech/turn-photos-into-paintings.html | url-status = live }}</ref>

There are many options, ranging from simple consumer-facing mobile apps to Jupyter notebooks and web UIs that require powerful GPUs to run effectively.<ref>{{cite web | url = https://pharmapsychotic.com/tools.html | title = Tools and Resources for AI Art | first = Pharma | last = Psychotic | archive-url = https://archive.today/20220604120005/https://pharmapsychotic.com/tools.html | archive-date = 4 June 2022 | access-date = 26 June 2022 }}</ref> 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)<ref>{{Cite arXiv|last1=Gal |first1=Rinon |last2=Alaluf |first2=Yuval |last3=Atzmon |first3=Yuval |last4=Patashnik |first4=Or |last5=Bermano |first5=Amit H. |last6=Chechik |first6=Gal |last7=Cohen-Or |first7=Daniel |title=An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion |date=2 August 2022|class=cs.CV |eprint=2208.01618}}</ref><ref>{{cite web | title = Textual Inversion · AUTOMATIC1111/stable-diffusion-webui Wiki | url = https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion | website = GitHub | access-date = 9 November 2022 | language = en | archive-date = 7 February 2023 | archive-url = https://web.archive.org/web/20230207174304/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion | url-status = live }}</ref> 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 non-commercial niche genres (such as cyberpunk derivatives like solarpunk) by amateurs, novel entertainment, fast prototyping,<ref name="computerworld">{{cite news | last1 = Elgan | first1 = Mike | date = 1 November 2022 | title = How 'synthetic media' will transform business forever | language = en | work = Computerworld | url = https://www.computerworld.com/article/3678172/how-synthetic-media-will-transform-business-forever.html | access-date = 9 November 2022 | archive-date = 10 February 2023 | archive-url = https://web.archive.org/web/20230210114540/https://www.computerworld.com/article/3678172/how-synthetic-media-will-transform-business-forever.html | url-status = live }}</ref> increasing art-making accessibility,<ref name="computerworld" /> and artistic output per effort or expenses or time<ref name="computerworld" />—e.g., via generating drafts, draft-definitions, and image components (inpainting). Generated images are sometimes used as sketches,<ref name="nytimesRoose">{{cite news | last1 = Roose | first1 = Kevin | title = A.I.-Generated Art Is Already Transforming Creative Work | url = https://www.nytimes.com/2022/10/21/technology/ai-generated-art-jobs-dall-e-2.html | access-date = 16 November 2022 | work = The New York Times | date = 21 October 2022 | archive-date = 15 February 2023 | archive-url = https://web.archive.org/web/20230215010527/https://www.nytimes.com/2022/10/21/technology/ai-generated-art-jobs-dall-e-2.html | url-status = live }}</ref> low-cost experiments,<ref name="CNBCLeswing">{{cite news | last1 = Leswing | first1 = Kif | title = Why Silicon Valley is so excited about awkward drawings done by artificial intelligence | url = https://www.cnbc.com/2022/10/08/generative-ai-silicon-valleys-next-trillion-dollar-companies.html | access-date = 16 November 2022 | publisher = CNBC | language = en | archive-date = 8 February 2023 | archive-url = https://web.archive.org/web/20230208155024/https://www.cnbc.com/2022/10/08/generative-ai-silicon-valleys-next-trillion-dollar-companies.html | url-status = live }}</ref> 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.<ref name="CNBCLeswing" />

Professional visual artists and designers used generative AI in early-stage conceptualization (divergent thinking) more than final production (convergent thinking) and practices producing digital or ephemeral outputs (e.g., UI/UX design, concept art) more readily integrate these than those producing physical, permanent artifacts (e.g., sculpture, architecture).<ref name="Tsao">{{cite journal |last1=Tsao |first1=Jack |last2=Liang |first2=Cindy Xinyi |last3=Nogues |first3=Collier |last4=Wong |first4=Alice |title=Perceptions and integration of generative artificial intelligence in creative practices and industries: a scoping review and conceptual model |journal=AI & Society |year=2026 |volume=41 |issue=3 |pages=2259–2278 |doi=10.1007/s00146-025-02667-2 |url=https://doi.org/10.1007/s00146-025-02667-2}}</ref> In physical domains, concerns regarding structural integrity, material constraints, and cultural "ethno-computation" often limit AI to a "complementary enhancement" role rather than a substitute for production.<ref>{{cite journal |last1=Roncoroni |first1=U. L. |last2=Crousse De Vallongue |first2=V. |last3=Centurion Bolaños |first3=O. |title=Computational creativity issues in generative design and digital fabrication of complex 3D meshes |journal=International Journal of Architectural Computing |volume=23 |issue=2 |pages=582–600 |year=2024 |doi=10.1177/14780771241260850 |url=https://doi.org/10.1177/14780771241260850}}</ref> Furthermore, attitudes toward adoption vary significantly by career stage, with entry-level professionals viewing generative AI as a pragmatic extension of digital tools necessary for market competitiveness, whereas senior practitioners often express critical skepticism regarding the devaluation of embodied expertise and long-term skill development.<ref name="Tsao"/>

=== Prompt engineering and sharing === {{see also|Prompt engineering#Text-to-image}} 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 [name of an artist]" in the prompt<ref>{{cite news | last1 = Robertson | first1 = Adi | date = 15 November 2022 | title = How DeviantArt is navigating the AI art minefield | work = The Verge | url = https://www.theverge.com/2022/11/15/23449036/deviantart-ai-art-dreamup-training-data-controversy | access-date = 16 November 2022 | archive-date = 4 January 2023 | archive-url = https://web.archive.org/web/20230104133124/https://www.theverge.com/2022/11/15/23449036/deviantart-ai-art-dreamup-training-data-controversy | url-status = live }}</ref> /or selection of a broad aesthetic/art style.<ref>{{cite news | last1 = Proulx | first1 = Natalie | date = September 2022 | title = Are A.I.-Generated Pictures Art? | work = The New York Times | url = https://www.nytimes.com/2022/09/16/learning/are-ai-generated-pictures-art.html | access-date = 16 November 2022 | archive-date = 6 February 2023 | archive-url = https://web.archive.org/web/20230206143848/https://www.nytimes.com/2022/09/16/learning/are-ai-generated-pictures-art.html | url-status = live }}</ref><ref name="nytimesRoose" /> There are platforms for sharing, trading, searching, forking/refining, or collaborating on prompts for generating specific imagery from image generators.<ref>{{cite news | last1 = Vincent | first1 = James | date = 15 September 2022 | title = Anyone can use this AI art generator — that's the risk | work = The Verge | url = https://www.theverge.com/2022/9/15/23340673/ai-image-generation-stable-diffusion-explained-ethics-copyright-data | access-date = 9 November 2022 | archive-date = 21 January 2023 | archive-url = https://web.archive.org/web/20230121153021/https://www.theverge.com/2022/9/15/23340673/ai-image-generation-stable-diffusion-explained-ethics-copyright-data | url-status = live }}</ref><ref>{{cite news | last1 = Davenport | first1 = Corbin | title = This AI Art Gallery Is Even Better Than Using a Generator | url = https://www.howtogeek.com/831697/this-ai-art-gallery-is-even-better-than-using-a-generator/ | access-date = 9 November 2022 | work = How-To Geek | archive-date = 27 December 2022 | archive-url = https://web.archive.org/web/20221227143745/https://www.howtogeek.com/831697/this-ai-art-gallery-is-even-better-than-using-a-generator/ | url-status = live }}</ref><ref>{{cite news | last1 = Robertson | first1 = Adi | title = Professional AI whisperers have launched a marketplace for DALL-E prompts | url = https://www.theverge.com/2022/9/2/23326868/dalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview | access-date = 9 November 2022 | work = The Verge | date = 2 September 2022 | archive-date = 15 February 2023 | archive-url = https://web.archive.org/web/20230215165523/https://www.theverge.com/2022/9/2/23326868/dalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview | url-status = live }}</ref><ref>{{cite news | title = Text-zu-Bild-Revolution: Stable Diffusion ermöglicht KI-Bildgenerieren für alle | url = https://www.heise.de/news/Text-zu-Bild-Revolution-Stable-Diffusion-ermoeglicht-KI-Bildgenerieren-fuer-alle-7244307.html | access-date = 9 November 2022 | work = heise online | language = de | archive-date = 29 January 2023 | archive-url = https://web.archive.org/web/20230129102135/https://www.heise.de/news/Text-zu-Bild-Revolution-Stable-Diffusion-ermoeglicht-KI-Bildgenerieren-fuer-alle-7244307.html | url-status = live }}</ref> 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.<ref>{{Cite web | url = https://cdn.openart.ai/assets/Stable%20Diffusion%20Prompt%20Book%20From%20OpenArt%2011-13.pdf | title = Stable Diffusion Prompt Book | author = Mohamad Diab, Julian Herrera, Musical Sleep, Bob Chernow, Coco Mao | date = 28 October 2022 | access-date = 7 August 2023 | archive-date = 30 March 2023 | archive-url = https://web.archive.org/web/20230330010200/https://cdn.openart.ai/assets/Stable%20Diffusion%20Prompt%20Book%20From%20OpenArt%2011-13.pdf | url-status = live }}</ref>

==== 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.<ref name="computerworld" /> 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.<ref>{{Cite journal |last1=Corsi |first1=Giulio |last2=Marino |first2=Bill |last3=Wong |first3=Willow |date=3 June 2024 |title=The spread of synthetic media on X |url=https://misinforeview.hks.harvard.edu/article/the-spread-of-synthetic-media-on-x/ |journal=Harvard Kennedy School Misinformation Review |language=en-US |doi=10.37016/mr-2020-140|doi-access=free }}</ref> ''Synthography'' is a proposed term for the practice of generating images that are similar to photographs using AI.<ref>{{cite web | url = https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cjLjVk8AAAAJ&citation_for_view=cjLjVk8AAAAJ:hC7cP41nSMkC | title = Synthography–An Invitation to Reconsider the Rapidly Changing Toolkit of Digital Image Creation as a New Genre Beyond Photography | last = Reinhuber | first = Elke | date = 2 December 2021 | publisher = Google Scholar | access-date = 20 December 2022 | archive-date = 10 February 2023 | archive-url = https://web.archive.org/web/20230210113807/https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cjLjVk8AAAAJ&citation_for_view=cjLjVk8AAAAJ:hC7cP41nSMkC | url-status = live }}</ref>

== Philosophical context == AI-generated visual art has been discussed in relation to questions of creativity, authorship, embodiment, and the status of images. The technology has also renewed debates about photographic and cinematic indexicality, since generative systems can produce images without recording a corresponding physical event in front of a camera.

In his 2026 essay "Manifesto of Reality: Cinema After the Physical Trace", filmmaker and artist Johannes Grenzfurthner argued for "ontological disclosure", distinguishing between physically referential, hybrid, and fully synthetic images.<ref>{{cite web |last=Grenzfurthner |first=Johannes |title=Manifesto of Reality: Cinema After the Physical Trace |website=Midwest Film Journal |date=20 February 2026 |url=https://midwestfilmjournal.com/2026/02/20/manifesto-of-reality-cinema-after-the-physical-trace/ |access-date=21 February 2026}}</ref> Artist and educator Matt Ballou discussed Grenzfurthner's argument in a 2026 talk on AI and pedagogy, relating it to concerns about embodiment, authorship, and the ability to verify whether an image, document, or event has a physical origin.<ref>{{cite web |last=Ballou |first=Matt |title=Reflections on AI and Pedagogy |website=eikonktizo |date=31 May 2026 |url=https://mattballou.com/2026/05/31/reflections-on-ai-and-pedagogy/ |access-date=31 May 2026}}</ref>

== 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.<ref>{{Cite journal | last1 = Cetinic | first1 = Eva | last2 = She | first2 = James | date = 31 May 2022 | title = Understanding and Creating Art with AI: Review and Outlook | url = https://dl.acm.org/doi/10.1145/3475799 | journal = ACM Transactions on Multimedia Computing, Communications, and Applications | language = en | volume = 18 | issue = 2 | pages = 1–22 | arxiv = 2102.09109 | doi = 10.1145/3475799 | issn = 1551-6857 | s2cid = 231951381 | archive-date = 22 June 2023 | access-date = 8 April 2023 | archive-url = https://web.archive.org/web/20230622125504/https://dl.acm.org/doi/10.1145/3475799 | url-status = live }}</ref><ref name="ACM Transactions">{{Cite journal | last1 = Cetinic | first1 = Eva | last2 = She | first2 = James | date = 16 February 2022 | title = Understanding and Creating Art with AI: Review and Outlook | journal = ACM Transactions on Multimedia Computing, Communications, and Applications | volume = 18 | issue = 2 | pages = 66:1–66Kate Vass2 | arxiv = 2102.09109 | doi = 10.1145/3475799 | issn = 1551-6857 | s2cid = 231951381 }}</ref>

Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art.<ref>{{Cite conference | last1 = Lang | first1 = Sabine | last2 = Ommer | first2 = Bjorn |year = 2018 | title = Reflecting on How Artworks Are Processed and Analyzed by Computer Vision: Supplementary Material | url = https://openaccess.thecvf.com/content_eccv_2018_workshops/w13/html/Lang_Reflecting_on_How_Artworks_Are_Processed_and_Analyzed_by_Computer_ECCVW_2018_paper.html | via = Computer Vision Foundation | book-title = Proceedings of the European Conference on Computer Vision (ECCV) Workshops | access-date = 8 January 2023 | archive-date = 16 April 2024 | archive-url = https://web.archive.org/web/20240416035600/https://openaccess.thecvf.com/content_eccv_2018_workshops/w13/html/Lang_Reflecting_on_How_Artworks_Are_Processed_and_Analyzed_by_Computer_ECCVW_2018_paper.html | url-status = live }}</ref> 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.<ref name="ACM Transactions" /> Synthetic images can also be used to train AI algorithms for art authentication and to detect forgeries.<ref>{{Cite journal |last1=Ostmeyer |first1=Johann |last2=Schaerf |first2=Ludovica |last3=Buividovich |first3=Pavel |last4=Charles |first4=Tessa |last5=Postma |first5=Eric |last6=Popovici |first6=Carina |date=14 February 2024 |title=Synthetic images aid the recognition of human-made art forgeries |journal=PLOS ONE |publication-place=United States |volume=19 |issue=2 |article-number=e0295967 |arxiv=2312.14998 |doi=10.1371/journal.pone.0295967 |issn=1932-6203 |pmc=10866502 |pmid=38354162 |doi-access=free|bibcode=2024PLoSO..1995967O }}</ref>

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.<ref>{{Cite arXiv |eprint=2101.07396 |class=cs.CV |first1=Panos |last1=Achlioptas |first2=Maks |last2=Ovsjanikov |title=ArtEmis: Affective Language for Visual Art |date=18 January 2021 |last3=Haydarov |first3=Kilichbek |last4=Elhoseiny |first4=Mohamed |last5=Guibas |first5=Leonidas}}</ref><ref>{{Cite web |last=Myers |first=Andrew |date=22 March 2021 |title=Artist's Intent: AI Recognizes Emotions in Visual Art |url=https://hai.stanford.edu/news/artists-intent-ai-recognizes-emotions-visual-art |access-date=24 November 2024 |website=hai.stanford.edu |language=en |archive-date=15 October 2024 |archive-url=https://web.archive.org/web/20241015061146/https://hai.stanford.edu/news/artists-intent-ai-recognizes-emotions-visual-art |url-status=live }}</ref>

== 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.<ref>{{cite book | last1 = Yannakakis | first1 = Geogios N. | title = Proceedings of the 9th conference on Computing Frontiers | date = 15 May 2012 | isbn = 978-1-4503-1215-8 | pages = 285–292 | chapter = Game AI revisited | doi = 10.1145/2212908.2212954 | s2cid = 4335529 }}</ref><ref>{{cite news | date = 8 May 2018 | title = AI creates new levels for Doom and Super Mario games | work = BBC News | url = https://www.bbc.com/news/technology-44040007 | access-date = 9 November 2022 | archive-date = 12 December 2022 | archive-url = https://web.archive.org/web/20221212130745/https://www.bbc.com/news/technology-44040007 | url-status = live }}</ref> AI has also been used in the literary arts,<ref>{{cite journal | last1 = Katsnelson | first1 = Alla | date = 29 August 2022 | title = Poor English skills? New AIs help researchers to write better | journal = Nature | language = en | volume = 609 | issue = 7925 | pages = 208–209 | bibcode = 2022Natur.609..208K | doi = 10.1038/d41586-022-02767-9 | pmid = 36038730 | s2cid = 251931306 | doi-access = free }}</ref> such as helping with writer's block, inspiration, or rewriting segments.<ref>{{cite web | date = 9 November 2022 | title = KoboldAI/KoboldAI-Client | url = https://github.com/KoboldAI/KoboldAI-Client | access-date = 9 November 2022 | website = GitHub | archive-date = 4 February 2023 | archive-url = https://web.archive.org/web/20230204101157/https://github.com/KoboldAI/KoboldAI-Client | url-status = live }}</ref><ref>{{cite news | last1 = Dzieza | first1 = Josh | date = 20 July 2022 | title = Can AI write good novels? | work = The Verge | url = https://www.theverge.com/c/23194235/ai-fiction-writing-amazon-kindle-sudowrite-jasper | access-date = 16 November 2022 | archive-date = 10 February 2023 | archive-url = https://web.archive.org/web/20230210114137/https://www.theverge.com/c/23194235/ai-fiction-writing-amazon-kindle-sudowrite-jasper | url-status = live }}</ref><ref>{{cite news | title = AI Writing Assistants: A Cure for Writer's Block or Modern-Day Clippy? | language = en | work = PC Magazine | url = https://www.pcmag.com/how-to/ai-writing-assistants-a-cure-for-writers-block-or-modern-day-clippy | access-date = 16 November 2022 | archive-date = 23 January 2023 | archive-url = https://web.archive.org/web/20230123173826/https://www.pcmag.com/how-to/ai-writing-assistants-a-cure-for-writers-block-or-modern-day-clippy | url-status = live }}</ref><ref>{{cite news | last1 = Song | first1 = Victoria | date = 2 November 2022 | title = Google's new prototype AI tool does the writing for you | work = The Verge | url = https://www.theverge.com/2022/11/2/23435258/google-ai-writing-wordcraft-lamda | access-date = 16 November 2022 | archive-date = 7 February 2023 | archive-url = https://web.archive.org/web/20230207035316/https://www.theverge.com/2022/11/2/23435258/google-ai-writing-wordcraft-lamda | url-status = live }}</ref> 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.<ref>{{cite journal | last1 = Sochacki | first1 = Grzegorz | last2 = Abdulali | first2 = Arsen | last3 = Iida | first3 = Fumiya |year = 2022 | title = Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking | journal = Frontiers in Robotics and AI | volume = 9 | article-number = 886074 | doi = 10.3389/frobt.2022.886074 | issn = 2296-9144 | pmc = 9114309 | pmid = 35603082 | doi-access = free }}</ref>

== Use of the term "art" == The usage of the label "art" when it applies to works generated by AI software has led to debate among artists, philosophers, scholars, and others. Various observers argue that referring to machine generated images as "art" undermines the traditional characteristics of human artistry, such as creativity, skill, and intentionality. Present-day definitions of true artistic creation often put an emphasis on the requirement of human-level intentions, personal experience and emotion, as well as historical and/or artistic context.<ref>{{Cite journal |last=Coeckelbergh |first=Mark |date=1 September 2017 |title=Can Machines Create Art? |url=https://doi.org/10.1007/s13347-016-0231-5 |journal=Philosophy & Technology |language=en |volume=30 |issue=3 |pages=285–303 |doi=10.1007/s13347-016-0231-5 |issn=2210-5441|hdl=2086/12670 |hdl-access=free }}</ref>

According to a research study from the National Library of Medicine, humans inherently show a bias against artwork described as being AI-generated. When participants of the study were shown two comparable images, with only one presented as having been generated by AI, subjects were more likely to rate the one described as being artificially generated lower in artistic value. This suggests that social and cultural attitudes can shape the determination of whether an image is considered art, regardless of the image's other visual features.<ref>{{Cite journal |last1=Horton |first1=C. Blaine |last2=White |first2=Michael W. |last3=Iyengar |first3=Sheena S. |date=3 November 2023 |title=Bias against AI art can enhance perceptions of human creativity |journal=Scientific Reports |volume=13 |issue=1 |pages=19001 |doi=10.1038/s41598-023-45202-3 |issn=2045-2322 |pmc=10624838 |pmid=37923764 |bibcode=2023NatSR..1319001H }}</ref>

In a 2023 report submitted to the ''Annual Convention of Digital Art Observers'', Samuel Loomis wrote that the term "AI art" acknowledges its dual nature as a product of human guidance and machine-driven generative systems, when evaluating it by the same critical standards applied to traditional art.<ref>Jonathan Doe: "A Summary and Analysis of Contemporary Digital Media Trends", published in ''Die Zeitung'' (February 2024)</ref>

==See also== {{Columns-list|* Algorithmic art * AI slop * {{section link|Applications of artificial intelligence|Art}} * {{annotated link|Artificial intelligence and elections}} * Artificial intelligence controversies * Artificial intelligence in architecture * Computational creativity * Cybernetic art * Deepfakes * Generative art * List of artificial intelligence artists * Music and artificial intelligence * Neural style transfer * Synthetic media}}

==References== {{reflist}}

{{Digital art}} {{Western art movements}}

Category:Artificial intelligence visual art<!--please leave the empty space as standard--> Category:20th-century introductions Category:20th-century art movements Category:Generative AI Category:Digital art Category:Computer art Category:Art controversies Category:Works involved in plagiarism controversies Category:Articles containing video clips