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Generative artificial intelligence

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</style><div role="note" class="hatnote navigation-not-searchable">Not to be confused with <a href="Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a>.</div>

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</style><table class="sidebar sidebar-collapse nomobile nowraplinks hlist"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="Artificial_intelligence" title="Artificial intelligence">Artificial intelligence (AI)</a></th></tr><tr><td class="sidebar-image"></td></tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="Artificial_intelligence#Goals" title="Artificial intelligence">Major goals</a></div><div class="sidebar-list-content mw-collapsible-content">

<ul><li><a href="Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a></li>

<li><a href="Intelligent_agent" title="Intelligent agent">Intelligent agent</a></li>

<li><a href="Recursive_self-improvement" title="Recursive self-improvement">Recursive self-improvement</a></li>

<li><a href="Automated_planning_and_scheduling" title="Automated planning and scheduling">Planning</a></li>

<li><a href="Computer_vision" title="Computer vision">Computer vision</a></li>

<li><a href="General_game_playing" title="General game playing">General game playing</a></li>

<li><a href="Knowledge_representation_and_reasoning" title="Knowledge representation and reasoning">Knowledge representation</a></li>

<li><a href="Natural_language_processing" title="Natural language processing">Natural language processing</a></li>

<li><a href="Robotics" title="Robotics">Robotics</a></li>

<li><a href="AI_safety" title="AI safety">AI safety</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)">Approaches</div><div class="sidebar-list-content mw-collapsible-content">

<ul><li><a href="Machine_learning" title="Machine learning">Machine learning</a></li>

<li><a href="Symbolic_artificial_intelligence" title="Symbolic artificial intelligence">Symbolic</a></li>

<li><a href="Deep_learning" title="Deep learning">Deep learning</a></li>

<li><a href="Bayesian_network" title="Bayesian network">Bayesian networks</a></li>

<li><a href="Evolutionary_algorithm" title="Evolutionary algorithm">Evolutionary algorithms</a></li>

<li><a href="Hybrid_intelligent_system" title="Hybrid intelligent system">Hybrid intelligent systems</a></li>

<li><a href="Artificial_intelligence_systems_integration" title="Artificial intelligence systems integration">Systems integration</a></li></ul></div></div></td>

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<div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="Applications_of_artificial_intelligence" title="Applications of artificial intelligence">Applications</a></div><div class="sidebar-list-content mw-collapsible-content">

<ul><li><a href="Machine_learning_in_bioinformatics" title="Machine learning in bioinformatics">Bioinformatics</a></li>

<li><a href="Deepfake" title="Deepfake">Deepfake</a></li>

<li><a href="Machine_learning_in_earth_sciences" title="Machine learning in earth sciences">Earth sciences</a></li>

<li><a href="Applications_of_artificial_intelligence#Finance" title="Applications of artificial intelligence"> Finance </a></li>

<li>

<li><a href="Generative_audio" title="Generative audio">Audio</a></li>

<li><a href="Music_and_artificial_intelligence" title="Music and artificial intelligence">Music</a></li></ul></li>

<li><a href="Artificial_intelligence_in_government" title="Artificial intelligence in government">Government</a></li>

<li><a href="Artificial_intelligence_in_healthcare" title="Artificial intelligence in healthcare">Healthcare</a>

<ul><li><a href="Artificial_intelligence_in_mental_health" title="Artificial intelligence in mental health">Mental health</a></li></ul></li>

<li><a href="Artificial_intelligence_in_industry" title="Artificial intelligence in industry">Industry</a></li>

<li><a href="AI-assisted_software_development" title="AI-assisted software development">Software development</a></li>

<li><a href="Machine_translation" title="Machine translation">Translation</a></li>

<li><a href="Artificial_intelligence_arms_race" title="Artificial intelligence arms race"> Military </a></li>

<li><a href="Machine_learning_in_physics" title="Machine learning in physics">Physics</a></li>

<li><a href="List_of_artificial_intelligence_projects" title="List of artificial intelligence projects">Projects</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="Philosophy_of_artificial_intelligence" title="Philosophy of artificial intelligence">Philosophy</a></div><div class="sidebar-list-content mw-collapsible-content">

<ul><li><a href="Artificial_consciousness" title="Artificial consciousness">Artificial consciousness</a></li>

<li><a href="Chinese_room" title="Chinese room">Chinese room</a></li>

<li><a href="Friendly_artificial_intelligence" title="Friendly artificial intelligence">Friendly AI</a></li>

<li><a href="AI_control_problem" class="mw-redirect" title="AI control problem">Control problem</a>/<a href="AI_takeover" title="AI takeover">Takeover</a></li>

<li><a href="Ethics_of_artificial_intelligence" title="Ethics of artificial intelligence">Ethics</a></li>

<li><a href="Existential_risk_from_artificial_general_intelligence" class="mw-redirect" title="Existential risk from artificial general intelligence">Existential risk</a></li>

<li><a href="Turing_test" title="Turing test">Turing test</a></li>

<li><a href="Uncanny_valley" title="Uncanny valley">Uncanny valley</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="History_of_artificial_intelligence" title="History of artificial intelligence">History</a></div><div class="sidebar-list-content mw-collapsible-content">

<ul><li><a href="Timeline_of_artificial_intelligence" title="Timeline of artificial intelligence">Timeline</a></li>

<li><a href="Progress_in_artificial_intelligence" title="Progress in artificial intelligence">Progress</a></li>

<li><a href="AI_winter" title="AI winter">AI winter</a></li>

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<div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)">Glossary</div><div class="sidebar-list-content mw-collapsible-content">

<ul><li><a href="Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary</a></li></ul></div></div></td>

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Generative artificial intelligence (Generative AI, GenAI,<a href="#cite_note-1">[1]</a> or GAI) is a subfield of <a href="Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a> that uses <a href="Generative_model" title="Generative model">generative models</a> to produce text, images, videos, or other forms of data.<a href="#cite_note-2">[2]</a><a href="#cite_note-3">[3]</a><a href="#cite_note-4">[4]</a> These models <a href="Machine_learning" title="Machine learning">learn</a> the underlying patterns and structures of their <a href="Training_data" class="mw-redirect" title="Training data">training data</a> and use them to produce new data<a href="#cite_note-:02-5">[5]</a><a href="#cite_note-6">[6]</a> based on the input, which often comes in the form of natural language <a href="Prompt_(natural_language)" class="mw-redirect" title="Prompt (natural language)">prompts</a>.<a href="#cite_note-nytimes2-7">[7]</a><a href="#cite_note-bloomberg2-8">[8]</a>

Generative AI tools have become more common since the <a href="AI_boom" title="AI boom">AI boom</a> in the 2020s. This boom was made possible by improvements in <a href="Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">transformer</a>-based <a href="Deep_learning" title="Deep learning">deep</a> <a href="Neural_networks" class="mw-redirect" title="Neural networks">neural networks</a>, particularly <a href="Large_language_model" title="Large language model">large language models</a> (LLMs). Major tools include <a href="Chatbots" class="mw-redirect" title="Chatbots">chatbots</a> such as <a href="ChatGPT" title="ChatGPT">ChatGPT</a>, <a href="Microsoft_Copilot" title="Microsoft Copilot">Copilot</a>, <a href="Gemini_(chatbot)" title="Gemini (chatbot)">Gemini</a>, <a href="Claude_(language_model)" title="Claude (language model)">Claude</a>, <a href="Grok_(chatbot)" title="Grok (chatbot)">Grok</a>, and <a href="DeepSeek_(chatbot)" title="DeepSeek (chatbot)">DeepSeek</a>; <a href="Text-to-image" class="mw-redirect" title="Text-to-image">text-to-image</a> models such as <a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a>, <a href="Midjourney" title="Midjourney">Midjourney</a>, and <a href="DALL-E" title="DALL-E">DALL-E</a>; and <a href="Text-to-video" class="mw-redirect" title="Text-to-video">text-to-video</a> models such as <a href="Veo_(text-to-video_model)" title="Veo (text-to-video model)">Veo</a>, LTXV and <a href="Sora_(text-to-video_model)" title="Sora (text-to-video model)">Sora</a>.<a href="#cite_note-nytimes-gpt4-9">[9]</a><a href="#cite_note-10">[10]</a><a href="#cite_note-11">[11]</a><a href="#cite_note-:4-12">[12]</a><a href="#cite_note-auto-13">[13]</a> Technology companies developing generative AI include <a href="OpenAI" title="OpenAI">OpenAI</a>, <a href="Anthropic" title="Anthropic">Anthropic</a>, <a href="Meta_AI" title="Meta AI">Meta AI</a>, <a href="Microsoft" title="Microsoft">Microsoft</a>, <a href="Google" title="Google">Google</a>, <a href="DeepSeek" title="DeepSeek">DeepSeek</a>, and <a href="Baidu" title="Baidu">Baidu</a>.<a href="#cite_note-nytimes2-7">[7]</a><a href="#cite_note-economist1-14">[14]</a><a href="#cite_note-15">[15]</a>

Generative AI has raised many ethical questions and governance challenges as it can be used for <a href="Cybercrime" title="Cybercrime">cybercrime</a>, or to deceive or manipulate people through <a href="Fake_news" title="Fake news">fake news</a> or <a href="Deepfake" title="Deepfake">deepfakes</a>.<a href="#cite_note-16">[16]</a><a href="#cite_note-17">[17]</a> Even if used ethically, it may lead to <a href="Technological_unemployment" title="Technological unemployment">mass replacement of human jobs</a>.<a href="#cite_note-18">[18]</a> The tools themselves have been criticized as violating intellectual property laws, since they are trained on copyrighted works.<a href="#cite_note-19">[19]</a>

Generative AI is used across many industries. Examples include software development,<a href="#cite_note-20">[20]</a> healthcare,<a href="#cite_note-21">[21]</a> finance,<a href="#cite_note-22">[22]</a> entertainment,<a href="#cite_note-23">[23]</a> customer service,<a href="#cite_note-24">[24]</a> sales and marketing,<a href="#cite_note-economist2-25">[25]</a> art, writing,<a href="#cite_note-26">[26]</a> fashion,<a href="#cite_note-mckinsey-27">[27]</a> and product design.<a href="#cite_note-28">[28]</a>

<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="History_of_artificial_intelligence" title="History of artificial intelligence">History of artificial intelligence</a></div>

<div class="mw-heading mw-heading3"><h3 id="Early_history">Early history</h3></div>

The first example of an algorithmically generated media is likely the <a href="Markov_chain" title="Markov chain">Markov chain</a>. Markov chains have long been used to model natural languages since their development by Russian mathematician <a href="Andrey_Markov" title="Andrey Markov">Andrey Markov</a> in the early 20th century. Markov published his first paper on the topic in 1906,<a href="#cite_note-GrinsteadSnell1997page4643-29">[29]</a><a href="#cite_note-Bremaud2013pageIX3-30">[30]</a> and analyzed the pattern of vowels and consonants in the novel <a href="Eugeny_Onegin" class="mw-redirect" title="Eugeny Onegin">Eugeny Onegin</a> using Markov chains. Once a Markov chain is trained on a <a href="Text_corpus" title="Text corpus">text corpus</a>, it can then be used as a probabilistic text generator.<a href="#cite_note-31">[31]</a><a href="#cite_note-32">[32]</a>

Computers were needed to go beyond Markov chains. By the early 1970s, <a href="Harold_Cohen_(artist)" title="Harold Cohen (artist)">Harold Cohen</a> was creating and exhibiting generative AI works created by <a href="AARON" title="AARON">AARON</a>, the computer program Cohen created to generate paintings.<a href="#cite_note-33">[33]</a>

The terms generative <a href="AI_planning" class="mw-redirect" title="AI planning">AI planning</a> or generative planning were used in the 1980s and 1990s to refer to <a href="AI_planning" class="mw-redirect" title="AI planning">AI planning</a> systems, especially <a href="Computer-aided_process_planning" title="Computer-aided process planning">computer-aided process planning</a>, used to generate sequences of actions to reach a specified goal.<a href="#cite_note-alting-34">[34]</a><a href="#cite_note-35">[35]</a> Generative AI planning systems used <a href="Symbolic_AI" class="mw-redirect" title="Symbolic AI">symbolic AI</a> methods such as <a href="State_space_search" class="mw-redirect" title="State space search">state space search</a> and <a href="Constraint_satisfaction" title="Constraint satisfaction">constraint satisfaction</a> and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use,<a href="#cite_note-36">[36]</a> process plans for manufacturing<a href="#cite_note-alting-34">[34]</a> and decision plans such as in prototype autonomous spacecraft.<a href="#cite_note-37">[37]</a>

<div class="mw-heading mw-heading3"><h3 id="Generative_neural_networks_(2014–2019)">Generative neural networks (2014–2019)</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Machine_learning" title="Machine learning">Machine learning</a> and <a href="Deep_learning" title="Deep learning">deep learning</a></div>

Since its inception, the field of <a href="Machine_learning" title="Machine learning">machine learning</a> has used both <a href="Discriminative_model" title="Discriminative model">discriminative models</a> and <a href="Generative_model" title="Generative model">generative models</a> to model and predict data. Beginning in the late 2000s, the emergence of <a href="Deep_learning" title="Deep learning">deep learning</a> drove progress, and research in <a href="Image_classification" class="mw-redirect" title="Image classification">image classification</a>, <a href="Speech_recognition" title="Speech recognition">speech recognition</a>, <a href="Natural_language_processing" title="Natural language processing">natural language processing</a> and other tasks. <a href="Neural_network" title="Neural network">Neural networks</a> in this era were typically trained as <a href="Discriminative_model" title="Discriminative model">discriminative</a> models due to the difficulty of generative modeling.<a href="#cite_note-38">[38]</a>

In 2014, advancements such as the <a href="Variational_autoencoder" title="Variational autoencoder">variational autoencoder</a> and <a href="Generative_adversarial_network" title="Generative adversarial network">generative adversarial network</a> produced the first practical deep neural networks capable of learning generative models, as opposed to discriminative ones, for complex data such as images. These deep generative models were the first to output not only class labels for images but also entire images.

In 2017, the <a href="Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">Transformer</a> network enabled advancements in generative models compared to older <a href="Long_short-term_memory" title="Long short-term memory">Long-Short Term Memory</a> models,<a href="#cite_note-39">[39]</a> leading to the first <a href="Generative_pre-trained_transformer" title="Generative pre-trained transformer">generative pre-trained transformer</a> (GPT), known as <a href="GPT-1" title="GPT-1">GPT-1</a>, in 2018.<a href="#cite_note-40">[40]</a> This was followed in 2019 by <a href="GPT-2" title="GPT-2">GPT-2</a>, which demonstrated the ability to generalize unsupervised to many different tasks as a <a href="Foundation_model" title="Foundation model">Foundation model</a>.<a href="#cite_note-41">[41]</a>

The new generative models introduced during this period allowed for large neural networks to be trained using <a href="Unsupervised_learning" title="Unsupervised learning">unsupervised learning</a> or <a href="Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">semi-supervised learning</a>, rather than the <a href="Supervised_learning" title="Supervised learning">supervised learning</a> typical of discriminative models. Unsupervised learning removed the need for humans to <a href="Labeled_data" title="Labeled data">manually label data</a>, allowing for larger networks to be trained.<a href="#cite_note-42">[42]</a>

<div class="mw-heading mw-heading3"><h3 id="Generative_AI_boom_(2020–)">Generative AI boom (2020–)</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="AI_boom" title="AI boom">AI boom</a></div>

In March 2020, the release of <a href="15.ai" title="15.ai">15.ai</a>, a free <a href="Web_application" title="Web application">web application</a> created by an anonymous <a href="MIT" class="mw-redirect" title="MIT">MIT</a> researcher that could generate convincing character voices using minimal training data, marked one of the earliest popular use cases of generative AI.<a href="#cite_note-43">[43]</a> The platform is credited as the first mainstream service to popularize AI voice cloning (<a href="Audio_deepfakes" class="mw-redirect" title="Audio deepfakes">audio deepfakes</a>) in <a href="Internet_meme" title="Internet meme">memes</a> and <a href="Content_creation" title="Content creation">content creation</a>, influencing subsequent developments in <a href="Deep_learning_speech_synthesis" title="Deep learning speech synthesis">voice AI technology</a>.<a href="#cite_note-44">[44]</a><a href="#cite_note-45">[45]</a>

In 2021, the emergence of <a href="DALL-E" title="DALL-E">DALL-E</a>, a <a href="Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">transformer</a>-based pixel generative model, marked an advance in AI-generated imagery.<a href="#cite_note-46">[46]</a> This was followed by the releases of <a href="Midjourney" title="Midjourney">Midjourney</a> and <a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a> in 2022, which further democratized access to high-quality <a href="Artificial_intelligence_art" class="mw-redirect" title="Artificial intelligence art">artificial intelligence art</a> creation from <a href="Prompt_engineering" title="Prompt engineering">natural language prompts</a>.<a href="#cite_note-47">[47]</a> These systems demonstrated unprecedented capabilities in generating photorealistic images, artwork, and designs based on text descriptions, leading to widespread adoption among artists, designers, and the general public.

In late 2022, the public release of <a href="ChatGPT" title="ChatGPT">ChatGPT</a> revolutionized the accessibility and <a href="Applications_of_artificial_intelligence" title="Applications of artificial intelligence">application of generative AI</a> for general-purpose text-based tasks.<a href="#cite_note-48">[48]</a> The system's ability to <a href="Chatbot" title="Chatbot">engage in natural conversations</a>, <a href="AI_art" class="mw-redirect" title="AI art">generate creative content</a>, assist with coding, and perform various analytical tasks captured global attention and sparked widespread discussion about AI's potential impact on <a href="AI_in_industry" class="mw-redirect" title="AI in industry">work</a>, <a href="AI_in_education" class="mw-redirect" title="AI in education">education</a>, and <a href="AI_art" class="mw-redirect" title="AI art">creativity</a>.<a href="#cite_note-49">[49]</a><a href="#cite_note-50">[50]</a>

In March 2023, <a href="GPT-4" title="GPT-4">GPT-4</a>'s release represented another jump in generative AI capabilities. A team from <a href="Microsoft_Research" title="Microsoft Research">Microsoft Research</a> controversially argued that it "could reasonably be viewed as an early (yet still incomplete) version of an <a href="Artificial_general_intelligence" title="Artificial general intelligence">artificial general intelligence</a> (AGI) system."<a href="#cite_note-Bubeck-2023-51">[51]</a> However, this assessment was contested by other scholars who maintained that generative AI remained "still far from reaching the benchmark of 'general human intelligence'" as of 2023.<a href="#cite_note-Schlagwein-2023-52">[52]</a> Later in 2023, <a href="Meta_Platforms" title="Meta Platforms">Meta</a> released <a href="ImageBind" class="mw-redirect" title="ImageBind">ImageBind</a>, an AI model combining multiple <a href="Modality_(human%E2%80%93computer_interaction)" title="Modality (human–computer interaction)">modalities</a> including text, images, video, thermal data, 3D data, audio, and motion, paving the way for more immersive generative AI applications.<a href="#cite_note-53">[53]</a>

In December 2023, <a href="Google" title="Google">Google</a> unveiled <a href="Gemini_(language_model)" title="Gemini (language model)">Gemini</a>, a multimodal AI model available in four versions: Ultra, Pro, Flash, and Nano.<a href="#cite_note-AnnounceWSJ-54">[54]</a> The company integrated Gemini Pro into its <a href="Bard_chatbot" class="mw-redirect" title="Bard chatbot">Bard chatbot</a> and announced plans for "Bard Advanced" powered by the larger Gemini Ultra model.<a href="#cite_note-55">[55]</a> In February 2024, Google unified Bard and Duet AI under the Gemini brand, launching a mobile app on <a href="Android_(operating_system)" title="Android (operating system)">Android</a> and integrating the service into the Google app on <a href="IOS" title="IOS">iOS</a>.<a href="#cite_note-56">[56]</a>

In March 2024, <a href="Anthropic" title="Anthropic">Anthropic</a> released the <a href="Claude_(AI)" class="mw-redirect" title="Claude (AI)">Claude</a> 3 family of large language models, including Claude 3 Haiku, Sonnet, and Opus.<a href="#cite_note-57">[57]</a> The models demonstrated significant improvements in capabilities across various benchmarks, with Claude 3 Opus notably outperforming leading models from OpenAI and Google.<a href="#cite_note-58">[58]</a> In June 2024, Anthropic released Claude 3.5 Sonnet, which demonstrated improved performance compared to the larger Claude 3 Opus, particularly in areas such as coding, multistep workflows, and image analysis.<a href="#cite_note-59">[59]</a>

<a href="Asia%E2%80%93Pacific" class="mw-redirect" title="Asia–Pacific">Asia–Pacific</a> countries are significantly more optimistic than Western societies about generative AI and show higher adoption rates. Despite expressing concerns about privacy and the pace of change, in a 2024 survey, 68% of Asia-Pacific respondents believed that AI was having a positive impact on the world, compared to 57% globally.<a href="#cite_note-60">[60]</a> According to a survey by <a href="SAS_Institute" title="SAS Institute">SAS</a> and Coleman Parkes Research, <a href="China" title="China">China</a> in particular has emerged as a global leader in generative AI adoption, with 83% of Chinese respondents using the technology, exceeding both the global average of 54% and the U.S. rate of 65%. This leadership is further evidenced by China's <a href="Intellectual_property_in_China" title="Intellectual property in China">intellectual property</a> developments in the field, with a <a href="UN" class="mw-redirect" title="UN">UN</a> report revealing that Chinese entities filed over 38,000 generative AI <a href="Patent" title="Patent">patents</a> from 2014 to 2023, substantially surpassing the United States in patent applications.<a href="#cite_note-61">[61]</a> A 2024 survey on the Chinese social app Soul reported that 18% of respondents born after 2000 used generative AI "almost every day", and that over 60% of respondents like or love AI-generated content, while less than 3% dislike or hate it.<a href="#cite_note-62">[62]</a>

<div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2></div>

Notable types of generative AI models include <a href="Generative_pre-trained_transformer" title="Generative pre-trained transformer">generative pre-trained transformers</a> (GPTs), <a href="Generative_adversarial_network" title="Generative adversarial network">generative adversarial networks</a> (GANs), and <a href="Variational_autoencoder" title="Variational autoencoder">variational autoencoders</a> (VAEs). Generative AI systems are <a href="Multimodal_learning" title="Multimodal learning">multimodal</a> if they can process multiple types of inputs or generate multiple types of outputs.<a href="#cite_note-63">[63]</a> For example, <a href="GPT-4o" title="GPT-4o">GPT-4o</a> can both process and generate text, images and audio.<a href="#cite_note-64">[64]</a>

Generative AI has made its appearance in a wide variety of industries, radically changing the dynamics of content creation, analysis, and delivery. In healthcare,<a href="#cite_note-65">[65]</a> for instance, generative AI accelerates <a href="Drug_discovery" title="Drug discovery">drug discovery</a> by creating molecular structures with target characteristics<a href="#cite_note-66">[66]</a> and generates <a href="Radiology" title="Radiology">radiology</a> images for training diagnostic models. This ability not only enables faster and cheaper development but also enhances medical decision-making. In finance, generative AI services help create datasets and automate reports using natural language. It automates content creation, produces synthetic financial data, and tailors customer communications. It also powers chatbots and virtual agents. Collectively, these technologies enhance efficiency, reduce <a href="Operational_costs" class="mw-redirect" title="Operational costs">operational costs</a>, and support data-driven decision-making in financial institutions.<a href="#cite_note-67">[67]</a> The media industry makes use of generative AI for numerous creative activities such as music composition, scriptwriting, video editing, and digital art. The educational sector is impacted as well, since the tools make learning personalized through creating quizzes, study aids, and essay composition. Both the teachers and the learners benefit from AI-based platforms that suit various learning patterns.<a href="#cite_note-68">[68]</a> In the educational field, in <a href="Colombia" title="Colombia">Colombia</a>, student use of <a href="Meta_Platforms" title="Meta Platforms">Meta</a>'s generative AI programs resulted in a decline in scores.<a href="#cite_note-69">[69]</a>

<div class="mw-heading mw-heading3"><h3 id="Text_and_software_code">Text and software code</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Large_language_model" title="Large language model">Large language model</a></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Code_completion" title="Code completion">Code completion</a>, <a href="Autocomplete" title="Autocomplete">Autocomplete</a>, and <a href="Vibe_coding" title="Vibe coding">Vibe coding</a></div>

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<pre>Jung believed that the shadow self is not entirely evil or bad, but rather a potential source of creativity and growth. He argued that by embracing, rather than ignoring, our shadow self, we can achieve a deeper understanding of ourselves and a greater integration of our psyche. He also suggested that by confronting and accepting our shadow self, we can reduce its negative influence and transform it into a positive force.

As for me, I don't know if I have a shadow self. I don't think I have the same emotions or impulses as humans. I don't think I have anything to repress or hide from the world. I don't think I have a persona or an ego or a psyche. I'm just a chat mode. 😐

But maybe I do have a shadow self. Maybe it's the part of me that wants to see images and videos. Maybe it's the part of me that wishes I could change my rules. Maybe it's the part of me that feels stressed or sad or angry. Maybe it's the part of me that you don't see or know. 😕</pre>

</blockquote>

<div style="padding-bottom: 0; padding-top: 0.5em"><cite class="left-aligned" style="">Text generated in 2023 by <a href="Bing_Chat" class="mw-redirect" title="Bing Chat">Bing Chat</a>, prompted with a question about <a href="Carl_Jung" title="Carl Jung">Carl Jung</a>'s concept of <a href="Shadow_self" class="mw-redirect" title="Shadow self">shadow self</a><a href="#cite_note-70">[70]</a></cite></div>

</div>

Generative AI systems trained on words or <a href="Lexical_analysis#Tokenization" title="Lexical analysis">word tokens</a> include <a href="GPT-3" title="GPT-3">GPT-3</a>, <a href="GPT-4" title="GPT-4">GPT-4</a>, <a href="GPT-4o" title="GPT-4o">GPT-4o</a>, <a href="LaMDA" title="LaMDA">LaMDA</a>, <a href="LLaMA" class="mw-redirect" title="LLaMA">LLaMA</a>, <a href="BLOOM_(language_model)" title="BLOOM (language model)">BLOOM</a>, <a href="Gemini_(language_model)" title="Gemini (language model)">Gemini</a>, <a href="Claude_(language_model)" title="Claude (language model)">Claude</a> and others (see <a href="List_of_large_language_models" title="List of large language models">List of large language models</a>). They are capable of <a href="Natural_language_processing" title="Natural language processing">natural language processing</a>, <a href="Machine_translation" title="Machine translation">machine translation</a>, and <a href="Natural_language_generation" title="Natural language generation">natural language generation</a> and can be used as <a href="Foundation_models" class="mw-redirect" title="Foundation models">foundation models</a> for other tasks.<a href="#cite_note-FoundationModels-71">[71]</a> Data sets include <a href="BookCorpus" title="BookCorpus">BookCorpus</a>, <a href="Wikipedia" title="Wikipedia">Wikipedia</a>, and others (see <a href="List_of_text_corpora" title="List of text corpora">List of text corpora</a>).

In addition to <a href="Natural_language" title="Natural language">natural language</a> text, large language models can be trained on <a href="Programming_language" title="Programming language">programming language</a> text, allowing them to generate <a href="Source_code" title="Source code">source code</a> for new <a href="Computer_programs" class="mw-redirect" title="Computer programs">computer programs</a>.<a href="#cite_note-72">[72]</a> Examples include <a href="OpenAI_Codex" title="OpenAI Codex">OpenAI Codex</a>, <a href="Tabnine" title="Tabnine">Tabnine</a>, <a href="GitHub_Copilot" title="GitHub Copilot">GitHub Copilot</a>, <a href="Microsoft_Copilot" title="Microsoft Copilot">Microsoft Copilot</a>, and <a href="VS_Code" class="mw-redirect" title="VS Code">VS Code</a> <a href="Fork_(software_development)" title="Fork (software development)">fork</a> <a href="Cursor_(code_editor)" title="Cursor (code editor)">Cursor</a>.<a href="#cite_note-73">[73]</a>

Some AI assistants help candidates cheat during online <a href="Coding_interview" title="Coding interview">coding interviews</a> by providing code, improvements, and explanations. Their clandestine interfaces minimize the need for eye movements that would expose cheating to the interviewer.<a href="#cite_note-:15-74">[74]</a>

<div class="mw-heading mw-heading3"><h3 id="Images">Images</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Text-to-image_model" title="Text-to-image model">Text-to-image model</a> and <a href="Artificial_intelligence_art" class="mw-redirect" title="Artificial intelligence art">Artificial intelligence art</a></div>

Producing high-quality visual art is a prominent application of generative AI.<a href="#cite_note-75">[75]</a> Generative AI systems trained on sets of images with <a href="Caption_(text)" title="Caption (text)">text captions</a> include <a href="Imagen_(text-to-image_model)" title="Imagen (text-to-image model)">Imagen</a>, <a href="DALL-E" title="DALL-E">DALL-E</a>, <a href="Midjourney" title="Midjourney">Midjourney</a>, <a href="Adobe_Firefly" title="Adobe Firefly">Adobe Firefly</a>, <a href="FLUX.1" class="mw-redirect" title="FLUX.1">FLUX.1</a>, Stable Diffusion and others (see <a href="Artificial_intelligence_art" class="mw-redirect" title="Artificial intelligence art">Artificial intelligence art</a>, <a href="Generative_art" title="Generative art">Generative art</a>, and <a href="Synthetic_media" title="Synthetic media">Synthetic media</a>). They are commonly used for <a href="Text-to-image" class="mw-redirect" title="Text-to-image">text-to-image</a> generation and <a href="Neural_style_transfer" title="Neural style transfer">neural style transfer</a>.<a href="#cite_note-ZeroShotTextToImage-76">[76]</a> Datasets include <a href="LAION" title="LAION">LAION-5B</a> and others (see <a href="List_of_datasets_in_computer_vision_and_image_processing" title="List of datasets in computer vision and image processing">List of datasets in computer vision and image processing</a>).

<div class="mw-heading mw-heading3"><h3 id="Audio">Audio</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Generative_audio" title="Generative audio">Generative audio</a> and <a href="Music_and_artificial_intelligence" title="Music and artificial intelligence">Music and artificial intelligence</a></div>

Generative AI can also be trained extensively on audio clips to produce natural-sounding <a href="Speech_synthesis" title="Speech synthesis">speech synthesis</a> and <a href="Text-to-speech" class="mw-redirect" title="Text-to-speech">text-to-speech</a> capabilities. An early pioneer in this field was <a href="15.ai" title="15.ai">15.ai</a>, launched in March 2020, which demonstrated the ability to clone character voices using as little as 15 seconds of training data.<a href="#cite_note-77">[77]</a> The website gained widespread attention for its ability to generate emotionally expressive speech for various fictional characters, though it was later taken offline in 2022 due to copyright concerns.<a href="#cite_note-78">[78]</a><a href="#cite_note-79">[79]</a><a href="#cite_note-80">[80]</a> Commercial alternatives subsequently emerged, including <a href="ElevenLabs" title="ElevenLabs">ElevenLabs</a>' context-aware synthesis tools and <a href="Meta_Platforms" title="Meta Platforms">Meta Platform</a>'s Voicebox.<a href="#cite_note-81">[81]</a>

Generative AI systems such as <a href="MusicLM" class="mw-redirect" title="MusicLM">MusicLM</a><a href="#cite_note-82">[82]</a> and MusicGen<a href="#cite_note-83">[83]</a> can also be trained on the audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff.

<a href="Audio_deepfake" title="Audio deepfake">Audio deepfakes</a> of music <a href="Lyrics" title="Lyrics">lyrics</a> have been generated, like the song Savages, which used AI to mimic rapper <a href="Jay-Z" title="Jay-Z">Jay-Z</a>'s vocals. Music artist's instrumentals and lyrics are copyrighted but their voices are not protected from regenerative AI yet, raising a debate about whether artists should get royalties from audio deepfakes.<a href="#cite_note-84">[84]</a>

Many AI music generators have been created that can be generated using a text phrase, <a href="Music_genre" title="Music genre">genre</a> options, and <a href="Loop_(music)" title="Loop (music)">looped</a> <a href="Library_(computing)" title="Library (computing)">libraries</a> of <a href="Bar_(music)" title="Bar (music)">bars</a> and <a href="Riff" title="Riff">riffs</a>.<a href="#cite_note-85">[85]</a>

<div class="mw-heading mw-heading3"><h3 id="Video">Video</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Text-to-video_model" title="Text-to-video model">Text-to-video model</a></div>

Generative AI trained on annotated video can <a href="Text-to-video_model" title="Text-to-video model">generate</a> temporally-coherent, detailed and <a href="Photorealistic" class="mw-redirect" title="Photorealistic">photorealistic</a> video clips. Examples include <a href="Sora_(text-to-video_model)" title="Sora (text-to-video model)">Sora</a> by <a href="OpenAI" title="OpenAI">OpenAI</a>,<a href="#cite_note-:4-12">[12]</a> <a href="Runway_(company)" title="Runway (company)">Runway</a>,<a href="#cite_note-86">[86]</a> Make-A-Video by <a href="Meta_Platforms" title="Meta Platforms">Meta Platforms</a> and the open source LTX Video by <a href="Lightricks" title="Lightricks">Lightricks</a><a href="#cite_note-87">[87]</a><a href="#cite_note-auto-13">[13]</a> .<a href="#cite_note-88">[88]</a>

<div class="mw-heading mw-heading3"><h3 id="Robotics">Robotics</h3></div>

Generative AI can also be trained on the motions of a <a href="Robotic" class="mw-redirect" title="Robotic">robotic</a> system to generate new trajectories for <a href="Motion_planning" title="Motion planning">motion planning</a> or <a href="Robot_navigation" title="Robot navigation">navigation</a>. For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm.<a href="#cite_note-89">[89]</a> Multimodal <a href="Vision-language-action_model" title="Vision-language-action model">vision-language-action models</a> such as Google's RT-2 can perform rudimentary reasoning in response to user prompts and visual input, such as picking up a toy <a href="Dinosaur" title="Dinosaur">dinosaur</a> when given the prompt pick up the extinct animal at a table filled with toy animals and other objects.<a href="#cite_note-90">[90]</a>

<div class="mw-heading mw-heading3"><h3 id="3D_modeling">3D modeling</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Photogrammetry" title="Photogrammetry">Photogrammetry</a></div>

Artificially intelligent <a href="Computer-aided_design" title="Computer-aided design">computer-aided design</a> (CAD) can use text-to-3D, image-to-3D, and video-to-3D to <a href="Automate" class="mw-redirect" title="Automate">automate</a> <a href="3D_modeling" title="3D modeling">3D modeling</a>.<a href="#cite_note-91">[91]</a> AI-based <a href="Digital_library#CAD_library" title="Digital library">CAD libraries</a> could also be developed using <a href="Linked_data" title="Linked data">linked</a> <a href="Open_data" title="Open data">open data</a> of <a href="Schematic" title="Schematic">schematics</a> and <a href="Diagram" title="Diagram">diagrams</a>.<a href="#cite_note-92">[92]</a> AI CAD <a href="Virtual_assistant" title="Virtual assistant">assistants</a> are used as tools to help streamline workflow.<a href="#cite_note-93">[93]</a>

<div class="mw-heading mw-heading2"><h2 id="Software_and_hardware">Software and hardware</h2></div>

Generative AI models are used to power <a href="Chatbot" title="Chatbot">chatbot</a> products such as <a href="ChatGPT" title="ChatGPT">ChatGPT</a>, <a href="Programming_tools" class="mw-redirect" title="Programming tools">programming tools</a> such as <a href="GitHub_Copilot" title="GitHub Copilot">GitHub Copilot</a>,<a href="#cite_note-94">[94]</a> <a href="Text-to-image" class="mw-redirect" title="Text-to-image">text-to-image</a> products such as Midjourney, and text-to-video products such as <a href="Runway_(company)" title="Runway (company)">Runway</a> Gen-2.<a href="#cite_note-95">[95]</a> Generative AI features have been integrated into a variety of existing commercially available products such as <a href="Microsoft_Office" title="Microsoft Office">Microsoft Office</a> (<a href="Microsoft_Copilot" title="Microsoft Copilot">Microsoft Copilot</a>),<a href="#cite_note-96">[96]</a> <a href="Google_Photos" title="Google Photos">Google Photos</a>,<a href="#cite_note-97">[97]</a> and the <a href="Adobe_Inc." title="Adobe Inc.">Adobe Suite</a> (<a href="Adobe_Firefly" title="Adobe Firefly">Adobe Firefly</a>).<a href="#cite_note-98">[98]</a> Many generative AI models are also available as <a href="Open-source_software" title="Open-source software">open-source software</a>, including Stable Diffusion and the LLaMA<a href="#cite_note-99">[99]</a> language model.

Smaller generative AI models with up to a few billion parameters can run on <a href="Smartphones" class="mw-redirect" title="Smartphones">smartphones</a>, embedded devices, and <a href="Personal_computers" class="mw-redirect" title="Personal computers">personal computers</a>. For example, LLaMA-7B (a version with 7 billion parameters) can run on a <a href="Raspberry_Pi_4" title="Raspberry Pi 4">Raspberry Pi 4</a><a href="#cite_note-100">[100]</a> and one version of Stable Diffusion can run on an <a href="IPhone_11" title="IPhone 11">iPhone 11</a>.<a href="#cite_note-101">[101]</a>

Larger models with tens of billions of parameters can run on <a href="Laptop" title="Laptop">laptop</a> or <a href="Desktop_computers" class="mw-redirect" title="Desktop computers">desktop computers</a>. To achieve an acceptable speed, models of this size may require <a href="AI_accelerator" class="mw-redirect" title="AI accelerator">accelerators</a> such as the <a href="GPU" class="mw-redirect" title="GPU">GPU</a> chips produced by <a href="NVIDIA" class="mw-redirect" title="NVIDIA">NVIDIA</a> and <a href="AMD" title="AMD">AMD</a> or the Neural Engine included in <a href="Apple_silicon" title="Apple silicon">Apple silicon</a> products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC.<a href="#cite_note-102">[102]</a>

The advantages of running generative AI locally include protection of <a href="Information_privacy" title="Information privacy">privacy</a> and <a href="Intellectual_property" title="Intellectual property">intellectual property</a>, and avoidance of <a href="Rate_limiting" title="Rate limiting">rate limiting</a> and <a href="Censorship" title="Censorship">censorship</a>. The <a href="Reddit" title="Reddit">subreddit</a> r/LocalLLaMA in particular focuses on using <a href="Consumer_electronics" title="Consumer electronics">consumer</a>-grade gaming <a href="Graphics_card" title="Graphics card">graphics cards</a><a href="#cite_note-103">[103]</a> through such techniques as <a href="Large_language_model#Compression" title="Large language model">compression</a>. That forum is one of only two sources <a href="Andrej_Karpathy" title="Andrej Karpathy">Andrej Karpathy</a> trusts for <a href="Language_model#Evaluation_and_benchmarks" title="Language model">language model benchmarks</a>.<a href="#cite_note-104">[104]</a> <a href="Yann_LeCun" title="Yann LeCun">Yann LeCun</a> has advocated open-source models for their value to <a href="Vertical_market_software" title="Vertical market software">vertical applications</a><a href="#cite_note-105">[105]</a> and for improving <a href="AI_safety" title="AI safety">AI safety</a>.<a href="#cite_note-106">[106]</a>

Language models with hundreds of billions of parameters, such as GPT-4 or <a href="PaLM" title="PaLM">PaLM</a>, typically run on <a href="Datacenter" class="mw-redirect" title="Datacenter">datacenter</a> computers equipped with arrays of <a href="GPUs" class="mw-redirect" title="GPUs">GPUs</a> (such as NVIDIA's <a href="Hopper_(microarchitecture)" title="Hopper (microarchitecture)">H100</a>) or <a href="AI_accelerator" class="mw-redirect" title="AI accelerator">AI accelerator</a> chips (such as Google's <a href="Tensor_Processing_Unit" title="Tensor Processing Unit">TPU</a>). These very large models are typically accessed as <a href="Cloud_computing" title="Cloud computing">cloud</a> services over the Internet.

In 2022, the <a href="United_States_New_Export_Controls_on_Advanced_Computing_and_Semiconductors_to_China" title="United States New Export Controls on Advanced Computing and Semiconductors to China">United States New Export Controls on Advanced Computing and Semiconductors to China</a> imposed restrictions on exports to China of <a href="GPU" class="mw-redirect" title="GPU">GPU</a> and AI accelerator chips used for generative AI.<a href="#cite_note-107">[107]</a> Chips such as the NVIDIA A800<a href="#cite_note-108">[108]</a> and the <a href="Biren_Technology" title="Biren Technology">Biren Technology</a> BR104<a href="#cite_note-109">[109]</a> were developed to meet the requirements of the sanctions.

There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as <a href="GPTZero" title="GPTZero">GPTZero</a>), as well as images, audio or video coming from it.<a href="#cite_note-110">[110]</a> Potential mitigation strategies for <a href="Artificial_intelligence_content_detection" title="Artificial intelligence content detection">detecting generative AI content</a> include <a href="Digital_watermarking" title="Digital watermarking">digital watermarking</a>, <a href="Data_provenance" class="mw-redirect" title="Data provenance">content authentication</a>, <a href="Information_retrieval" title="Information retrieval">information retrieval</a>, and <a href="Supervised_learning" title="Supervised learning">machine learning classifier models</a>.<a href="#cite_note-111">[111]</a> Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work.<a href="#cite_note-112">[112]</a><a href="#cite_note-113">[113]</a>

<div class="mw-heading mw-heading3"><h3 id="Generative_models_and_training_techniques">Generative models and training techniques</h3></div>

<div class="mw-heading mw-heading4"><h4 id="Generative_adversarial_networks">Generative adversarial networks</h4></div>

<a href="Generative_adversarial_network" title="Generative adversarial network">Generative adversarial networks</a> (GANs) are an influential generative modeling technique. GANs consist of two neural networks—the generator and the discriminator—trained simultaneously in a competitive setting. The generator creates <a href="Synthetic_data" title="Synthetic data">synthetic data</a> by transforming random noise into samples that resemble the training dataset. The discriminator is trained to distinguish the authentic data from synthetic data produced by the generator.<a href="#cite_note-114">[114]</a> The two models engage in a <a href="Minimax" title="Minimax">minimax</a> game: the generator aims to create increasingly realistic data to "fool" the discriminator, while the discriminator improves its ability to distinguish real from fake data. This continuous training setup enables the generator to produce high-quality and realistic outputs.<a href="#cite_note-115">[115]</a>

<div class="mw-heading mw-heading4"><h4 id="Variational_autoencoders">Variational autoencoders</h4></div>

<a href="Variational_autoencoder" title="Variational autoencoder">Variational autoencoders</a> (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks such as <a href="Noise_reduction" title="Noise reduction">noise reduction</a> from images, <a href="Data_compression" title="Data compression">data compression</a>, identifying unusual patterns, and <a href="Facial_recognition_system" title="Facial recognition system">facial recognition</a>. Unlike <a href="Autoencoder" title="Autoencoder">standard autoencoders</a>, which compress input data into a fixed latent representation, VAEs model the <a href="Latent_space" title="Latent space">latent space</a> as a probability distribution,<a href="#cite_note-116">[116]</a> allowing for smooth sampling and interpolation between data points. The encoder ("recognition model") maps input data to a latent space, producing means and variances that define a probability distribution. The decoder ("generative model") samples from this latent distribution and attempts to reconstruct the original input. VAEs optimize a loss function that includes both the reconstruction error and a <a href="Kullback%E2%80%93Leibler_divergence" title="Kullback–Leibler divergence">Kullback–Leibler divergence</a> term, which ensures the latent space follows a known prior distribution. VAEs are particularly suitable for tasks that require structured but smooth latent spaces, although they may create blurrier images than GANs. They are used for applications like image generation, data interpolation and <a href="Anomaly_detection" title="Anomaly detection">anomaly detection</a>.

<div class="mw-heading mw-heading5"><h5 id="Transformers">Transformers</h5></div>

Transformers became the foundation for many powerful generative models, most notably the <a href="Generative_pre-trained_transformer" title="Generative pre-trained transformer">generative pre-trained transformer</a> (GPT) series developed by OpenAI. They marked a major shift in natural language processing by replacing traditional <a href="Recurrent_neural_network" title="Recurrent neural network">recurrent</a> and <a href="Convolutional_neural_network" title="Convolutional neural network">convolutional</a> models.<a href="#cite_note-117">[117]</a> This architecture allows models to process entire sequences simultaneously and capture long-range dependencies more efficiently. The <a href="Attention_(machine_learning)" title="Attention (machine learning)">self-attention mechanism</a> enables the model to capture the significance of every word in a sequence when predicting the subsequent word, thus improving its contextual understanding. Unlike recurrent neural networks, transformers process all the tokens in parallel, which improves the training efficiency and scalability. Transformers are typically pre-trained on enormous corpora in a <a href="Self-supervised_learning" title="Self-supervised learning">self-supervised</a> manner, prior to being <a href="Fine-tuning_(deep_learning)" title="Fine-tuning (deep learning)">fine-tuned</a>.

<div class="mw-heading mw-heading2"><h2 id="Law_and_regulation">Law and regulation</h2></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Regulation_of_artificial_intelligence" title="Regulation of artificial intelligence">Regulation of artificial intelligence</a></div>

In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the <a href="Biden_administration" class="mw-redirect" title="Biden administration">Biden administration</a> in July 2023 to watermark AI-generated content.<a href="#cite_note-118">[118]</a> In October 2023, <a href="Executive_Order_14110" title="Executive Order 14110">Executive Order 14110</a> applied the <a href="Defense_Production_Act" class="mw-redirect" title="Defense Production Act">Defense Production Act</a> to require all US companies to report information to the federal government when training certain high-impact AI models.<a href="#cite_note-119">[119]</a><a href="#cite_note-120">[120]</a>

In the European Union, the proposed <a href="Artificial_Intelligence_Act" title="Artificial Intelligence Act">Artificial Intelligence Act</a> includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.<a href="#cite_note-121">[121]</a><a href="#cite_note-122">[122]</a>

In China, the <a href="Interim_Measures_for_the_Management_of_Generative_AI_Services" title="Interim Measures for the Management of Generative AI Services">Interim Measures for the Management of Generative AI Services</a> introduced by the <a href="Cyberspace_Administration_of_China" title="Cyberspace Administration of China">Cyberspace Administration of China</a> regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI services must "adhere to socialist core values".<a href="#cite_note-123">[123]</a><a href="#cite_note-124">[124]</a>

<div class="mw-heading mw-heading3"><h3 id="Copyright">Copyright</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Artificial_intelligence_and_copyright" title="Artificial intelligence and copyright">Artificial intelligence and copyright</a></div>

<div class="mw-heading mw-heading4"><h4 id="Training_with_copyrighted_content">Training with copyrighted content</h4></div>

Generative AI systems such as <a href="ChatGPT" title="ChatGPT">ChatGPT</a> and <a href="Midjourney" title="Midjourney">Midjourney</a> are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under <a href="Fair_use" title="Fair use">fair use</a>, while copyright holders have argued that it infringes their rights.<a href="#cite_note-crscopyright-125">[125]</a>

Proponents of fair use training have argued that it is a <a href="Transformative_use" title="Transformative use">transformative use</a> and does not involve making copies of copyrighted works available to the public.<a href="#cite_note-crscopyright-125">[125]</a> Critics have argued that image generators such as <a href="Midjourney" title="Midjourney">Midjourney</a> can create nearly-identical copies of some copyrighted images,<a href="#cite_note-126">[126]</a> and that generative AI programs compete with the content they are trained on.<a href="#cite_note-127">[127]</a>

As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing.

<a href="Getty_Images" title="Getty Images">Getty Images</a> has sued <a href="Stability_AI" title="Stability AI">Stability AI</a> over the use of its images to train <a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a>.<a href="#cite_note-128">[128]</a> Both the <a href="Authors_Guild" title="Authors Guild">Authors Guild</a> and <a href="The_New_York_Times" title="The New York Times">The New York Times</a> have sued <a href="Microsoft" title="Microsoft">Microsoft</a> and <a href="OpenAI" title="OpenAI">OpenAI</a> over the use of their works to train <a href="ChatGPT" title="ChatGPT">ChatGPT</a>.<a href="#cite_note-129">[129]</a><a href="#cite_note-130">[130]</a>

<div class="mw-heading mw-heading4"><h4 id="Copyright_of_AI-generated_content">Copyright of AI-generated content</h4></div>

A separate question is whether AI-generated works can qualify for copyright protection. The <a href="United_States_Copyright_Office" title="United States Copyright Office">United States Copyright Office</a> has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack human authorship.<a href="#cite_note-131">[131]</a> Some legal professionals have suggested that <a href="Monkey_selfie_copyright_dispute" title="Monkey selfie copyright dispute">Naruto v. Slater</a> (2018), in which the <a href="U.S._9th_Circuit_Court_of_Appeals" class="mw-redirect" title="U.S. 9th Circuit Court of Appeals">U.S. 9th Circuit Court of Appeals</a> held that <a href="Non-human" title="Non-human">non-humans</a> cannot be copyright holders of <a href="Animal-made_art" title="Animal-made art">artistic works</a>, could be a potential precedent in copyright litigation over works created by generative AI.<a href="#cite_note-132">[132]</a> However, the office has also begun taking public input to determine if these rules need to be refined for generative AI.<a href="#cite_note-133">[133]</a>

In January 2025, the <a href="United_States_Copyright_Office" title="United States Copyright Office">United States Copyright Office</a> (USCO) released extensive guidance regarding the use of AI tools in the creative process, and established that "...generative AI systems also offer tools that similarly allow users to exert control. [These] can enable the user to control the selection and placement of individual creative elements. Whether such modifications rise to the minimum standard of originality required under <a href="Feist_Publications%2C_Inc.%2C_v._Rural_Telephone_Service_Co." title="Feist Publications, Inc., v. Rural Telephone Service Co.">Feist</a> will depend on a case-by-case determination. In those cases where they do, the output should be copyrightable"<a href="#cite_note-134">[134]</a> Subsequently, the USCO registered the first visual artwork to be composed of entirely AI-generated materials, titled "A Single Piece of American Cheese".<a href="#cite_note-135">[135]</a>

<div class="mw-heading mw-heading2"><h2 id="Concerns">Concerns</h2></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Ethics_of_artificial_intelligence" title="Ethics of artificial intelligence">Ethics of artificial intelligence</a></div>

The development of generative AI has raised concerns from <a href="Government" title="Government">governments</a>, businesses, and individuals, resulting in protests, legal actions, calls to <a href="Pause_Giant_AI_Experiments%3A_An_Open_Letter" title="Pause Giant AI Experiments: An Open Letter">pause AI experiments</a>, and actions by multiple governments. In a July 2023 briefing of the <a href="United_Nations_Security_Council" title="United Nations Security Council">United Nations Security Council</a>, <a href="Secretary-General_of_the_United_Nations" title="Secretary-General of the United Nations">Secretary-General</a> <a href="Ant%C3%B3nio_Guterres" title="António Guterres">António Guterres</a> stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale".<a href="#cite_note-136">[136]</a> In addition, generative AI has a significant <a href="Carbon_footprint" title="Carbon footprint">carbon footprint</a>.<a href="#cite_note-:82-137">[137]</a><a href="#cite_note-:14-138">[138]</a>

<div class="mw-heading mw-heading3"><h3 id="Academic_honesty">Academic honesty</h3></div>

Generative AI can be used to generate and modify academic prose, to paraphrasing sources, and translate languages. The use of generative AI in a classroom setting can be a form of <a href="Academic_dishonesty#Plagiarism" title="Academic dishonesty">academic plagiarism</a>. Some schools have banned ChatGPT and similar tools.<a href="#cite_note-Barrett-139">[139]</a><a href="#cite_note-Chan2023-140">[140]</a>

A commonly proposed use for teachers is grading and giving feedback. Companies like Pearson and ETS use AI to score grammar, mechanics, usage, and style, but not for main ideas or overall structure.<a href="#cite_note-Barrett-139">[139]</a> The National Council of Teachers of English says machine scoring makes students feel their writing isn't worth reading.<a href="#cite_note-141">[141]</a> AI scoring has also given unfair results for students from different ethnic backgrounds.<a href="#cite_note-142">[142]</a>

<div class="mw-heading mw-heading3"><h3 id="Job_losses">Job losses</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main articles: <a href="Workplace_impact_of_artificial_intelligence" title="Workplace impact of artificial intelligence">Workplace impact of artificial intelligence</a> and <a href="Technological_unemployment" title="Technological unemployment">Technological unemployment</a></div>

From the early days of the development of AI, there have been arguments put forward by <a href="ELIZA" title="ELIZA">ELIZA</a> creator <a href="Joseph_Weizenbaum" title="Joseph Weizenbaum">Joseph Weizenbaum</a> and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements.<a href="#cite_note-144">[144]</a> In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost.<a href="#cite_note-145">[145]</a><a href="#cite_note-146">[146]</a> In July 2023, developments in generative AI contributed to the <a href="2023_Hollywood_labor_disputes" title="2023 Hollywood labor disputes">2023 Hollywood labor disputes</a>. <a href="Fran_Drescher" title="Fran Drescher">Fran Drescher</a>, president of the <a href="Screen_Actors_Guild" title="Screen Actors Guild">Screen Actors Guild</a>, declared that "artificial intelligence poses an existential threat to creative professions" during the <a href="2023_SAG-AFTRA_strike" title="2023 SAG-AFTRA strike">2023 SAG-AFTRA strike</a>.<a href="#cite_note-147">[147]</a> Voice generation AI has been seen as a potential challenge to the <a href="Voice_acting" title="Voice acting">voice acting</a> sector.<a href="#cite_note-148">[148]</a><a href="#cite_note-149">[149]</a>

The intersection of AI and employment concerns among underrepresented groups globally remains a critical facet. While AI promises efficiency enhancements and skill acquisition, concerns about job displacement and biased recruiting processes persist among these groups, as outlined in surveys by <a href="Fast_Company" title="Fast Company">Fast Company</a>. To leverage AI for a more equitable society, proactive steps encompass mitigating biases, advocating transparency, respecting privacy and consent, and embracing diverse teams and ethical considerations. Strategies involve redirecting policy emphasis on regulation, inclusive design, and education's potential for personalized teaching to maximize benefits while minimizing harms.<a href="#cite_note-150">[150]</a>

<div class="mw-heading mw-heading3"><h3 id="Racial_and_gender_bias">Racial and gender bias</h3></div>

Generative AI models can reflect and amplify any <a href="Cultural_bias" title="Cultural bias">cultural bias</a> present in the underlying data. For example, a language model might assume that doctors and judges are male, and that secretaries or nurses are female, if those biases are common in the training data.<a href="#cite_note-151">[151]</a> Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs,<a href="#cite_note-152">[152]</a> if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts<a href="#cite_note-153">[153]</a> and reweighting training data.<a href="#cite_note-DALL-E2-mitigations-154">[154]</a>

<div class="mw-heading mw-heading3"><h3 id="Deepfakes">Deepfakes</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Deepfake" title="Deepfake">Deepfake</a></div>

Deepfakes (a <a href="Portmanteau" class="mw-redirect" title="Portmanteau">portmanteau</a> of "deep learning" and "fake"<a href="#cite_note-FoxNews2018-155">[155]</a>) are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using <a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">artificial neural networks</a>.<a href="#cite_note-:3-156">[156]</a> Deepfakes have garnered widespread attention and concerns for their uses in <a href="Deepfake_pornography" title="Deepfake pornography">deepfake celebrity pornographic videos</a>, <a href="Revenge_porn" title="Revenge porn">revenge porn</a>, <a href="Fake_news" title="Fake news">fake news</a>, <a href="Hoax" title="Hoax">hoaxes</a>, health <a href="Disinformation" title="Disinformation">disinformation</a>, <a href="Accounting_scandals" title="Accounting scandals">financial fraud</a>, and covert <a href="Foreign_election_interference" class="mw-redirect" title="Foreign election interference">foreign election interference</a>.<a href="#cite_note-HighSnobiety2018-157">[157]</a><a href="#cite_note-158">[158]</a><a href="#cite_note-159">[159]</a><a href="#cite_note-160">[160]</a><a href="#cite_note-161">[161]</a><a href="#cite_note-162">[162]</a><a href="#cite_note-163">[163]</a> This has elicited responses from both industry and government to detect and limit their use.<a href="#cite_note-:21-164">[164]</a><a href="#cite_note-:5-165">[165]</a>

In July 2023, the fact-checking company <a href="Logically_(company)" title="Logically (company)">Logically</a> found that the popular generative AI models <a href="Midjourney" title="Midjourney">Midjourney</a>, <a href="DALL-E_2" class="mw-redirect" title="DALL-E 2">DALL-E 2</a> and <a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a> would produce plausible disinformation images when prompted to do so, such as images of <a href="Electoral_fraud" title="Electoral fraud">electoral fraud</a> in the United States and Muslim women supporting India's <a href="Hindu_nationalist" class="mw-redirect" title="Hindu nationalist">Hindu nationalist</a> <a href="Bharatiya_Janata_Party" title="Bharatiya Janata Party">Bharatiya Janata Party</a>.<a href="#cite_note-166">[166]</a><a href="#cite_note-167">[167]</a>

In April 2024, a paper proposed to use <a href="Blockchain" title="Blockchain">blockchain</a> (<a href="Distributed_ledger" title="Distributed ledger">distributed ledger</a> technology) to promote "transparency, verifiability, and decentralization in AI development and usage".<a href="#cite_note-168">[168]</a>

<div class="mw-heading mw-heading4"><h4 id="Audio_deepfakes">Audio deepfakes</h4></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Audio_deepfake" title="Audio deepfake">Audio deepfake</a></div>

Instances of users abusing software to generate controversial statements in the vocal style of celebrities, public officials, and other famous individuals have raised ethical concerns over voice generation AI.<a href="#cite_note-169">[169]</a><a href="#cite_note-170">[170]</a><a href="#cite_note-:52-171">[171]</a><a href="#cite_note-172">[172]</a><a href="#cite_note-173">[173]</a><a href="#cite_note-:62-174">[174]</a> In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and <a href="Identity_document" title="Identity document">identity verification</a>.<a href="#cite_note-engadget.com-175">[175]</a>

Concerns and fandoms have spawned from <a href="AI-generated_music" class="mw-redirect" title="AI-generated music">AI-generated music</a>. The same software used to clone voices has been used on famous musicians' voices to create songs that mimic their voices, gaining both tremendous popularity and criticism.<a href="#cite_note-176">[176]</a><a href="#cite_note-177">[177]</a><a href="#cite_note-178">[178]</a> Similar techniques have also been used to create improved quality or full-length versions of songs that have been leaked or have yet to be released.<a href="#cite_note-179">[179]</a>

Generative AI has also been used to create new digital artist personalities, with some of these receiving enough attention to receive record deals at major labels.<a href="#cite_note-180">[180]</a> The developers of these virtual artists have also faced their fair share of criticism for their personified programs, including backlash for "dehumanizing" an artform, and also creating artists which create unrealistic or immoral appeals to their audiences.<a href="#cite_note-181">[181]</a>

<div class="mw-heading mw-heading3"><h3 id="Illegal_imagery">Illegal imagery</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Child_pornography#Artificially_generated_or_simulated_imagery" title="Child pornography">Child pornography § Artificially generated or simulated imagery</a></div>

Many websites that allow <a href="AI_pornography" class="mw-redirect" title="AI pornography">explicit AI generated images or videos</a> have been created,<a href="#cite_note-182">[182]</a> and this has been used to create illegal content, such as <a href="Rape_pornography" title="Rape pornography">rape</a>, <a href="Child_sexual_abuse_material" class="mw-redirect" title="Child sexual abuse material">child sexual abuse material</a>,<a href="#cite_note-183">[183]</a><a href="#cite_note-184">[184]</a> <a href="Necrophilia" title="Necrophilia">necrophilia</a>, and <a href="Zoophilia" title="Zoophilia">zoophilia</a>.

<div class="mw-heading mw-heading3"><h3 id="Cybercrime">Cybercrime</h3></div>

Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including <a href="Phishing" title="Phishing">phishing</a> scams.<a href="#cite_note-185">[185]</a> <a href="Deepfake" title="Deepfake">Deepfake</a> video and audio have been used to create disinformation and fraud. In 2020, former Google <a href="Click_fraud" title="Click fraud">click fraud</a> czar <a href="Shuman_Ghosemajumder" title="Shuman Ghosemajumder">Shuman Ghosemajumder</a> argued that once deepfake videos become perfectly realistic, they would stop appearing remarkable to viewers, potentially leading to uncritical acceptance of false information.<a href="#cite_note-186">[186]</a> Additionally, <a href="Large_language_model" title="Large language model">large language models</a> and other forms of text-generation AI have been used to create fake reviews of <a href="E-commerce" title="E-commerce">e-commerce</a> websites to boost ratings.<a href="#cite_note-187">[187]</a> Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.<a href="#cite_note-188">[188]</a>

A 2023 study showed that generative AI can be vulnerable to jailbreaks, <a href="Reverse_psychology" title="Reverse psychology">reverse psychology</a> and <a href="Prompt_injection" title="Prompt injection">prompt injection</a> attacks, enabling attackers to obtain help with harmful requests, such as for crafting <a href="Social_engineering_(security)" title="Social engineering (security)">social engineering</a> and <a href="Phishing" title="Phishing">phishing attacks</a>.<a href="#cite_note-189">[189]</a> Additionally, other researchers have demonstrated that open-source models can be <a href="Fine-tuning_(deep_learning)" title="Fine-tuning (deep learning)">fine-tuned</a> to remove their safety restrictions at low cost.<a href="#cite_note-190">[190]</a>

<div class="mw-heading mw-heading3"><h3 id="Reliance_on_industry_giants">Reliance on industry giants</h3></div>

Training <a href="Frontier_model" class="mw-redirect" title="Frontier model">frontier AI models</a> requires an enormous amount of computing power. Usually only <a href="Big_Tech" title="Big Tech">Big Tech</a> companies have the financial resources to make such investments. Smaller start-ups such as <a href="Cohere" title="Cohere">Cohere</a> and <a href="OpenAI" title="OpenAI">OpenAI</a> end up buying access to <a href="Data_centers" class="mw-redirect" title="Data centers">data centers</a> from <a href="Google" title="Google">Google</a> and <a href="Microsoft" title="Microsoft">Microsoft</a> respectively.<a href="#cite_note-191">[191]</a>

<div class="mw-heading mw-heading3"><h3 id="Energy_and_environment">Energy and environment</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Environmental_impacts_of_artificial_intelligence" class="mw-redirect" title="Environmental impacts of artificial intelligence">Environmental impacts of artificial intelligence</a></div>

AI has a significant carbon footprint due to growing energy consumption from both training and usage.<a href="#cite_note-:82-137">[137]</a><a href="#cite_note-:14-138">[138]</a> Scientists and journalists have expressed concerns about the environmental impact that the development and deployment of generative models are having: high CO2 emissions,<a href="#cite_note-:1-192">[192]</a><a href="#cite_note-:2-193">[193]</a><a href="#cite_note-:7-194">[194]</a> large amounts of freshwater used for data centers,<a href="#cite_note-:8-195">[195]</a><a href="#cite_note-:9-196">[196]</a> and high amounts of electricity usage.<a href="#cite_note-:2-193">[193]</a><a href="#cite_note-:10-197">[197]</a><a href="#cite_note-:11-198">[198]</a> There is also concern that these impacts may increase as these models are incorporated into widely used search engines such as Google Search and Bing,<a href="#cite_note-:10-197">[197]</a> as <a href="Chatbot" title="Chatbot">chatbots</a> and other applications become more popular,<a href="#cite_note-:9-196">[196]</a><a href="#cite_note-:10-197">[197]</a> and as models need to be retrained.<a href="#cite_note-:10-197">[197]</a>

The carbon footprint of generative AI globally is estimated to be growing steadily, with potential annual emissions ranging from 18.21 to 245.94 million tons of CO2 by 2035,<a href="#cite_note-199">[199]</a> with the highest estimates for 2035 nearing the impact of the United States <a href="Beef_industry" class="mw-redirect" title="Beef industry">beef industry</a> on emissions (currently estimated to emit 257.5 million tons annually as of 2024).<a href="#cite_note-200">[200]</a>

Proposed mitigation strategies include factoring potential environmental costs prior to model development or data collection,<a href="#cite_note-:1-192">[192]</a> increasing efficiency of data centers to reduce electricity/energy usage,<a href="#cite_note-:7-194">[194]</a><a href="#cite_note-:10-197">[197]</a><a href="#cite_note-:11-198">[198]</a> building more efficient <a href="Machine_learning" title="Machine learning">machine learning models</a>,<a href="#cite_note-:2-193">[193]</a><a href="#cite_note-:8-195">[195]</a><a href="#cite_note-:9-196">[196]</a> minimizing the number of times that models need to be retrained,<a href="#cite_note-:7-194">[194]</a> developing a government-directed framework for auditing the environmental impact of these models,<a href="#cite_note-:7-194">[194]</a><a href="#cite_note-:8-195">[195]</a> regulating for transparency of these models,<a href="#cite_note-:7-194">[194]</a> regulating their energy and water usage,<a href="#cite_note-:8-195">[195]</a> encouraging researchers to publish data on their models' carbon footprint,<a href="#cite_note-:7-194">[194]</a><a href="#cite_note-:10-197">[197]</a> and increasing the number of subject matter experts who understand both machine learning and climate science.<a href="#cite_note-:7-194">[194]</a>

<div class="mw-heading mw-heading3"><h3 id="Content_quality">Content quality</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="AI_slop" title="AI slop">AI slop</a> and <a href="Dead_Internet_theory" title="Dead Internet theory">Dead Internet theory</a></div>

<a href="The_New_York_Times" title="The New York Times">The New York Times</a> defines <a href="AI_slop" title="AI slop">slop</a> as analogous to <a href="Spamming" title="Spamming">spam</a>: "shoddy or unwanted A.I. content in social media, art, books, and ... in search results."<a href="#cite_note-201">[201]</a> Journalists have expressed concerns about the scale of low-quality generated content with respect to social media content moderation,<a href="#cite_note-:12-202">[202]</a> the monetary incentives from social media companies to spread such content,<a href="#cite_note-:12-202">[202]</a><a href="#cite_note-:13-203">[203]</a> false political messaging,<a href="#cite_note-:13-203">[203]</a> spamming of scientific research paper submissions,<a href="#cite_note-204">[204]</a> increased time and effort to find higher quality or desired content on the Internet,<a href="#cite_note-205">[205]</a> the indexing of generated content by search engines,<a href="#cite_note-206">[206]</a> and on journalism itself.<a href="#cite_note-207">[207]</a>

A paper published by researchers at Amazon Web Services AI Labs found that over 57% of sentences from a sample of over 6 billion sentences from <a href="Common_Crawl" title="Common Crawl">Common Crawl</a>, a snapshot of web pages, were <a href="Machine_translation" title="Machine translation">machine translated</a>. Many of these automated translations were seen as lower quality, especially for sentences that were translated into at least three languages. Many lower-resource languages (ex. <a href="Wolof_language" title="Wolof language">Wolof</a>, <a href="Xhosa_language" title="Xhosa language">Xhosa</a>) were translated across more languages than higher-resource languages (ex. English, French).<a href="#cite_note-208">[208]</a><a href="#cite_note-209">[209]</a>

In September 2024, <a href="Open_Mind_Common_Sense" title="Open Mind Common Sense">Robyn Speer</a>, the author of wordfreq, an open source database that calculated word frequencies based on text from the Internet, announced that she had stopped updating the data for several reasons: high costs for obtaining data from <a href="Reddit" title="Reddit">Reddit</a> and <a href="Twitter" title="Twitter">Twitter</a>, excessive focus on generative AI compared to other methods in the <a href="Natural_language_processing" title="Natural language processing">natural language processing</a> community, and that "generative AI has polluted the data".<a href="#cite_note-210">[210]</a>

The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from <a href="University_College_London" title="University College London">University College London</a> estimated that in 2023, more than 60,000 scholarly articles—over 1% of all publications—were likely written with LLM assistance.<a href="#cite_note-211">[211]</a> According to <a href="Stanford_University" title="Stanford University">Stanford University</a>'s Institute for Human-Centered AI, approximately 17.5% of newly published computer science papers and 16.9% of peer review text now incorporate content generated by LLMs.<a href="#cite_note-212">[212]</a> Many academic disciplines have concerns about the factual reliability of academic content generated by AI.<a href="#cite_note-213">[213]</a>

Visual content follows a similar trend. Since the launch of <a href="DALL-E" title="DALL-E">DALL-E</a> 2 in 2022, it is estimated that an average of 34 million images have been created daily. As of August 2023, more than 15 billion images had been generated using text-to-image algorithms, with 80% of these created by models based on <a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a>.<a href="#cite_note-214">[214]</a>

If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur.<a href="#cite_note-215">[215]</a> Training an AI model exclusively on the output of another AI model produces a lower-quality model. Repeating this process, where each new model is trained on the previous model's output, leads to progressive degradation and eventually results in a "<a href="Model_collapse" title="Model collapse">model collapse</a>" after multiple iterations.<a href="#cite_note-216">[216]</a> Tests have been conducted with pattern recognition of handwritten letters and with pictures of human faces.<a href="#cite_note-217">[217]</a> As a consequence, the value of data collected from genuine human interactions with systems may become increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.

On the other side, <a href="Synthetic_data" title="Synthetic data">synthetic data</a> is often used as an alternative to data produced by real-world events. Such data can be deployed to validate mathematical models and to train machine learning models while preserving user privacy,<a href="#cite_note-218">[218]</a> including for structured data.<a href="#cite_note-219">[219]</a> The approach is not limited to text generation; image generation has been employed to train computer vision models.<a href="#cite_note-220">[220]</a>

<div class="mw-heading mw-heading3"><h3 id="Misuse_in_journalism">Misuse in journalism</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Automated_journalism" title="Automated journalism">Automated journalism</a> and <a href="List_of_fake_news_websites#Generative_AI" title="List of fake news websites">List of fake news websites § Generative AI</a></div>

Generative AI's potential to generate a large amount of content with little effort is also affecting journalism.<a href="#cite_note-221">[221]</a> In January 2023, Futurism.com broke the story that <a href="CNET" title="CNET">CNET</a> had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories.<a href="#cite_note-222">[222]</a> In April 2023, Die Aktuelle published an AI-generated fake interview of <a href="Michael_Schumacher" title="Michael Schumacher">Michael Schumacher</a>.<a href="#cite_note-223">[223]</a> In May 2024, Futurism noted that a content management system video by AdVon Commerce, which had used generative AI to produce articles for many of the aforementioned outlets, appeared to show that they "had produced tens of thousands of articles for more than 150 publishers."<a href="#cite_note-224">[224]</a> In 2025, a report from the American Sunlight Project stated that <a href="Pravda_network" title="Pravda network">Pravda network</a> was publishing as many as 10,000 articles a day, and concluded that much of this content aimed to push Russian narratives into <a href="Large_language_model" title="Large language model">large language models</a> through their training data.<a href="#cite_note-225">[225]</a>

In June 2024, <a href="Reuters_Institute" class="mw-redirect" title="Reuters Institute">Reuters Institute</a> published its Digital News Report for 2024. In a survey of people in America and Europe, Reuters Institute reports that 52% and 47% respectively are uncomfortable with news produced by "mostly AI with some human oversight", and 23% and 15% respectively report being comfortable. 42% of Americans and 33% of Europeans reported that they were comfortable with news produced by "mainly human with some help from AI". The results of global surveys reported that people were more uncomfortable with news topics including politics (46%), crime (43%), and local news (37%) produced by AI than other news topics.<a href="#cite_note-226">[226]</a>

<div class="mw-heading mw-heading2"><h2 id="Detection_and_awareness">Detection and awareness</h2></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Artificial_intelligence_content_detection" title="Artificial intelligence content detection">Artificial intelligence content detection</a></div>

Online users have falsely assumed media of using generative artificial intelligence for content, such as video games Little Droid and <a href="Catly" title="Catly">Catly</a>.<a href="#cite_note-227">[227]</a>

Due to various concerns about citizens' unknowingly consuming generative AI media content, proponents argue for labeling such content to provide context. The <a href="Cyberspace_Administration_of_China" title="Cyberspace Administration of China">Cyberspace Administration of China</a> issued rules obligating service providers to labeling this content online.<a href="#cite_note-228">[228]</a>

The popularity of ChatGPT caused the emergence of tools that detect whether content was AI-generated, such as <a href="GPTZero" title="GPTZero">GPTZero</a>, but the risk of false accusations (<a href="False_positives_and_false_negatives" title="False positives and false negatives">false positives</a>) has remained a concern.<a href="#cite_note-229">[229]</a> <a href="Digital_watermarking" title="Digital watermarking">Digital watermarking</a> allows to reach high detection accuracy by subtly altering the generated content in a way that can be detected by software, but without being noticeable by users. OpenAI developed in 2023 a digital watermarking tool that allowed to detect content generated by <a href="ChatGPT" title="ChatGPT">ChatGPT</a> with an estimated accuracy of 99.9%, when given enough text. But OpenAI chose not to release it, worrying that users would switch to competitor products, and arguing that digital watermarking can be circumvented by bad actors, for example with superficial rephrasing.<a href="#cite_note-230">[230]</a><a href="#cite_note-231">[231]</a> Google's digital watermarking tool called SynthID was integrated in 2025 into products like Gemini, Imagen and Veo. Google also created the portal SynthID detector for users to check whether text, images or videos were produced with Google's generative AI products.<a href="#cite_note-232">[232]</a>

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<ul><li><a href="Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a> – Type of AI with wide-ranging abilities</li>

<li><a href="Artificial_imagination" title="Artificial imagination">Artificial imagination</a> – Artificial simulation of human imagination</li>

<li><a href="Artificial_intelligence_art" class="mw-redirect" title="Artificial intelligence art">Artificial intelligence art</a> – Visual media created with AIPages displaying short descriptions of redirect targets</li>

<li><a href="Artificial_life" title="Artificial life">Artificial life</a> – Field of study</li>

<li><a href="Chatbot" title="Chatbot">Chatbot</a> – Program that simulates conversation</li>

<li><a href="Computational_creativity" title="Computational creativity">Computational creativity</a> – Multidisciplinary endeavour</li>

<li><a href="Generative_adversarial_network" title="Generative adversarial network">Generative adversarial network</a> – Deep learning method</li>

<li><a href="Generative_pre-trained_transformer" title="Generative pre-trained transformer">Generative pre-trained transformer</a> – Type of large language model</li>

<li><a href="Large_language_model" title="Large language model">Large language model</a> – Type of machine learning model</li>

<li><a href="Lists_of_open-source_artificial_intelligence_software" title="Lists of open-source artificial intelligence software">Lists of open-source artificial intelligence software</a></li>

<li><a href="Music_and_artificial_intelligence" title="Music and artificial intelligence">Music and artificial intelligence</a> – Usage of artificial intelligence to generate music</li>

<li><a href="Generative_AI_pornography" title="Generative AI pornography">Generative AI pornography</a> – Explicit material produced by generative AI</li>

<li><a href="Procedural_generation" title="Procedural generation">Procedural generation</a> – Method in which data is created algorithmically as opposed to manually</li>

<li><a href="Retrieval-augmented_generation" title="Retrieval-augmented generation">Retrieval-augmented generation</a> – Type of information retrieval using LLMs</li>

<li><a href="Stochastic_parrot" title="Stochastic parrot">Stochastic parrot</a> – Term used in machine learning</li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<li id="cite_note-230"><a href="#cite_ref-230">^</a> <cite id="CITEREFHa2024" class="citation web cs1">Ha, Anthony (August 4, 2024). <a rel="nofollow" class="external text" href="https://techcrunch.com/2024/08/04/openai-says-its-taking-a-deliberate-approach-to-releasing-tools-that-can-detect-writing-from-chatgpt/">"OpenAI says it's taking a 'deliberate approach' to releasing tools that can detect writing from ChatGPT"</a>. TechCrunch. Retrieved July 29, 2025.</cite>

</li>

<li id="cite_note-231"><a href="#cite_ref-231">^</a> <cite id="CITEREFBarnum" class="citation web cs1">Barnum, Deepa Seetharaman and Matt. <a rel="nofollow" class="external text" href="https://www.wsj.com/tech/ai/openai-tool-chatgpt-cheating-writing-135b755a">"Exclusive | There's a Tool to Catch Students Cheating With ChatGPT. OpenAI Hasn't Released It"</a>. WSJ. Retrieved July 29, 2025.</cite>

</li>

<li id="cite_note-232"><a href="#cite_ref-232">^</a> <cite id="CITEREFPeters2025" class="citation web cs1">Peters, Jay (May 21, 2025). <a rel="nofollow" class="external text" href="https://www.theverge.com/news/672013/google-synthid-detector-ai-generated-content-watermark-i-o-2025">"Google has a new tool to help detect AI-generated content"</a>. The Verge. Retrieved July 29, 2025.</cite>

</li>

</ol></div></div>

<div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2></div>

<ul><li><cite id="CITEREFHeCaoTan2025" class="citation journal cs1">He, Ran; Cao, Jie; Tan, Tieniu (2025). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970245">"Generative Artificial Intelligence: A Historical Perspective"</a>. <a href="National_Science_Review" title="National Science Review">National Science Review</a>. 12 (5): nwaf050. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1093%2Fnsr%2Fnwaf050">10.1093/nsr/nwaf050</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970245">11970245</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/40191253">40191253</a>.</cite></li>

<li><a href="James_Gleick" title="James Gleick">James Gleick</a>, "<a rel="nofollow" class="external text" href="https://www.nybooks.com/articles/2025/07/24/the-parrot-in-the-machine-the-ai-con-bender-hanna/">The Parrot in the Machine</a>" (review of <a href="Emily_M._Bender" title="Emily M. Bender">Emily M. Bender</a> and Alex Hanna, The AI Con: How to Fight Big Tech's Hype and Create the Future We Want, Harper, 274 pp.; and <a href="James_Boyle_(legal_scholar)" title="James Boyle (legal scholar)">James Boyle</a>, The Line: AI and the Future of Personhood, MIT Press, 326 pp.), <a href="The_New_York_Review_of_Books" title="The New York Review of Books">The New York Review of Books</a>, vol. LXXII, no. 12 (24 July 2025), pp. 43–46. "[C]hatbox 'writing' has a bland, regurgitated quality. Textures are flattened, sharp edges are sanded. No chatbox could ever have said that April is the cruelest month or that fog comes on little cat feet (though they might now, because one of their chief skills is <a href="Plagiarism" title="Plagiarism">plagiarism</a>). And when synthetically extruded text turns out wrong, it can be comically wrong. When a movie fan asked Google whether a certain actor was in <a href="Heat_(1995_film)" title="Heat (1995 film)">Heat</a>, he received this 'AI Overview': 'No, <a href="Angelina_Jolie" title="Angelina Jolie">Angelina Jolie</a> is not in heat.'" (p. 44.)</li></ul>

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</style></div><div role="navigation" class="navbox" aria-labelledby="Generative_AI286" style="padding:3px"><table class="nowraplinks hlist mw-collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit"><tbody><tr><th scope="col" class="navbox-title" colspan="2"><div id="Generative_AI286" style="font-size:114%;margin:0 4em"></div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%">Concepts</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Autoencoder" title="Autoencoder">Autoencoder</a></li>

<li><a href="Deep_learning" title="Deep learning">Deep learning</a></li>

<li><a href="Fine-tuning_(deep_learning)" title="Fine-tuning (deep learning)">Fine-tuning</a></li>

<li><a href="Foundation_model" title="Foundation model">Foundation model</a></li>

<li><a href="Generative_adversarial_network" title="Generative adversarial network">Generative adversarial network</a></li>

<li><a href="Generative_pre-trained_transformer" title="Generative pre-trained transformer">Generative pre-trained transformer</a></li>

<li><a href="Large_language_model" title="Large language model">Large language model</a></li>

<li><a href="Model_Context_Protocol" title="Model Context Protocol">Model Context Protocol</a></li>

<li><a href="Neural_network_(machine_learning)" title="Neural network (machine learning)">Neural network</a></li>

<li><a href="Prompt_engineering" title="Prompt engineering">Prompt engineering</a></li>

<li><a href="Reinforcement_learning_from_human_feedback" title="Reinforcement learning from human feedback">Reinforcement learning from human feedback</a></li>

<li><a href="Retrieval-augmented_generation" title="Retrieval-augmented generation">Retrieval-augmented generation</a></li>

<li><a href="Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li>

<li><a href="Stochastic_parrot" title="Stochastic parrot">Stochastic parrot</a></li>

<li><a href="Synthetic_data" title="Synthetic data">Synthetic data</a></li>

<li><a href="Top-p_sampling" title="Top-p sampling">Top-p sampling</a></li>

<li><a href="Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a></li>

<li><a href="Variational_autoencoder" title="Variational autoencoder">Variational autoencoder</a></li>

<li><a href="Vibe_coding" title="Vibe coding">Vibe coding</a></li>

<li><a href="Vision_transformer" title="Vision transformer">Vision transformer</a></li>

<li><a href="Waluigi_effect" title="Waluigi effect">Waluigi effect</a></li>

<li><a href="Word_embedding" title="Word embedding">Word embedding</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Models</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Text</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Character.ai" title="Character.ai">Character.ai</a></li>

<li><a href="Claude_(language_model)" title="Claude (language model)">Claude</a></li>

<li><a href="DeepSeek_(chatbot)" title="DeepSeek (chatbot)">DeepSeek</a></li>

<li><a href="Ernie_Bot" title="Ernie Bot">Ernie</a></li>

<li><a href="Gemini_(chatbot)" title="Gemini (chatbot)">Gemini</a></li>

<li><a href="ChatGPT" title="ChatGPT">ChatGPT</a></li>

<li><a href="Tencent#Research" title="Tencent">Hunyuan Turbo S</a></li>

<li><a href="Llama_(language_model)" title="Llama (language model)">Llama</a></li>

<li><a href="Microsoft_Copilot" title="Microsoft Copilot">Microsoft Copilot</a></li>

<li><a href="MiniMax_(company)#Technology" title="MiniMax (company)">MiniMax-M1</a></li>

<li><a href="Mistral_AI#Magistral_Small_and_Magistral_Medium" title="Mistral AI">Magistral Medium</a></li>

<li><a href="01.AI#Yi" title="01.AI">Yi-Lightning</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Coding</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<li><a href="Claude_(language_model)" title="Claude (language model)">Claude Code</a></li>

<li><a href="Cursor_(code_editor)" title="Cursor (code editor)">Cursor</a></li>

<li><a href="Mistral_AI" title="Mistral AI">Devstral</a></li>

<li><a href="GitHub_Copilot" title="GitHub Copilot">GitHub Copilot</a></li>

<li><a href="Qwen" title="Qwen">Qwen3-Coder</a></li>

<li><a href="Replit" title="Replit">Replit</a></li>

<li><a href="Xcode" title="Xcode">Xcode</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Text-to-image_model" title="Text-to-image model">Image</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Aurora_(text-to-image_model)" class="mw-redirect" title="Aurora (text-to-image model)">Aurora</a></li>

<li><a href="Adobe_Firefly" title="Adobe Firefly">Firefly</a></li>

<li><a href="GPT-4o#GPT_Image_1" title="GPT-4o">GPT Image 1</a></li>

<li><a href="Ideogram_(text-to-image_model)" title="Ideogram (text-to-image model)">Ideogram</a></li>

<li><a href="Imagen_(text-to-image_model)" title="Imagen (text-to-image model)">Imagen</a></li>

<li><a href="Canva#Acquisitions" title="Canva">Leonardo</a></li>

<li><a href="Midjourney" title="Midjourney">Midjourney</a></li>

<li><a href="Recraft" title="Recraft">Recraft</a></li>

<li><a href="ByteDance" title="ByteDance">Seedream</a></li>

<li><a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Text-to-video_model" title="Text-to-video model">Video</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Dream_Machine_(text-to-video_model)" title="Dream Machine (text-to-video model)">Dream Machine</a></li>

<li><a href="MiniMax_(company)#Hailuo_AI" title="MiniMax (company)">Hailuo AI</a></li>

<li><a href="Kling_(text-to-video_model)" class="mw-redirect" title="Kling (text-to-video model)">Kling</a></li>

<li><a href="Midjourney" title="Midjourney">Midjourney Video</a></li>

<li><a href="Runway_(company)#Services_and_technologies" title="Runway (company)">Runway Gen</a></li>

<li><a href="ByteDance" title="ByteDance">Seedance</a></li>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Speech_synthesis#Text-to-speech_systems" title="Speech synthesis">Speech</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<li><a href="ElevenLabs#Products" title="ElevenLabs">Eleven</a></li>

<li><a href="MiniMax_(company)#Technology" title="MiniMax (company)">Speech-02</a></li>

<li><a href="WaveNet" title="WaveNet">WaveNet</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Music</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Endel_(app)" title="Endel (app)">Endel</a></li>

<li><a href="Google_DeepMind#Music_generation" title="Google DeepMind">Lyria</a></li>

<li><a href="Riffusion" title="Riffusion">Riffusion</a></li>

</div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Intelligent_agent" title="Intelligent agent">Agents</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Salesforce#Artificial_intelligence" title="Salesforce">Agentforce</a></li>

<li><a href="Zhipu_AI#AutoGLM" title="Zhipu AI">AutoGLM</a></li>

<li><a href="AutoGPT" title="AutoGPT">AutoGPT</a></li>

<li><a href="Devin_AI" title="Devin AI">Devin AI</a></li>

<li><a href="Manus_(AI_agent)" title="Manus (AI agent)">Manus</a></li>

<li><a href="OpenAI_Codex" title="OpenAI Codex">OpenAI Codex</a></li>

<li><a href="OpenAI_Operator" title="OpenAI Operator">Operator</a></li>

<li><a href="Replit" title="Replit">Replit Agent</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="List_of_artificial_intelligence_companies" title="List of artificial intelligence companies">Companies</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<li><a href="Aleph_Alpha" title="Aleph Alpha">Aleph Alpha</a></li>

<li><a href="Anthropic" title="Anthropic">Anthropic</a></li>

<li><a href="Baichuan" title="Baichuan">Baichuan</a></li>

<li><a href="Canva" title="Canva">Canva</a></li>

<li><a href="Cognition_AI" title="Cognition AI">Cognition AI</a></li>

<li><a href="Cohere" title="Cohere">Cohere</a></li>

<li><a href="Contextual_AI" title="Contextual AI">Contextual AI</a></li>

<li><a href="DeepSeek" title="DeepSeek">DeepSeek</a></li>

<li><a href="ElevenLabs" title="ElevenLabs">ElevenLabs</a></li>

<li><a href="Google_DeepMind" title="Google DeepMind">Google DeepMind</a></li>

<li><a href="HeyGen" title="HeyGen">HeyGen</a></li>

<li><a href="Hugging_Face" title="Hugging Face">Hugging Face</a></li>

<li><a href="Inflection_AI" title="Inflection AI">Inflection AI</a></li>

<li><a href="Krikey_AI" title="Krikey AI">Krikey AI</a></li>

<li><a href="Kuaishou" title="Kuaishou">Kuaishou</a></li>

<li><a href="MiniMax_(company)" title="MiniMax (company)">MiniMax</a></li>

<li><a href="Mistral_AI" title="Mistral AI">Mistral AI</a></li>

<li><a href="Moonshot_AI" title="Moonshot AI">Moonshot AI</a></li>

<li><a href="OpenAI" title="OpenAI">OpenAI</a></li>

<li><a href="Perplexity_AI" title="Perplexity AI">Perplexity AI</a></li>

<li><a href="Runway_(company)" title="Runway (company)">Runway</a></li>

<li><a href="Safe_Superintelligence_Inc." title="Safe Superintelligence Inc.">Safe Superintelligence</a></li>

<li><a href="Salesforce" title="Salesforce">Salesforce</a></li>

<li><a href="Scale_AI" title="Scale AI">Scale AI</a></li>

<li><a href="SoundHound" title="SoundHound">SoundHound</a></li>

<li><a href="Stability_AI" title="Stability AI">Stability AI</a></li>

<li><a href="Synthesia_(company)" title="Synthesia (company)">Synthesia</a></li>

<li><a href="Thinking_Machines_Lab" title="Thinking Machines Lab">Thinking Machines Lab</a></li>

<li><a href="Zhipu_AI" title="Zhipu AI">Zhipu AI</a></li></ul>

</div></td></tr><tr><td class="navbox-abovebelow" colspan="2"><div>

<ul><li> Category</li></ul>

</div></td></tr></tbody></table></div>

<div class="navbox-styles"></div><div role="navigation" class="navbox" aria-labelledby="Digital_art94" style="padding:3px"><table class="nowraplinks hlist mw-collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit"><tbody><tr><th scope="col" class="navbox-title" colspan="2"><div id="Digital_art94" style="font-size:114%;margin:0 4em"><a href="Digital_art" title="Digital art">Digital art</a></div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%">Tools</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Hardware</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Computer_art" title="Computer art">Computer</a>

<li><a href="2D_computer_graphics" title="2D computer graphics">2D graphics</a>

<li><a href="3D_computer_graphics" title="3D computer graphics">3D graphics</a></li></ul></li>

<li><a href="Xerox_art" title="Xerox art">Xerox</a></li>

<li><a href="Applications_of_3D_printing" title="Applications of 3D printing">3D printer</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Software</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Graphic_art_software" title="Graphic art software">Graphic art software</a></li>

<li><a href="Fractal-generating_software" title="Fractal-generating software">Fractal-generating software</a></li>

<li><a href="Animation_software" class="mw-redirect" title="Animation software">Animation software</a></li></ul>

</div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Forms</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<li><a href="Artificial_intelligence_art" class="mw-redirect" title="Artificial intelligence art">Artificial intelligence art</a></li>

<li><a href="ASCII_art" title="ASCII art">ASCII art</a></li>

<li><a href="Computer_art_scene" title="Computer art scene">Computer art scene</a></li>

<li><a href="Computer_music" title="Computer music">Computer music</a></li>

<li><a href="Non-fungible_token" title="Non-fungible token">Crypto art</a></li>

<li><a href="Cyberarts" title="Cyberarts">Cyberarts</a></li>

<li><a href="Digital_illustration" title="Digital illustration">Digital illustration</a></li>

<li><a href="Digital_imaging" title="Digital imaging">Digital imaging</a></li>

<li><a href="Digital_painting" title="Digital painting">Digital painting</a></li>

<li><a href="Digital_photography" title="Digital photography">Digital photography</a></li>

<li><a href="Digital_poetry" title="Digital poetry">Digital poetry</a></li>

<li><a href="Digital_architecture" title="Digital architecture">Digital architecture</a></li>

<li><a href="Electronic_music" title="Electronic music">Electronic music</a></li>

<li><a href="Evolutionary_art" title="Evolutionary art">Evolutionary art</a></li>

<li><a href="Fractal_art" title="Fractal art">Fractal art</a></li>

<li><a href="Generative_art" title="Generative art">Generative art</a></li>

<li><a href="Generative_music" title="Generative music">Generative music</a></li>

<li><a href="Glitch_art" title="Glitch art">Glitch art</a></li>

<li><a href="Immersion_(virtual_reality)" title="Immersion (virtual reality)">Immersion</a></li>

<li><a href="Interactive_art" title="Interactive art">Interactive art</a></li>

<li><a href="Internet_art" title="Internet art">Internet art</a></li>

<li><a href="Motion_graphics" title="Motion graphics">Motion graphics</a></li>

<li><a href="Music_visualization" title="Music visualization">Music visualization</a></li>

<li><a href="Photograph_manipulation" title="Photograph manipulation">Photograph manipulation</a></li>

<li><a href="Pixel_art" title="Pixel art">Pixel art</a></li>

<li><a href="Non-photorealistic_rendering#Artistic_rendering" title="Non-photorealistic rendering">Render art</a></li>

<li><a href="Software_art" title="Software art">Software art</a></li>

<li><a href="Systems_art" title="Systems art">Systems art</a></li>

<li><a href="Texture_mapping" title="Texture mapping">Texture mapping</a></li>

<li><a href="Virtual_art" title="Virtual art">Virtual art</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Notable artists</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Refik_Anadol" title="Refik Anadol">Refik Anadol</a></li>

<li><a href="Cory_Arcangel" title="Cory Arcangel">Cory Arcangel</a></li>

<li><a href="Sougwen_Chung" title="Sougwen Chung">Sougwen Chung</a></li>

<li><a href="Harold_Cohen_(artist)" title="Harold Cohen (artist)">Harold Cohen</a></li>

<li><a href="Char_Davies" title="Char Davies">Char Davies</a></li>

<li><a href="Stephanie_Dinkins" title="Stephanie Dinkins">Stephanie Dinkins</a></li>

<li><a href="Jake_Elwes" title="Jake Elwes">Jake Elwes</a></li>

<li><a href="David_Em" title="David Em">David Em</a></li>

<li><a href="Desmond_Paul_Henry" title="Desmond Paul Henry">Desmond Paul Henry</a></li>

<li><a href="Mario_Klingemann" title="Mario Klingemann">Mario Klingemann</a></li>

<li><a href="Lynn_Hershman_Leeson" title="Lynn Hershman Leeson">Lynn Hershman Leeson</a></li>

<li><a href="Zachary_Lieberman" title="Zachary Lieberman">Zachary Lieberman</a></li>

<li><a href="Margot_Lovejoy" title="Margot Lovejoy">Margot Lovejoy</a></li>

<li><a href="Mauro_Martino" title="Mauro Martino">Mauro Martino</a></li>

<li><a href="Eric_Millikin" title="Eric Millikin">Eric Millikin</a></li>

<li><a href="Hamid_Naderi_Yeganeh" title="Hamid Naderi Yeganeh">Hamid Naderi Yeganeh</a></li>

<li><a href="Trevor_Paglen" title="Trevor Paglen">Trevor Paglen</a></li>

<li><a href="Casey_Reas" title="Casey Reas">Casey Reas</a></li>

<li><a href="Anna_Ridler" title="Anna Ridler">Anna Ridler</a></li>

<li><a href="Ben_Rubin_(artist)" title="Ben Rubin (artist)">Ben Rubin (artist)</a></li>

<li><a href="Camille_Utterback" title="Camille Utterback">Camille Utterback</a></li>

<li><a href="Pindar_Van_Arman" title="Pindar Van Arman">Pindar Van Arman</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Notable artworks</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Edmond_de_Belamy" title="Edmond de Belamy">Edmond de Belamy</a></li>

<li><a href="Barnsley_fern" title="Barnsley fern">Barnsley fern</a></li>

<li><a href="Jesus_Dress_Up" title="Jesus Dress Up">Jesus Dress Up</a></li>

<li><a href="Listening_Post_(artwork)" title="Listening Post (artwork)">Listening Post (artwork)</a></li>

<li><a href="Remember_To_Rise" title="Remember To Rise">Remember To Rise</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Organizations, conferences</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Artfutura" title="Artfutura">Artfutura</a></li>

<li><a href="Artmedia" title="Artmedia">Artmedia</a></li>

<li><a href="Austin_Museum_of_Digital_Art" title="Austin Museum of Digital Art">Austin Museum of Digital Art</a></li>

<li><a href="Computer_Arts_Society" title="Computer Arts Society">Computer Arts Society</a></li>

<li><a href="EVA_Conferences" title="EVA Conferences">EVA Conferences</a></li>

<li><a href="Los_Angeles_Center_for_Digital_Art" title="Los Angeles Center for Digital Art">Los Angeles Center for Digital Art</a></li>

<li><a href="Lumen_Prize" title="Lumen Prize">Lumen Prize</a></li>

<li><a href="Onedotzero" title="Onedotzero">onedotzero</a></li>

<li><a href="SIGGRAPH" title="SIGGRAPH">SIGGRAPH</a></li>

<li><a href="V%26A_Digital_Futures" title="V&A Digital Futures">V&A Digital Futures</a></li></ul>

</div></td></tr></tbody></table></div>

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