Generative artificial intelligence

Short description: Subset of AI using generative models

Not to be confused with Artificial general intelligence.

Théâtre D'opéra Spatial (2022), an image made using generative AI  Artificial intelligence

Major goals

* Knowledge reasoning
* Planning
* Machine learning
* Natural language processing
* Computer vision
* Robotics
* Artificial general intelligence

Approaches

* Symbolic
* Deep learning
* Bayesian networks
* Evolutionary algorithms

Philosophy

* Ethics
* Existential risk
* Turing test
* Chinese room
* Control problem
* Friendly AI

History

* Timeline
* Progress
* AI winter

Technology

* Applications
* Projects
* Programming languages

Glossary

* Glossary

* v
* t
* e

Generative artificial intelligence (Generative AI, GenAI,[1] or GAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, audio, software code or other forms of data.[2][3] These models learn the underlying patterns and structures of their training data and use them to produce new data[4][5] based on the input, which often comes in the form of natural language prompts.[6][7]

Generative AI tools have become more common since the AI boom in the 2020s. This boom was made possible by improvements in transformer-based deep neural networks, particularly large language models (LLMs). Major tools include chatbots such as ChatGPT, Copilot, Gemini, Claude, Grok, and DeepSeek; text-to-image models such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video models such as Veo and Sora.[8][9][10] Technology companies developing generative AI include OpenAI, xAI, Anthropic, Meta AI, Microsoft, Google, Mistral AI, DeepSeek, Baidu[11] and Yandex.[12]

Generative AI is used across many industries, including software development,[13] healthcare,[14] finance,[15] entertainment,[16] customer service,[17] sales and marketing,[18] art, writing,[19] fashion,[20] and product design.[21] The production of generative AI systems requires large scale data centers using specialized chips which require a lot of electricity for processing and water for cooling.[22]

Generative AI has raised many ethical questions and governance challenges as it can be used for cybercrime, or to deceive or manipulate people through fake news or deepfakes.[23][24] Even if used ethically, it may lead to mass replacement of human jobs.[25] The tools themselves have been criticized as violating intellectual property laws, since they are trained on copyrighted works.[26] The material and energy intensity of the AI systems has raised concerns about the environmental impact of AI, especially in light of the challenges created by the energy transition.

## Contents

* 1 History
* 1.1 Early history
* 1.2 Generative neural networks (2014–2019)
* 1.3 Generative AI boom (2020–)
* 2 Applications
* 2.1 Text and software code
* 2.2 Audio
* 2.3 Images
* 2.4 Video
* 2.5 Robotics
* 2.6 3D modeling
* 3 Software and hardware
* 3.1 Generative models and training techniques
* 3.1.1 Generative adversarial networks
* 3.1.2 Variational autoencoders
* 3.1.2.1 Transformers
* 4 Law and regulation
* 4.1 Copyright
* 4.1.1 Training with copyrighted content
* 4.1.2 Copyright of AI-generated content
* 5 Concerns
* 5.1 Academic honesty
* 5.2 Job losses
* 5.3 Racial and gender bias
* 5.4 Deepfakes
* 5.4.1 Audio deepfakes
* 5.5 Illegal imagery
* 5.6 Cybercrime
* 5.7 Information laundering
* 5.8 Reliance on industry giants
* 5.9 Energy and environment
* 5.10 Content quality
* 5.11 Misuse in journalism
* 6 Detection and awareness
* 7 See also
* 8 References
* 9 Further reading

## History

Main article: History of artificial intelligence

### Early history

The first example of an algorithmically generated media is likely the Markov chain. Markov chains have long been used to model natural languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 1906,[27][28] and analyzed the pattern of vowels and consonants in the novel Eugeny Onegin using Markov chains. Once a Markov chain is trained on a text corpus, it can then be used as a probabilistic text generator.[29][30]

Computers were needed to go beyond Markov chains. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON, the computer program Cohen created to generate paintings.[31]

The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal.[32][33] Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use,[34] process plans for manufacturing[32] and decision plans such as in prototype autonomous spacecraft.[35]

### Generative neural networks (2014–2019)

See also: Machine learning and deep learning

File:Discriminative vs Generative Neural Networks.pngAbove: An image classifier, an example of a neural network trained with a discriminative objective. Below: A text-to-image model, an example of a network trained with a generative objective.

Machine learning uses both discriminative models and generative models to predict data. Beginning in the late 2000s, the introduction of deep learning technology led to improvements in image classification, speech recognition, natural language processing and other tasks. Neural networks in this era were typically trained as discriminative models due to the difficulty of generative modeling.[36]

In 2014, advancements such as the variational autoencoder and generative adversarial network 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, such as DeepDream.[citation needed]

In 2017, the Transformer network enabled advancements in generative models compared to older long short-term memory (LSTM) models, leading to the first generative pre-trained transformer (GPT), known as GPT-1, in 2018.[37][non-primary source needed]

### Generative AI boom (2020–)

Main article: AI boom

AI generated images have become much more advanced.

In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, marked one of the earliest popular use cases of generative AI.[38] The platform is credited as the first mainstream service to popularize AI voice cloning (audio deepfakes) in memes and content creation, influencing subsequent developments in voice AI technology.[39][40]

In 2021, the emergence of DALL-E, a transformer-based generative model, marked an advance in AI-generated imagery.[41] This was followed by the releases of Midjourney and Stable Diffusion in 2022, which further democratized access to high-quality artificial intelligence art creation from natural language prompts.[42] These systems can generate photorealistic images, artwork, and designs based on text descriptions, leading to widespread adoption among artists, designers, and the general public.

In November 2022, the public release of ChatGPT popularized generative AI for general-purpose text-based tasks.[43] The system's ability to engage in natural conversations, generate creative content, assist with coding, and perform various analytical tasks captured global attention and sparked widespread discussion about AI's potential impact on work, education, and creativity.[44][45]

In March 2023, GPT-4's release represented another jump in generative AI capabilities. A team from Microsoft Research controversially argued that it "could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system."[46] 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.[47] Later in 2023, Meta released ImageBind, an AI model research project which includes six different modalities: text, images, video, thermal data, 3D data, audio, and motion.[48]

In December 2023, Google unveiled Gemini, a multimodal AI model available in four versions: Ultra, Pro, Flash, and Nano.[49] The company integrated Gemini Pro into its Bard chatbot and announced plans for "Bard Advanced", based on the larger Gemini Ultra model.[50] In February 2024, Google unified Bard and Duet AI under the Gemini brand, launching a mobile app on Android and integrating the service into the Google app on iOS.[51]

Anthropic also released a family of large language models named Claude, which showed competitive performance on benchmarks.[52]

Private investment in AI (pink) and generative AI (green).

Asia–Pacific 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.[53] According to a survey by SAS and Coleman Parkes Research, China 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%. A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, substantially surpassing the United States in patent applications.[54] 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.[55]

By mid 2025, despite continued consumer growth, many companies were increasingly abandoning generative AI pilot projects as they had difficulties with integration, data quality and unmet returns, leading analysts to characterize the period as entering the Gartner hype cycle's "trough of disillusionment" phase.[56][57]

## Applications

Notable types of generative AI models include generative pre-trained transformers (GPTs), generative adversarial networks (GANs), and variational autoencoders (VAEs). Generative AI systems are multimodal if they can process multiple types of inputs or generate multiple types of outputs.[58] For example, GPT-4o can both process and generate text, images and audio.[59]

Generative AI has made its appearance in a wide variety of industries, radically changing the dynamics of content creation, analysis, and delivery. In healthcare,[60] for instance, generative AI accelerates drug discovery by creating molecular structures with target characteristics[61] and generates radiology 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 operational costs, and support data-driven decision-making in financial institutions.[62] 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.[63] In the educational field, in Colombia, student use of Meta's generative AI programs resulted in a decline in scores.[64]

### Text and software code

Main article: Large language model

See also: Software:Code completion, Autocomplete, Vibe coding, List of large language models, List of text corpora, and List of chatbots

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. 😕

Text generated in 2023 by Bing Chat, prompted with a question about Carl Jung's concept of shadow self[65]

Large language models (LLM) are trained on tokenized text from text corpora. Such systems include ChatGPT, Gemini, Claude, LLaMA, and BLOOM. LLMs are capable of natural language processing, machine translation, and natural language generation.[66]

LLMs can be used as foundation models for other tasks.[67] They can be trained on computer code, which makes it possible to generate source code for new computer programs with prompts, a practice known as vibe coding.[68] Examples include OpenAI Codex, Tabnine, GitHub Copilot, Microsoft Copilot, and the VS Code fork Cursor.[69]

Some AI assistants help candidates cheat during online coding interviews by providing code, improvements, and explanations. Their clandestine interfaces minimize the need for eye movements that would expose cheating to the interviewer.[70]

### Audio

In 2016, DeepMind's WaveNet showed that deep neural networks are capable of generating raw waveforms.[72] WaveNet's ability to model raw waveforms meant that it could model any kind of audio, including music: for example, it was capable of generating relatively realistic-sounding human-like voices by training on recordings of real speech.[73] In subsequent years, research shifted from concatenative synthesis to deep learning speech synthesis,[74] with models like Tacotron 2 in 2018 demonstrating that neural networks could convert text into natural speech by being trained on tens of hours of speech.[75] In 2020, a free text-to-speech website called 15.ai showed that deep neural networks could generate emotionally expressive speech with only 15 seconds of speech,[76] a large reduction compared to the tens of hours of data previously required.[77]

Other platforms that use generative AI to produce speech include Amazon Polly, Meta's Voicebox, and ElevenLabs.[78] Systems that can generate music via text descriptions (text-to-music) include Meta's MusicGen and Google's MusicLM.[79][80] Audio deepfakes have been used to generate vocal tracks of lyrics that mimic the voices of other singers.[81]

### Images

Generative AI can be used to create visual art.[82] Such systems are trained on sets of images along with their text captions. Examples of text-to-image models include Stable Diffusion, DALL-E, Midjourney, Imagen, Adobe Firefly, and Flux. They can also be used for neural style transfer.[83]

### Video

See also: Text-to-video model

File:Borneo wildlife on the Kinabatangan River.webmVideo generated by Sora with prompt `Borneo wildlife on the Kinabatangan River`

Generative AI can be used to generate photorealistic videos. Examples include Sora by OpenAI,[9] Runway,[84] Make-A-Video by Meta Platforms and the open source LTX Video by Lightricks.[85][10][86]

### Robotics

By training on robotic system motions, generative AI can create new trajectories for motion planning and robot navigation.[87] Multimodal vision-language-action models such as Google's RT-2 can perform rudimentary reasoning in response to user prompts and visual input, such as picking up a toy dinosaur when given the prompt pick up the extinct animal at a table filled with toy animals and other objects.[88]

### 3D modeling

See also: Earth:Photogrammetry

Artificially intelligent computer-aided design (CAD) can use text-to-3D, image-to-3D, and video-to-3D to automate 3D modeling.[89] AI-based CAD libraries could also be developed using linked open data of schematics and diagrams.[90] AI CAD assistants are used as tools to help streamline workflow.[91]

## Software and hardware

File:GenAI Agent.pngArchitecture of a generative AI agent

Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot,[92] text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2.[93] Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot),[94] Google Photos,[95] and the Adobe Suite (Adobe Firefly).[96] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA[97] language model.

Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4[98] and one version of Stable Diffusion can run on an iPhone 11.[99]

Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by NVIDIA and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC.[100]

The advantages of running generative AI locally include protection of privacy and intellectual property, and avoidance of rate limiting and censorship. The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards[101] through such techniques as compression. That forum is one of only two sources Andrej Karpathy trusts for language model benchmarks.[102] Yann LeCun has advocated open-source models for their value to vertical applications[103] and for improving AI safety.[104]

Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as NVIDIA's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet.

In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI.[105] Chips such as the NVIDIA A800[106] and the Biren Technology BR104[107] 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 GPTZero), as well as images, audio or video coming from it.[108] Potential mitigation strategies for detecting generative AI content include digital watermarking, content authentication, information retrieval, and machine learning classifier models.[109] Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work.[110][111]

### Generative models and training techniques

#### Generative adversarial networks

Workflow for the training of a generative adversarial network.

Generative adversarial networks (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 synthetic data 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.[112] The two models engage in a minimax 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.[113]

#### Variational autoencoders

Comparison between images generated by a VAE (left) and a GAN (right). VAEs tend to produce smoother but blurrier images due to their probabilistic decoding.

Variational autoencoders (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks such as noise reduction from images, data compression, identifying unusual patterns, and facial recognition. Unlike standard autoencoders, which compress input data into a fixed latent representation, VAEs model the latent space as a probability distribution,[114] 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 Kullback–Leibler divergence 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 anomaly detection.

The full architecture of a GPT model.

##### Transformers

Transformers became the foundation for many powerful generative models, most notably the generative pre-trained transformer (GPT) series developed by OpenAI. They marked a major shift in natural language processing by replacing traditional recurrent and convolutional models.[115] This architecture allows models to process entire sequences simultaneously and capture long-range dependencies more efficiently. The self-attention mechanism 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 self-supervised manner, prior to being fine-tuned.

## Law and regulation

Main article: Regulation of artificial intelligence

In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content.[116] In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to report information to the federal government when training certain high-impact AI models.[117][118]

In the European Union, the proposed Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.[119][120]

In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China 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".[121][122]

### Copyright

Main article: Artificial intelligence and copyright

#### Training with copyrighted content

Generative AI systems such as ChatGPT and Midjourney are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under fair use, while copyright holders have argued that it infringes their rights.[123]

Proponents of fair use training have argued that it is a transformative use and does not involve making copies of copyrighted works available to the public.[123] Critics have argued that image generators such as Midjourney can create nearly-identical copies of some copyrighted images,[124] and that generative AI programs compete with the content they are trained on.[125]

As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing. Getty Images has sued Stability AI over the use of its images to train Stable Diffusion.[126] Both the Authors Guild and The New York Times have sued Microsoft and OpenAI over the use of their works to train ChatGPT.[127][128]

#### Copyright of AI-generated content

A separate question is whether AI-generated works can qualify for copyright protection. The United States Copyright Office has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack human authorship.[129] Some legal professionals have suggested that Naruto v. Slater (2018), in which the U.S. 9th Circuit Court of Appeals held that non-humans cannot be copyright holders of artistic works, could be a potential precedent in copyright litigation over works created by generative AI.[130] However, the office has also begun taking public input to determine if these rules need to be refined for generative AI.[131]

In January 2025, the United States Copyright Office (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 Feist will depend on a case-by-case determination. In those cases where they do, the output should be copyrightable"[132] Subsequently, the USCO registered the first visual artwork to be composed of entirely AI-generated materials, titled "A Single Piece of American Cheese".[133]

## Concerns

See also: Philosophy:Ethics of artificial intelligence and Artificial intelligence controversies

The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council, Secretary-General António Guterres 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".[134] In addition, generative AI has a significant carbon footprint.[135][136]

### Academic honesty

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 academic plagiarism. Some schools have banned ChatGPT and similar tools.[137][138] 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.[137] The National Council of Teachers of English says machine scoring makes students feel their writing isn't worth reading.[139] AI scoring has also given unfair results for students from different ethnic backgrounds.[140]

### Job losses

Main articles: Workplace impact of artificial intelligence and Technological unemployment

A picketer at the 2023 Writers Guild of America strike. While not a top priority, one of the WGA's 2023 requests was "regulations around the use of (generative) AI".[141]

From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum 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.[142] 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.[143][144] In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes. Fran Drescher, president of the Screen Actors Guild, declared that "artificial intelligence poses an existential threat to creative professions" during the 2023 SAG-AFTRA strike.[145] Voice generation AI has been seen as a potential challenge to the voice acting sector.[146][147]

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 Fast Company. 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.[148]

### Racial and gender bias

Generative AI models can reflect and amplify any cultural bias 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.[149] Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs,[150] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts[151] and reweighting training data.[152]

### Deepfakes

Main article: Deepfake

Deepfakes (a portmanteau of "deep learning" and "fake"[153]) are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks.[154] Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos, revenge porn, fake news, hoaxes, health disinformation, financial fraud, and covert foreign election interference.[155][156][157][158][159][160][161]

In July 2023, the fact-checking company Logically found that the popular generative AI models Midjourney, DALL-E 2 and Stable Diffusion would produce plausible disinformation images when prompted to do so, such as images of electoral fraud in the United States and Muslim women supporting India's Hindu nationalist Bharatiya Janata Party.[162][163]

#### Audio deepfakes

Main article: Audio deepfake

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.[164][165][166][167][168][169] In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and identity verification.[170]

Concerns and fandoms have spawned from AI-generated music. 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.[171][172][173] 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.[174]

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.[175] 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.[176]

### Illegal imagery

Many websites that allow explicit AI generated images or videos have been created,[177] and this has been used to create illegal content, such as rape, child sexual abuse material,[178][179] necrophilia, and zoophilia.

### Cybercrime

Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams.[180] Deepfake video and audio have been used to create disinformation and fraud. In 2020, former Google click fraud czar Shuman Ghosemajumder argued that once deepfake videos become perfectly realistic, they would stop appearing remarkable to viewers, potentially leading to uncritical acceptance of false information.[181] Additionally, large language models and other forms of text-generation AI have been used to create fake reviews of e-commerce websites to boost ratings.[182] Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.[183]

A 2023 study showed that generative AI can be vulnerable to jailbreaks, reverse psychology and prompt injection attacks, enabling attackers to obtain help with harmful requests, such as for crafting social engineering and phishing attacks.[184] Additionally, other researchers have demonstrated that open-source models can be fine-tuned to remove their safety restrictions at low cost.[185]

### Information laundering

Generative AI has been noted for its use by state-sponsored propaganda campaigns in information laundering. According to a 2025 report by Graphika, generative AI is used to launder articles from Chinese state media such as China Global Television Network through various social media sites in an attempt to disguise the articles' origin.[186]

### Reliance on industry giants

Training frontier AI models requires an enormous amount of computing power. Usually only Big Tech companies have the financial resources to make such investments. Smaller start-ups such as Cohere and OpenAI end up buying access to data centers from Google and Microsoft respectively.[187]

### Energy and environment

AI has a significant carbon footprint due to growing energy consumption from both training and usage.[135][136] Scientists and journalists have expressed concerns about the environmental impact that the development and deployment of generative models are having: high CO2 emissions,[188][189][190] large amounts of freshwater used for data centers,[191][192] and high amounts of electricity usage.[189][193][194] 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,[193] as chatbots and other applications become more popular,[192][193] and as models need to be retrained.[193]

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,[195] with the highest estimates for 2035 nearing the impact of the United States beef industry on emissions (currently estimated to emit 257.5 million tons annually as of 2024).[196]

Proposed mitigation strategies include factoring potential environmental costs prior to model development or data collection,[188] increasing efficiency of data centers to reduce electricity/energy usage,[190][193][194] building more efficient machine learning models,[189][191][192] minimizing the number of times that models need to be retrained,[190] developing a government-directed framework for auditing the environmental impact of these models,[190][191] regulating for transparency of these models,[190] regulating their energy and water usage,[191] encouraging researchers to publish data on their models' carbon footprint,[190][193] and increasing the number of subject matter experts who understand both machine learning and climate science.[190]

### Content quality

The New York Times defines slop as analogous to spam: "shoddy or unwanted A.I. content in social media, art, books, and ... in search results."[197] Journalists have expressed concerns about the scale of low-quality generated content with respect to social media content moderation,[198] the monetary incentives from social media companies to spread such content,[198][199] false political messaging,[199] spamming of scientific research paper submissions,[200] increased time and effort to find higher quality or desired content on the Internet,[201] the indexing of generated content by search engines,[202] and on journalism itself.[203]

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 Common Crawl, a snapshot of web pages, were machine translated. 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. Wolof, Xhosa) were translated across more languages than higher-resource languages (ex. English, French).[204][205]

In September 2024, Robyn Speer, 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 Reddit and Twitter, excessive focus on generative AI compared to other methods in the natural language processing community, and that "generative AI has polluted the data".[206]

The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in 2023, more than 60,000 scholarly articles—over 1% of all publications—were likely written with LLM assistance.[207] According to Stanford University'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.[208]

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.[209] 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 "model collapse" after multiple iterations.[210] Tests have been conducted with pattern recognition of handwritten letters and with pictures of human faces.[211]

On the other side, synthetic data 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,[212] including for structured data.[213] The approach is not limited to text generation; image generation has been employed to train computer vision models.[214]

### Misuse in journalism

See also: Automated journalism and List of fake news websites § Generative AI

Generative AI's potential to generate a large amount of content with little effort is also affecting journalism.[215] In January 2023, Futurism broke the story that CNET 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.[216] In April 2023, Die Aktuelle published an AI-generated fake interview of Michael Schumacher.[217] 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."[218] In 2025, a report from the American Sunlight Project stated that Pravda network was publishing as many as 10,000 articles a day, and concluded that much of this content aimed to push Russian narratives into large language models through their training data.[219]

In June 2024, Reuters Institute 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.[220]

## Detection and awareness

See also: Artificial intelligence content detection

Online users have falsely assumed media of using generative artificial intelligence for content, such as video games Little Droid and Catly.[221]

Due to various concerns about citizens' unknowingly consuming generative AI media content, proponents argue for labeling such content to provide context. The Cyberspace Administration of China issued rules obligating service providers to labeling this content online.[222]

The popularity of ChatGPT caused the emergence of tools that detect whether content was AI-generated, such as GPTZero, but the risk of false accusations (false positives) has remained a concern.[223] Digital watermarking 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 ChatGPT 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.[224][225] 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.[226]

## See also

* Artificial general intelligence - Type of AI with wide-ranging abilities
* Artificial imagination - Artificial simulation of human imagination
* Artificial intelligence art - Machine application of knowledge of human aesthetic expressions
* Artificial life - Field of study
* Chatbot
* Computational creativity - Multidisciplinary endeavour
* Generative adversarial network - Deep learning method
* Generative pre-trained transformer - Type of large language model
* Large language model - Type of machine learning model
* Lists of open-source artificial intelligence software
* Music and artificial intelligence
* Retrieval-augmented generation - Type of information retrieval using LLMs
* Stochastic parrot - Term used in machine learning

## References

1. ↑ "Executive Order N-12-23". Executive Department, State of California. September 5, 2023. https://www.gov.ca.gov/wp-content/uploads/2023/09/AI-EO-No.12-_-GGN-Signed.pdf.
2. ↑ "What is ChatGPT, DALL-E, and generative AI?". https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai.
3. ↑ "What is generative AI?". March 22, 2024. https://www.ibm.com/topics/generative-ai.
4. ↑ Pasick, Adam (March 27, 2023). "Artificial Intelligence Glossary: Neural Networks and Other Terms Explained" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html.
5. ↑ Karpathy, Andrej; Abbeel, Pieter; Brockman, Greg; Chen, Peter; Cheung, Vicki; Duan, Yan; Goodfellow, Ian; Kingma, Durk et al. (June 16, 2016). "Generative models". OpenAI. https://openai.com/research/generative-models.
6. ↑ Griffith, Erin; Metz, Cade (January 27, 2023). "Anthropic Said to Be Closing In on $300 Million in New A.I. Funding". The New York Times. https://www.nytimes.com/2023/01/27/technology/anthropic-ai-funding.html.
7. ↑ Lanxon, Nate; Bass, Dina; Davalos, Jackie (March 10, 2023). "A Cheat Sheet to AI Buzzwords and Their Meanings". Bloomberg News. https://news.bloomberglaw.com/tech-and-telecom-law/a-cheat-sheet-to-ai-buzzwords-and-their-meanings-quicktake.
8. ↑ Roose, Kevin (October 21, 2022). "A Coming-Out Party for Generative A.I., Silicon Valley's New Craze". https://www.nytimes.com/2022/10/21/technology/generative-ai.html.
9. ↑ 9.0 9.1 Metz, Cade (February 15, 2024). "OpenAI Unveils A.I. That Instantly Generates Eye-Popping Videos" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2024/02/15/technology/openai-sora-videos.html.
10. ↑ 10.0 10.1 Fink, Charlie. "LTX Video Breaks The 60-Second Barrier, Redefining AI Video As A Longform Medium" (in en). https://www.forbes.com/sites/charliefink/2025/07/16/ltx-video-breaks-the-60-second-barrier-redefining-ai-video-as-a-longform-medium/.
11. ↑ "The race of the AI labs heats up". The Economist. January 30, 2023. https://www.economist.com/business/2023/01/30/the-race-of-the-ai-labs-heats-up.
12. ↑ Petrella, Stephanie; Miller, Chris; Cooper, Benjamin (2021). "Russia's Artificial Intelligence Strategy: The Role of State-Owned Firms". Orbis 65 (1): 75–100. doi:10.1016/j.orbis.2020.11.004.
13. ↑ "The Transformative Impact of Generative AI on Software Development and Quality Engineering" (in en-US). July 17, 2024. https://www.unite.ai/the-transformative-impact-of-generative-ai-on-software-development-and-quality-engineering/.
14. ↑ Raza, Marium M.; Venkatesh, Kaushik P.; Kvedar, Joseph C. (March 7, 2024). "Generative AI and large language models in health care: pathways to implementation" (in en). npj Digital Medicine 7 (1): 62. doi:10.1038/s41746-023-00988-4. ISSN 2398-6352. PMID 38454007.
15. ↑ Mogaji, Emmanuel (January 7, 2025). "How generative AI is transforming financial services – and what it means for customers" (in en-US). https://theconversation.com/how-generative-ai-is-transforming-financial-services-and-what-it-means-for-customers-246649.
16. ↑ Bean, Thomas H. Davenport and Randy (June 19, 2023). "The Impact of Generative AI on Hollywood and Entertainment" (in en-US). https://sloanreview.mit.edu/article/the-impact-of-generative-ai-on-hollywood-and-entertainment/.
17. ↑ Brynjolfsson, Erik; Li, Danielle; Raymond, Lindsey R. (April 2023), Generative AI at Work, Working Paper Series, doi:10.3386/w31161, https://www.nber.org/papers/w31161, retrieved January 21, 2024
18. ↑ "Don't fear an AI-induced jobs apocalypse just yet". The Economist. March 6, 2023. https://www.economist.com/business/2023/03/06/dont-fear-an-ai-induced-jobs-apocalypse-just-yet.
19. ↑ Coyle, Jake (September 27, 2023). "In Hollywood writers' battle against AI, humans win (for now)". Associated Press. https://apnews.com/article/hollywood-ai-strike-wga-artificial-intelligence-39ab72582c3a15f77510c9c30a45ffc8.
20. ↑ Harreis, H.; Koullias, T.; Roberts, Roger. "Generative AI: Unlocking the future of fashion". https://www.mckinsey.com/industries/retail/our-insights/generative-ai-unlocking-the-future-of-fashion.
21. ↑ "How Generative AI Can Augment Human Creativity". Harvard Business Review. June 16, 2023. ISSN 0017-8012. https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity.
22. ↑ "AI has an environmental problem. Here's what the world can do about that." (in en). 2024-09-21. https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about.
23. ↑ Taeihagh, Araz (2025-04-04). "Governance of Generative AI" (in en). Policy and Society 44 (1): 1–22. doi:10.1093/polsoc/puaf001. ISSN 1449-4035. https://academic.oup.com/policyandsociety/article/44/1/1/7997395.
24. ↑ Simon, Felix M.; Altay, Sacha; Mercier, Hugo (18 October 2023). "Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown". Harvard Kennedy School Misinformation Review. doi:10.37016/mr-2020-127.
25. ↑ Hendrix, Justin (May 16, 2023). "Transcript: Senate Judiciary Subcommittee Hearing on Oversight of AI". https://techpolicy.press/transcript-senate-judiciary-subcommittee-hearing-on-oversight-of-ai/.
26. ↑ "New AI systems collide with copyright law". BBC News. August 1, 2023. https://www.bbc.co.uk/news/business-66231268.
27. ↑ Grinstead, Charles Miller; Snell, James Laurie (1997) (in en-us). Introduction to Probability. American Mathematical Society. pp. 464–466. ISBN 978-0-8218-0749-1. https://archive.org/details/flooved3489.
28. ↑ Bremaud, Pierre (March 9, 2013). Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues. Springer Science & Business Media. p. ix. ISBN 978-1-4757-3124-8. https://books.google.com/books?id=jrPVBwAAQBAJ.
29. ↑ Hayes, Brian (2013). "First Links in the Markov Chain". American Scientist 101 (2): 92. doi:10.1511/2013.101.92. ISSN 0003-0996.
30. ↑ Fine, Shai; Singer, Yoram; Tishby, Naftali (July 1998). "The Hierarchical Hidden Markov Model: Analysis and Applications". Machine Learning 32 (1): 41–62. doi:10.1023/A:1007469218079.
31. ↑ Bergen, Nathan; Huang, Angela (2023). "A Brief History of Generative AI". Dichotomies: Generative AI: Navigating Towards a Better Future (2): 4. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-gen-ai-dichotomies.pdf. Retrieved August 8, 2023.
32. ↑ 32.0 32.1 Alting, Leo; Zhang, Hongchao (1989). "Computer aided process planning: the state-of-the-art survey". The International Journal of Production Research 27 (4): 553–585. doi:10.1080/00207548908942569. https://www.researchgate.net/publication/236649325. Retrieved October 3, 2023.
33. ↑ Chien, Steve (1998). "Automated planning and scheduling for goal-based autonomous spacecraft". IEEE Intelligent Systems and Their Applications 13 (5): 50–55. doi:10.1109/5254.722362. Bibcode: 1998IISA...13e..50C.
34. ↑ Burstein, Mark H., ed (1994). ARPA/Rome Laboratory Knowledge-based Planning and Scheduling Initiative Workshop Proceedings. The Advanced Research Projects Agency, Department of Defense, and Rome Laboratory, US Air Force, Griffiss AFB. p. 219. ISBN 1-55860-345-X.
35. ↑ Pell, Barney; Bernard, Douglas E.; Chien, Steve A.; Gat, Erann; Muscettola, Nicola; Nayak, P. Pandurang; Wagner, Michael D.; Williams, Brian C. (1998). Bekey, George A.. ed. An Autonomous Spacecraft Agent Prototype. Autonomous Robots Volume 5, No. 1. pp. 29–45. "Our deliberator is a traditional generative AI planner based on the HSTS planning framework (Muscettola, 1994), and our control component is a traditional spacecraft attitude control system (Hackney et al. 1993). We also add an architectural component explicitly dedicated to world modeling (the mode identifier), and distinguish between control and monitoring."
36. ↑ Jebara, Tony (2012). Machine learning: discriminative and generative. 755. Springer Science & Business Media.
37. ↑ "finetune-transformer-lm". https://github.com/openai/finetune-transformer-lm.
38. ↑ Chandraseta, Rionaldi (January 21, 2021). "Generate Your Favourite Characters' Voice Lines using Machine Learning". https://towardsdatascience.com/generate-your-favourite-characters-voice-lines-using-machine-learning-c0939270c0c6.
39. ↑ Temitope, Yusuf (December 10, 2024). "15.ai Creator reveals journey from MIT Project to internet phenomenon". https://guardian.ng/technology/15-ai-creator-reveals-journey-from-mit-project-to-internet-phenomenon/.
40. ↑ Anirudh VK (March 18, 2023). "Deepfakes Are Elevating Meme Culture, But At What Cost?". https://analyticsindiamag.com/ai-origins-evolution/deepfakes-are-elevating-meme-culture-but-at-what-cost/. ""While AI voice memes have been around in some form since '15.ai' launched in 2020, [...]""
41. ↑ Coldewey, Devin (January 5, 2021). "OpenAI's DALL-E creates plausible images of literally anything you ask it to" (in en-US). https://techcrunch.com/2021/01/05/openais-dall-e-creates-plausible-images-of-literally-anything-you-ask-it-to/.
42. ↑ "Stable Diffusion Public Release" (in en-GB). https://stability.ai/blog/stable-diffusion-public-release.
43. ↑ Lock, Samantha (December 5, 2022). "What is AI chatbot phenomenon ChatGPT and could it replace humans?" (in en-GB). https://www.theguardian.com/technology/2022/dec/05/what-is-ai-chatbot-phenomenon-chatgpt-and-could-it-replace-humans.
44. ↑ Huang, Haomiao (August 23, 2023). "How ChatGPT turned generative AI into an "anything tool"". Ars Technica. https://arstechnica.com/ai/2023/08/how-chatgpt-turned-generative-ai-into-an-anything-tool/.
45. ↑ Krakowski, Sebastian (March 2025). "Human-AI agency in the age of generative AI". Information and Organization 35 (1). doi:10.1016/j.infoandorg.2025.100560.
46. ↑ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (March 22, 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL].
47. ↑ Schlagwein, Daniel; Willcocks, Leslie (September 2023). "'ChatGPT et al.': The ethics of using (generative) artificial intelligence in research and science". Journal of Information Technology 38 (3): 232–238. doi:10.1177/02683962231200411.
48. ↑ "Meta open-sources multisensory AI model that combines six types of data". May 9, 2023. https://www.theverge.com/2023/5/9/23716558/meta-imagebind-open-source-multisensory-modal-ai-model-research.
49. ↑ Kruppa, Miles (December 6, 2023). "Google Announces AI System Gemini After Turmoil at Rival OpenAI". The Wall Street Journal. ISSN 0099-9660. https://www.wsj.com/tech/ai/google-announces-ai-system-gemini-after-turmoil-at-rival-openai-10835335.
50. ↑ Edwards, Benj (December 6, 2023). "Google launches Gemini—a powerful AI model it says can surpass GPT-4". https://arstechnica.com/information-technology/2023/12/google-launches-gemini-a-powerful-ai-model-it-says-can-surpass-gpt-4/.
51. ↑ Metz, Cade (February 8, 2024). "Google Releases Gemini, an A.I.-Driven Chatbot and Voice Assistant". The New York Times. https://www.nytimes.com/2024/02/08/technology/google-gemini-ai-app.html.
52. ↑ Nuñez, Michael (March 4, 2024). "Anthropic unveils Claude 3, surpassing GPT-4 and Gemini Ultra in benchmark tests". https://venturebeat.com/ai/anthropic-unveils-claude-3-claims-new-standard-for-intelligence/.
53. ↑ "Asia Pacific open to digital and reform transformation" (in en-th). 2024-11-11. https://www.ipsos.com/en-th/ua_globaltrends.
54. ↑ Baptista, Eduardo (July 9, 2024). "China leads the world in adoption of generative AI, survey shows". Reuters. https://www.reuters.com/technology/artificial-intelligence/china-leads-world-adoption-generative-ai-survey-shows-2024-07-09/.
55. ↑ "Young Chinese Have Almost No Concerns About AI, Survey Finds". Sixth Tone. May 31, 2024. https://www.sixthtone.com/news/1015263.
56. ↑ "Gartner Says Generative AI for Procurement Has Entered the Trough of Disillusionment". Gartner. 30 July 2025. https://www.gartner.com/en/newsroom/press-releases/2025-07-30-gartner-says-generative-ai-for-procurement-has-entered-the-trough-of-disillusionment.
57. ↑ "Welcome to the AI trough of disillusionment". The Economist. 21 May 2025. https://www.economist.com/business/2025/05/21/welcome-to-the-ai-trough-of-disillusionment.
58. ↑ "A History of Generative AI: From GAN to GPT-4". March 21, 2023. https://www.marktechpost.com/2023/03/21/a-history-of-generative-ai-from-gan-to-gpt-4/.
59. ↑ Mittal, Aayush (2024-05-22). "OpenAI's GPT-4o: The Multimodal AI Model Transforming Human-Machine Interaction" (in en-US). https://www.unite.ai/openais-gpt-4o-the-multimodal-ai-model-transforming-human-machine-interaction/.
60. ↑ Serrano, Dolores R.; Luciano, Francis C.; Anaya, Brayan J.; Ongoren, Baris; Kara, Aytug; Molina, Gracia; Ramirez, Bianca I.; Sánchez-Guirales, Sergio A. et al. (October 14, 2024). "Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine" (in en). Pharmaceutics 16 (10): 1328. doi:10.3390/pharmaceutics16101328. ISSN 1999-4923. PMID 39458657.
61. ↑ Vemula, Divya; Jayasurya, Perka; Sushmitha, Varthiya; Kumar, Yethirajula Naveen; Bhandari, Vasundhra (February 1, 2023). "CADD, AI and ML in drug discovery: A comprehensive review". European Journal of Pharmaceutical Sciences 181. doi:10.1016/j.ejps.2022.106324. ISSN 0928-0987. PMID 36347444.
62. ↑ "Generative AI in Banking: Practical Use Cases and Future Potential" (in en). https://www.trinetix.com/insights/generative-ai-in-banking.
63. ↑ Element451 (November 26, 2024). "Top Benefits Of AI In Education For Teachers and Students" (in en). https://element451.com/blog/benefits-of-ai-in-education.
64. ↑ Salamanca, Laura Rodríguez (July 30, 2025). "Meta brought AI to rural Colombia. Now students are failing exams". https://restofworld.org/2025/colombia-meta-ai-education/.
65. ↑ Roose, Kevin (2023-02-16). "Bing's A.I. Chat: 'I Want to Be Alive.'". The New York Times. https://www.nytimes.com/2023/02/16/technology/bing-chatbot-transcript.html.
66. ↑ Kaushal, Ayush; Mahowald, Kyle (2022-06-06). "What do tokens know about their characters and how do they know it?". NAACL. https://aclanthology.org/2022.naacl-main.179.pdf.
67. ↑ Bommasani, R.; Hudson, D. A.; Adeli, E.; Altman, R.; Arora, S.; von Arx, S.; Bernstein, M. S.; Bohg, J.; Bosselut, A; Brunskill, E.; Brynjolfsson, E. (August 16, 2021). "On the opportunities and risks of foundation models". arXiv:2108.07258 [cs.LG].
68. ↑ Chen, Ming; Tworek, Jakub; Jun, Hongyu; Yuan, Qinyuan; Pinto, Hanyu Philippe De Oliveira; Kaplan, Jerry; Edwards, Haley; Burda, Yannick; Joseph, Nicholas; Brockman, Greg; Ray, Alvin (July 6, 2021). "Evaluating Large Language Models Trained on Code". arXiv:2107.03374 [cs.LG].
69. ↑ "Investing in Cursor". https://a16z.com/announcement/investing-in-cursor/.
70. ↑ Elias, Jennifer (March 9, 2025). "Meet the 21-year-old helping coders use AI to cheat in Google and other tech job interviews" (in en). https://www.cnbc.com/2025/03/09/google-ai-interview-coder-cheat.html.
71. ↑ "Examples". 2025-05-15. https://alpha.15.dev/examples.
72. ↑ van den Oord, Aaron; Dieleman, Sander; Zen, Heiga; Simonyan, Karen; Vinyals, Oriol; Graves, Alex; Kalchbrenner, Nal; Senior, Andrew; Kavukcuoglu, Koray (September 12, 2016). "WaveNet: A Generative Model for Raw Audio". arXiv:1609.03499 [cs.SD].
73. ↑ Meyer, David (2016-09-09). "Google's DeepMind Claims Massive Progress in Synthesized Speech". http://fortune.com/2016/09/09/google-deepmind-wavenet-ai/.
74. ↑ Triantafyllopoulos, Andreas (October 2023). "An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era". Proceedings of the IEEE 111 (10): 1355–1381. doi:10.1109/JPROC.2023.3250266. ISSN 0018-9219.
75. ↑ "Audio samples from "Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis"". 2018-08-30. https://google.github.io/tacotron/publications/semisupervised/index.html.
76. ↑ Chandraseta, Rionaldi (January 21, 2021). "Generate Your Favourite Characters' Voice Lines using Machine Learning". https://towardsdatascience.com/generate-your-favourite-characters-voice-lines-using-machine-learning-c0939270c0c6.
77. ↑ Feng, Bai (March 15, 2020). "模型参数过亿跑不动？看MIT小哥，少量数据完成高质量文本转语音！" (in zh). XinZhiYuan. https://news.qq.com/rain/a/20200315A0B0AY00.
78. ↑ Desai, Saahil (July 17, 2023). "A Voicebot Just Left Me Speechless" (in en). https://www.theatlantic.com/technology/archive/2023/07/ai-voice-assistants-name-pronunciation/674731/.
79. ↑ Agostinelli, Andrea; Denk, Timo I.; Borsos, Zalán; Engel, Jesse; Verzetti, Mauro; Caillon, Antoine; Huang, Qingqing; Jansen, Aren; Roberts, Adam; Tagliasacchi, Marco; Sharifi, Matt; Zeghidour, Neil; Frank, Christian (January 26, 2023). "MusicLM: Generating Music From Text". arXiv:2301.11325 [cs.SD].
80. ↑ Dalugdug, Mandy (August 3, 2023). "Meta in June said that it used 20,000 hours of licensed music to train MusicGen, which included 10,000 "high-quality" licensed music tracks. At the time, Meta's researchers outlined in a paper the ethical challenges that they encountered around the development of generative AI models like MusicGen.". https://www.musicbusinessworldwide.com/meet-audiocraft-metas-new-generative-ai-tool-for-audio-and-music/.
81. ↑ "Jay-Z's Delaware producer sparks debate over AI rights". https://www.delawareonline.com/story/entertainment/2023/04/21/jay-zs-delaware-producer-young-guru-artificial-voice-generators-rights/70107389007/.
82. ↑ Epstein, Ziv; Hertzmann, Aaron; Akten, Memo; Farid, Hany; Fjeld, Jessica; Frank, Morgan R.; Groh, Matthew; Herman, Laura et al. (16 June 2023). "Art and the science of generative AI". Science 380 (6650): 1110–1111. doi:10.1126/science.adh4451. PMID 37319193. Bibcode: 2023Sci...380.1110E.
83. ↑ Ramesh, Aditya; Pavlov, Mikhail; Goh, Gabriel; Gray, Scott; Voss, Chelsea; Radford, Alec; Chen, Mark; Sutskever, Ilya (2021). "Zero-shot text-to-image generation". PMLR. pp. 8821–8831. https://proceedings.mlr.press/v139/ramesh21a.html.
84. ↑ Metz, Cade (April 4, 2023). "Instant Videos Could Represent the Next Leap in A.I. Technology" (in en). https://www.nytimes.com/2023/04/04/technology/runway-ai-videos.html.
85. ↑ Nuñez, Michael (2025-05-06). "Lightricks just made AI video generation 30x faster — and you won't need a $10,000 GPU" (in en-US). https://venturebeat.com/ai/lightricks-just-made-ai-video-generation-30x-faster-and-you-wont-need-a-10000-gpu/.
86. ↑ Wong, Queenie (September 29, 2022). "Facebook Parent Meta's AI Tool Can Create Artsy Videos From Text". cnet.com. https://www.cnet.com/news/social-media/facebook-parent-metas-ai-tool-can-create-artsy-videos-from-text/.
87. ↑ Yang, Sherry; Du, Yilun (April 12, 2023). "UniPi: Learning universal policies via text-guided video generation". Google Research, Brain Team. Google AI Blog. https://ai.googleblog.com/2023/04/unipi-learning-universal-policies-via.html.
88. ↑ Brohan, Anthony (2023). "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control". arXiv:2307.15818 [cs.RO].
89. ↑ Abdullahi, Aminu (November 17, 2023). "10 Best Artificial Intelligence (AI) 3D Generators". https://www.eweek.com/artificial-intelligence/best-ai-3d-generators/.
90. ↑ "Slash CAD model build times with new AI-driven part creation methodology | GlobalSpec". https://insights.globalspec.com/article/21167/slash-cad-model-build-times-with-new-ai-driven-part-creation-methodology.
91. ↑ "The Role of Artificial Intelligence (AI) in the CAD Industry". March 22, 2023. https://www.scan2cad.com/blog/cad/ai-cad/.
92. ↑ Sabin, Sam (June 30, 2023). "GitHub has a vision to make code more secure by design". https://www.axios.com/2023/06/30/githubs-vision-to-make-code-more-secure-by-design.
93. ↑ Vincent, James (March 20, 2023). "Text-to-video AI inches closer as startup Runway announces new model". The Verge. https://www.theverge.com/2023/3/20/23648113/text-to-video-generative-ai-runway-ml-gen-2-model-access. "Text-to-video is the next frontier for generative AI, though current output is rudimentary. Runway says it'll be making its new generative video model, Gen-2, available to users in 'the coming weeks.'"
94. ↑ Vanian, Jonathan (March 16, 2023). "Microsoft adds OpenAI technology to Word and Excel". CNBC. https://www.cnbc.com/2023/03/16/microsoft-to-improve-office-365-with-chatgpt-like-generative-ai-tech-.html. "Microsoft is bringing generative artificial intelligence technologies such as the popular ChatGPT chatting app to its Microsoft 365 suite of business software....the new A.I. features, dubbed Copilot, will be available in some of the company's most popular business apps, including Word, PowerPoint and Excel."
95. ↑ Wilson, Mark (August 15, 2023). "The app's Memories feature just got a big upgrade". TechRadar. https://www.techradar.com/computing/software/google-photos-now-shows-you-an-ai-powered-highlights-reel-of-your-life. "The Google Photos app is getting a redesigned, AI-powered Memories feature...you'll be able to use generative AI to come up with some suggested names like "a desert adventure"."
96. ↑ Sullivan, Laurie (May 23, 2023). "Adobe Adds Generative AI To Photoshop". MediaPost. https://www.mediapost.com/publications/article/385660/adobe-adds-generative-ai-to-photoshop.html. "Generative artificial intelligence (AI) will become one of the most important features for creative designers and marketers. Adobe on Tuesday unveiled a Generative Fill feature in Photoshop to bring Firefly's AI capabilities into design."
97. ↑ Michael Nuñez (July 19, 2023). "LLaMA 2: How to access and use Meta's versatile open-source chatbot right now". https://venturebeat.com/ai/llama-2-how-to-access-and-use-metas-versatile-open-source-chatbot-right-now/. "If you want to run LLaMA 2 on your own machine or modify the code, you can download it directly from Hugging Face, a leading platform for sharing AI models."
98. ↑ Pounder, Les (March 25, 2023). "How To Create Your Own AI Chatbot Server With Raspberry Pi 4". https://www.tomshardware.com/how-to/create-ai-chatbot-server-on-raspberry-pi. "Using a Pi 4 with 8GB of RAM, you can create a ChatGPT-like server based on LLaMA."
99. ↑ Kemper, Jonathan (November 10, 2022). ""Draw Things" App brings Stable Diffusion to the iPhone". https://the-decoder.com/draw-things-app-brings-stable-diffusion-to-the-iphone/. "Draw Things is an app that brings Stable Diffusion to the iPhone. The AI images are generated locally, so you don't need an Internet connection."
100. ↑ Witt, Allan (July 7, 2023). "Best Computer to Run LLaMA AI Model at Home (GPU, CPU, RAM, SSD)". https://www.hardware-corner.net/guides/computer-to-run-llama-ai-model/. "To run LLaMA model at home, you will need a computer build with a powerful GPU that can handle the large amount of data and computation required for inferencing."
101. ↑ Westover, Brian (September 28, 2023). "Who Needs ChatGPT? How to Run Your Own Free and Private AI Chatbot". Ziff Davis. https://www.pcmag.com/how-to/how-to-run-your-own-chatgpt-like-llm-for-free-and-in-private.
102. ↑ @karpathy. "I pretty much only trust two LLM evals right now". https://twitter.com/karpathy/status/1737544497016578453. Missing or empty `|date=` (help)
103. ↑ @ylecun. "Nabla's shift from ChatGPT to open source LLMs...". https://twitter.com/ylecun/status/1743314259285581951. Missing or empty `|date=` (help)
104. ↑ @ylecun. "Open source platforms *increase* safety and security". https://twitter.com/ylecun/status/1719692258591506483. Missing or empty `|date=` (help)
105. ↑ Nellis, Stephen; Lee, Jane (September 1, 2022). "U.S. officials order Nvidia to halt sales of top AI chips to China". https://www.reuters.com/technology/nvidia-says-us-has-imposed-new-license-requirement-future-exports-china-2022-08-31/.
106. ↑ Shilov, Anton (May 7, 2023). "Nvidia's Chinese A800 GPU's Performance Revealed". Tom's Hardware. https://www.tomshardware.com/news/nvidia-a800-performance-revealed. "the A800 operates at 70% of the speed of A100 GPUs while complying with strict U.S. export standards that limit how much processing power Nvidia can sell."
107. ↑ Patel, Dylan (October 24, 2022). "How China's Biren Is Attempting To Evade US Sanctions". https://www.semianalysis.com/p/how-chinas-biren-is-attempting-to.
108. ↑ "5 free software to recognise fake AI-generated images" (in it). October 28, 2023. https://www-wired-it.translate.goog/article/software-riconoscere-intelligenza-artificiale-immagini-false/?_x_tr_sl=it&_x_tr_tl=en&_x_tr_hl=it&_x_tr_pto=wapp.
109. ↑ "Detecting AI fingerprints: A guide to watermarking and beyond" (in en-US). January 4, 2024. https://www.brookings.edu/articles/detecting-ai-fingerprints-a-guide-to-watermarking-and-beyond/.
110. ↑ Fowler, Geoffrey (April 3, 2023). "We tested a new ChatGPT-detector for teachers. It flagged an innocent student". https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin.
111. ↑ Fowler, Geoffrey (June 2, 2023). "Detecting AI may be impossible. That's a big problem for teachers.". https://www.washingtonpost.com/technology/2023/06/02/turnitin-ai-cheating-detector-accuracy/.
112. ↑ Jafarigol, Elaheh; Trafalis, Theodore B. (May 5, 2023). Federated Learning with GANs-based Synthetic Minority Over-sampling Technique for Improving Weather Prediction from Imbalanced Data. doi:10.21203/rs.3.rs-2880376/v1.
113. ↑ Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (October 22, 2020). "Generative adversarial networks". Communications of the ACM 63 (11): 139–144. doi:10.1145/3422622. ISSN 0001-0782.
114. ↑ Kingma, Diederik P.; Welling, Max (2019). An Introduction to Variational Autoencoders. Now Publishers. doi:10.1561/9781680836233. ISBN 978-1-68083-622-6.
115. ↑ "RNN vs. CNN: Which Neural Network Is Right for Your Project?" (in en). October 27, 2021. https://www.springboard.com/blog/data-science/rnn-vs-cnn/.
116. ↑ Bartz, Diane; Hu, Krystal (July 21, 2023). "OpenAI, Google, others pledge to watermark AI content for safety, White House says". Reuters. https://www.reuters.com/technology/openai-google-others-pledge-watermark-ai-content-safety-white-house-2023-07-21/.
117. ↑ "FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence". The White House. October 30, 2023. https://bidenwhitehouse.archives.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/.
118. ↑ Burt, Andrew (October 31, 2023). "3 Obstacles to Regulating Generative AI". Harvard Business Review. ISSN 0017-8012. https://hbr.org/2023/10/3-obstacles-to-regulating-generative-ai.
119. ↑ "EU AI Act: first regulation on artificial intelligence" (in en). August 6, 2023. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence.
120. ↑ Chee, Foo Yun; Mukherjee, Supantha (June 14, 2023). "EU lawmakers vote for tougher AI rules as draft moves to final stage" (in en). https://www.reuters.com/technology/eu-lawmakers-agree-changes-draft-artificial-intelligence-rules-2023-06-14/.
121. ↑ Ye, Josh (July 13, 2023). "China says generative AI rules to apply only to products for the public". https://www.reuters.com/technology/china-issues-temporary-rules-generative-ai-services-2023-07-13/.
122. ↑ "生成式人工智能服务管理暂行办法". July 13, 2023. http://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm.
123. ↑ 123.0 123.1 "Generative Artificial Intelligence and Copyright Law". Congressional Research Service. September 29, 2023. https://crsreports.congress.gov/product/pdf/LSB/LSB10922.
124. ↑ Thompson, Stuart (January 25, 2024). "We Asked A.I. to Create the Joker. It Generated a Copyrighted Image.". The New York Times. https://www.nytimes.com/interactive/2024/01/25/business/ai-image-generators-openai-microsoft-midjourney-copyright.html.
125. ↑ Hadero, Haleluya; Bauder, David (December 27, 2023). "The New York Times sues OpenAI and Microsoft for using its stories to train chatbots". Associated Press News (AP News). https://apnews.com/article/nyt-new-york-times-openai-microsoft-6ea53a8ad3efa06ee4643b697df0ba57.
126. ↑ O'Brien, Matt (September 25, 2023). "Photo giant Getty took a leading AI image-maker to court. Now it's also embracing the technology". Associated Press. https://apnews.com/article/getty-images-artificial-intelligence-ai-image-generator-stable-diffusion-a98eeaaeb2bf13c5e8874ceb6a8ce196.
127. ↑ Barber, Gregory (December 9, 2023). "The Generative AI Copyright Fight Is Just Getting Started". Wired. https://www.wired.com/story/livewired-generative-ai-copyright/. Retrieved January 19, 2024.
128. ↑ Bruell, Alexandra (December 27, 2023). "New York Times Sues Microsoft and OpenAI, Alleging Copyright Infringement". Wall Street Journal. https://www.wsj.com/tech/ai/new-york-times-sues-microsoft-and-openai-alleging-copyright-infringement-fd85e1c4.
129. ↑ Brittain, Blake (August 21, 2023). "AI-generated art cannot receive copyrights, US court says". Reuters. https://www.reuters.com/legal/ai-generated-art-cannot-receive-copyrights-us-court-says-2023-08-21/.
130. ↑ "The Lawsuits That Could Shape the Future of AI and Copyright Law". The Wall Street Journal (News Corp). April 15, 2024. https://www.wsj.com/video/series/wsj-explains/the-lawsuits-that-could-shape-the-future-of-ai-and-copyright-law/43D1BBBB-F393-4F18-AA4A-A80CFFA0F8A5.
131. ↑ David, Emilla (August 29, 2023). "US Copyright Office wants to hear what people think about AI and copyright". The Verge. https://www.theverge.com/2023/8/29/23851126/us-copyright-office-ai-public-comments.
132. ↑ "Copyright and Artificial Intelligence | U.S. Copyright Office". https://copyright.gov/ai/.
133. ↑ "U.S. Copyright Office Grants Registration to AI-Generated Artwork" (in en-US). March 24, 2025. https://journals.law.harvard.edu/jsel/2025/03/u-s-copyright-office-grants-registration-to-ai-generated-artwork/.
134. ↑ "Secretary-General's remarks to the Security Council on Artificial Intelligence". July 18, 2023. https://www.un.org/sg/en/content/sg/statement/2023-07-18/secretary-generals-remarks-the-security-council-artificial-intelligence-bilingual-delivered-scroll-down-for-all-english.
135. ↑ 135.0 135.1 Toews, Rob. "Deep Learning's Carbon Emissions Problem" (in en). https://www.forbes.com/sites/robtoews/2020/06/17/deep-learnings-climate-change-problem/.
136. ↑ 136.0 136.1 Heikkilä, Melissa (December 5, 2023). "AI's carbon footprint is bigger than you think" (in en). https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/.
137. ↑ 137.0 137.1 Barrett, A.; Pack, A. (2023). "Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process". International Journal of Educational Technology in Higher Education 20 (1): 59. doi:10.1186/s41239-023-00427-0.
138. ↑ Chan, C. K. Y. (2023). "A comprehensive AI policy education framework for university teaching and learning". International Journal of Educational Technology in Higher Education 20 (1). doi:10.1186/s41239-023-00408-3.
139. ↑ "NCTE Position Statement on Machine Scoring". 20 April 2013. https://ncte.org/statement/machine_scoring/.
140. ↑ Mangal, M.; Pardos, Z. A. (2024). "Implementing equitable and intersectionality-aware AI in education: A practical guide". British Journal of Educational Technology 55 (5): 2003–2038. doi:10.1111/bjet.13484.
141. ↑ "The Writers Strike Is Taking a Stand on AI" (in en). Time. May 4, 2023. https://time.com/6277158/writers-strike-ai-wga-screenwriting/. Retrieved June 11, 2023.
142. ↑ Tarnoff, Ben (August 4, 2023). "Lessons from Eliza". The Guardian Weekly: pp. 34–39.
143. ↑ Zhou, Viola (April 11, 2023). "AI is already taking video game illustrators' jobs in China" (in en-US). https://restofworld.org/2023/ai-image-china-video-game-layoffs/.
144. ↑ Carter, Justin (April 11, 2023). "China's game art industry reportedly decimated by growing AI use" (in en). https://www.gamedeveloper.com/art/china-s-game-art-industry-reportedly-decimated-ai-art-use.
145. ↑ Collier, Kevin (July 14, 2023). "Actors vs. AI: Strike brings focus to emerging use of advanced tech". NBC News. https://www.nbcnews.com/tech/tech-news/hollywood-actor-sag-aftra-ai-artificial-intelligence-strike-rcna94191. "SAG-AFTRA has joined the Writer's [sic] Guild of America in demanding a contract that explicitly demands AI regulations to protect writers and the works they create. ... The future of generative artificial intelligence in Hollywood—and how it can be used to replace labor—has become a crucial sticking point for actors going on strike. In a news conference Thursday, Fran Drescher, president of the Screen Actors Guild-American Federation of Television and Radio Artists (more commonly known as SAG-AFTRA), declared that 'artificial intelligence poses an existential threat to creative professions, and all actors and performers deserve contract language that protects them from having their identity and talent exploited without consent and pay.'"
146. ↑ Wiggers, Kyle (August 22, 2023). "ElevenLabs' voice-generating tools launch out of beta" (in en-US). https://techcrunch.com/2023/08/22/elevenlabs-voice-generating-tools-launch-out-of-beta/.
147. ↑ Shrivastava, Rashi. "'Keep Your Paws Off My Voice': Voice Actors Worry Generative AI Will Steal Their Livelihoods" (in en). https://www.forbes.com/sites/rashishrivastava/2023/10/09/keep-your-paws-off-my-voice-voice-actors-worry-generative-ai-will-steal-their-livelihoods/.
148. ↑ Gupta, Shalene (October 31, 2023). "Underrepresented groups in countries around the world are worried about AI being a threat to jobs". https://www.fastcompany.com/90975180/ai-threat-jobs-fears-survey-seven-countries-job-seekers-hr.
149. ↑ Rachel Gordon (March 3, 2023). "Large language models are biased. Can logic help save them?". MIT CSAIL. https://news.mit.edu/2023/large-language-models-are-biased-can-logic-help-save-them-0303.
150. ↑ OpenAI (July 18, 2022). "Reducing bias and improving safety in DALL·E 2". OpenAI. https://openai.com/blog/reducing-bias-and-improving-safety-in-dall-e-2.
151. ↑ Jake Traylor (July 27, 2022). "No quick fix: How OpenAI's DALL·E 2 illustrated the challenges of bias in AI". https://www.nbcnews.com/tech/tech-news/no-quick-fix-openais-dalle-2-illustrated-challenges-bias-ai-rcna39918.
152. ↑ "DALL·E 2 pre-training mitigations". June 28, 2022. https://openai.com/research/dall-e-2-pre-training-mitigations.
153. ↑ Brandon, John (February 16, 2018). "Terrifying high-tech porn: Creepy 'deepfake' videos are on the rise" (in en-US). Fox News. https://www.foxnews.com/tech/terrifying-high-tech-porn-creepy-deepfake-videos-are-on-the-rise.
154. ↑ Cole, Samantha (January 24, 2018). "We Are Truly Fucked: Everyone Is Making AI-Generated Fake Porn Now". https://www.vice.com/en/article/reddit-fake-porn-app-daisy-ridley/.
155. ↑ "What Are Deepfakes & Why the Future of Porn is Terrifying" (in en-US). Highsnobiety. February 20, 2018. https://www.highsnobiety.com/p/what-are-deepfakes-ai-porn/.
156. ↑ "Experts fear face swapping tech could start an international showdown" (in en). https://theoutline.com/post/3179/deepfake-videos-are-freaking-experts-out.
157. ↑ Roose, Kevin (March 4, 2018). "Here Come the Fake Videos, Too" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2018/03/04/technology/fake-videos-deepfakes.html.
158. ↑ Schreyer, Marco; Sattarov, Timur; Reimer, Bernd; Borth, Damian (2019). "Adversarial Learning of Deepfakes in Accounting". arXiv:1910.03810 [cs.LG].
159. ↑ Menz, Bradley D.; Modi, Natansh D.; Sorich, Michael J.; Hopkins, Ashley M. (January 2024). "Health Disinformation Use Case Highlighting the Urgent Need for Artificial Intelligence Vigilance: Weapons of Mass Disinformation". JAMA Internal Medicine 184 (1): 92–96. doi:10.1001/jamainternmed.2023.5947. PMID 37955873.
160. ↑ Chalfant, Morgan (March 6, 2024). "U.S. braces for foreign interference in 2024 election". https://www.semafor.com/article/03/06/2024/us-braces-for-foreign-interference-in-2024-election.
161. ↑ Menn, Joseph (September 23, 2024). "Russia, Iran use AI to boost anti-U.S. influence campaigns, officials say". The Washington Post. ISSN 0190-8286. https://www.washingtonpost.com/technology/2024/09/23/us-election-foreign-influence-russia-china-iran-ai/.
162. ↑ "New Research Reveals Scale of Threat Posed by AI-generated Images on 2024 Elections". July 27, 2023. https://www.logically.ai/press/new-research-reveals-scale-of-threat-posed-by-ai-generated-images-on-2024-elections.
163. ↑ Lawton, Graham (September 12, 2023). "Disinformation wars: The fight against fake news in the age of AI" (in en-US). https://www.newscientist.com/article/mg25934563-200-disinformation-wars-the-fight-against-fake-news-in-the-age-of-ai/.
164. ↑ "People Are Still Terrible: AI Voice-Cloning Tool Misused for Deepfake Celeb Clips" (in en-ae). January 31, 2023. https://me.pcmag.com/en/news/14327/people-are-still-terrible-ai-voice-cloning-tool-misused-for-deepfake-celeb-clips.
165. ↑ "The generative A.I. software race has begun" (in en). https://fortune.com/2023/01/31/generative-a-i-is-about-to-upend-enterprise-software-and-cybersecurity/.
166. ↑ Milmo, Dan; Hern, Alex (May 20, 2023). "Elections in UK and US at risk from AI-driven disinformation, say experts" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/technology/2023/may/20/elections-in-uk-and-us-at-risk-from-ai-driven-disinformation-say-experts.
167. ↑ "Seeing is believing? Global scramble to tackle deepfakes" (in en-US). https://news.yahoo.com/seeing-believing-global-scramble-tackle-013429757.html.
168. ↑ Vincent, James (January 31, 2023). "4chan users embrace AI voice clone tool to generate celebrity hatespeech" (in en-US). https://www.theverge.com/2023/1/31/23579289/ai-voice-clone-deepfake-abuse-4chan-elevenlabs.
169. ↑ Thompson, Stuart A. (March 12, 2023). "Making Deepfakes Gets Cheaper and Easier Thanks to A.I." (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2023/03/12/technology/deepfakes-cheapfakes-videos-ai.html.
170. ↑ "A new AI voice tool is already being abused to make deepfake celebrity audio clips" (in en-US). January 31, 2023. https://www.engadget.com/ai-voice-tool-deepfake-celebrity-audio-clips-094648743.html.
171. ↑ Gee, Andre (April 20, 2023). "Just Because AI-Generated Rap Songs Go Viral Doesn't Mean They're Good" (in en-US). Rolling Stone. https://www.rollingstone.com/music/music-features/ai-generated-drake-the-weeknd-hip-hop-fandom-1234720440/. Retrieved December 6, 2023.
172. ↑ Coscarelli, Joe (April 19, 2023). "An A.I. Hit of Fake 'Drake' and 'The Weeknd' Rattles the Music World". The New York Times. https://www.nytimes.com/2023/04/19/arts/music/ai-drake-the-weeknd-fake.html.
173. ↑ Lippiello, Emily; Smith, Nathan; Pereira, Ivan (November 3, 2023). "AI songs that mimic popular artists raising alarms in the music industry" (in en). https://abcnews.go.com/US/ai-songs-mimic-popular-artists-raising-alarms-music/story?id=104569841.
174. ↑ Skelton, Eric. "Fans Are Using Artificial Intelligence to Turn Rap Snippets Into Full Songs" (in en-us). https://www.complex.com/music/a/eric-skelton/fans-using-artificial-intelligence-rap-snippets.
175. ↑ Marr, Bernard. "Virtual Influencer Noonoouri Lands Record Deal: Is She The Future Of Music?" (in en). https://www.forbes.com/sites/bernardmarr/2023/09/05/virtual-influencer-noonoouri-lands-record-deal-is-she-the-future-of-music/.
176. ↑ Thaler, Shannon (September 8, 2023). "Warner Music signs first-ever record deal with AI pop star" (in en-US). https://nypost.com/2023/09/08/warner-music-signs-first-ever-record-deal-with-ai-pop-star/.
177. ↑ Alilunas, Peter (2 January 2024). "What we must be: AI and the future of porn studies". Porn Studies 11 (1): 99–112. doi:10.1080/23268743.2024.2312181.
178. ↑ "How AI is being abused to create child sexual abuse material (CSAM) online" (in en-gb). https://www.iwf.org.uk/about-us/why-we-exist/our-research/how-ai-is-being-abused-to-create-child-sexual-abuse-imagery/.
179. ↑ "Ban AI apps creating naked images of children, says children's commissioner" (in en-GB). April 28, 2025. https://www.bbc.com/news/articles/cr78pd7p42ro.
180. ↑ Sjouwerman, Stu (December 26, 2022). "Deepfakes: Get ready for phishing 2.0". Fast Company. https://www.fastcompany.com/90829233/deepfakes-get-ready-for-phishing-2-0.
181. ↑ Sonnemaker, Tyler. "As social media platforms brace for the incoming wave of deepfakes, Google's former 'fraud czar' predicts the biggest danger is that deepfakes will eventually become boring" (in en-US). https://www.businessinsider.com/google-ex-fraud-czar-danger-of-deepfakes-is-becoming-boring-2020-1.
182. ↑ Collinson, Patrick (July 15, 2023). "Fake reviews: can we trust what we read online as use of AI explodes?" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/money/2023/jul/15/fake-reviews-ai-artificial-intelligence-hotels-restaurants-products.
183. ↑ "After WormGPT, FraudGPT Emerges to Help Scammers Steal Your Data" (in en). July 25, 2023. https://www.pcmag.com/news/after-wormgpt-fraudgpt-emerges-to-help-scammers-steal-your-data.
184. ↑ Gupta, Maanak; Akiri, Charankumar; Aryal, Kshitiz; Parker, Eli; Praharaj, Lopamudra (2023). "From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy". IEEE Access 11: 80218–80245. doi:10.1109/ACCESS.2023.3300381. Bibcode: 2023IEEEA..1180218G.
185. ↑ Piper, Kelsey (February 2, 2024). "Should we make our most powerful AI models open source to all?" (in en-US). https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake.
186. ↑ Kaufman, Arthur (2025-09-08). "AI Assists Chinese External Propaganda" (in en-US). https://chinadigitaltimes.net/2025/09/ai-assists-chinese-external-propaganda/.
187. ↑ Metz, Cade (July 10, 2023). "In the Age of A.I., Tech's Little Guys Need Big Friends". New York Times. https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html.
188. ↑ 188.0 188.1 Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (March 1, 2021). "On the Dangers of Stochastic Parrots: Can Language Models be Too Big? 🦜". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7. https://dl.acm.org/doi/10.1145/3442188.3445922. Retrieved August 27, 2024.
189. ↑ 189.0 189.1 189.2 "AI is an energy hog. This is what it means for climate change." (in en). May 23, 2024. https://www.technologyreview.com/2024/05/23/1092777/ai-is-an-energy-hog-this-is-what-it-means-for-climate-change/.
190. ↑ 190.0 190.1 190.2 190.3 190.4 190.5 190.6 Dhar, Payal (August 1, 2020). "The carbon impact of artificial intelligence" (in en). Nature Machine Intelligence 2 (8): 423–425. doi:10.1038/s42256-020-0219-9. ISSN 2522-5839. https://www.nature.com/articles/s42256-020-0219-9.
191. ↑ 191.0 191.1 191.2 191.3 Crawford, Kate (February 20, 2024). "Generative AI's environmental costs are soaring — and mostly secret" (in en). Nature 626 (8000): 693. doi:10.1038/d41586-024-00478-x. PMID 38378831. Bibcode: 2024Natur.626..693C. https://www.nature.com/articles/d41586-024-00478-x.
192. ↑ 192.0 192.1 192.2 Rogers, Reece. "AI's Energy Demands Are Out of Control. Welcome to the Internet's Hyper-Consumption Era" (in en-US). Wired. ISSN 1059-1028. https://www.wired.com/story/ai-energy-demands-water-impact-internet-hyper-consumption-era/. Retrieved August 27, 2024.
193. ↑ 193.0 193.1 193.2 193.3 193.4 193.5 Saenko, Kate (May 23, 2023). "Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins" (in en-US). https://theconversation.com/is-generative-ai-bad-for-the-environment-a-computer-scientist-explains-the-carbon-footprint-of-chatgpt-and-its-cousins-204096.
194. ↑ 194.0 194.1 Lohr, Steve (August 26, 2024). "Will A.I. Ruin the Planet or Save the Planet?" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2024/08/26/climate/ai-planet-climate-change.html.
195. ↑ Ding, Zhaohao; Wang, Jianxiao; Song, Yiyang; Zheng, Xiaokang; He, Guannan; Chen, Xiupeng; Zhang, Tiance; Lee, Wei-Jen et al. (May 5, 2025). "Tracking the carbon footprint of global generative artificial intelligence". The Innovation 6 (5). doi:10.1016/j.xinn.2025.100866. ISSN 2666-6758. PMID 40432776. Bibcode: 2025Innov...600866D.
196. ↑ Pelton, Rylie E. O.; Kazanski, Clare E.; Keerthi, Shamitha; Racette, Kelly A.; Gennet, Sasha; Springer, Nathaniel; Yacobson, Eugene; Wironen, Michael et al. (September 2024). "Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures" (in en). Nature Food 5 (9): 787–797. doi:10.1038/s43016-024-01031-9. ISSN 2662-1355. PMID 39215094.
197. ↑ Hoffman, Benjamin (June 11, 2024). "First Came 'Spam.' Now, With A.I., We've Got 'Slop'" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2024/06/11/style/ai-search-slop.html.
198. ↑ 198.0 198.1 "Investigation Finds Actual Source of All That AI Slop on Facebook". August 10, 2024. https://futurism.com/the-byte/source-ai-slop-facebook.
199. ↑ 199.0 199.1 Warzel, Charlie (August 21, 2024). "The MAGA Aesthetic Is AI Slop" (in en). https://www.theatlantic.com/technology/archive/2024/08/trump-posts-ai-image/679540/.
200. ↑ Edwards, Benj (August 14, 2024). "Research AI model unexpectedly attempts to modify its own code to extend runtime" (in en-us). https://arstechnica.com/information-technology/2024/08/research-ai-model-unexpectedly-modified-its-own-code-to-extend-runtime/.
201. ↑ Hern, Alex; Milmo, Dan (May 19, 2024). "Spam, junk … slop? The latest wave of AI behind the 'zombie internet'" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/technology/article/2024/may/19/spam-junk-slop-the-latest-wave-of-ai-behind-the-zombie-internet.
202. ↑ Cox, Joseph (January 18, 2024). "Google News Is Boosting Garbage AI-Generated Articles" (in en). https://www.404media.co/google-news-is-boosting-garbage-ai-generated-articles/.
203. ↑ "Beloved Local Newspapers Fired Staffers, Then Started Running AI Slop". July 31, 2024. https://futurism.com/the-byte/newspaper-fired-staff-ai-slop.
204. ↑ Thompson, Brian; Dhaliwal, Mehak; Frisch, Peter; Domhan, Tobias; Federico, Marcello (August 2024). Ku, Lun-Wei; Martins, Andre; Srikumar, Vivek. eds. "A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism". Findings of the Association for Computational Linguistics ACL 2024 (Bangkok, Thailand and virtual meeting: Association for Computational Linguistics): 1763–1775. doi:10.18653/v1/2024.findings-acl.103. https://aclanthology.org/2024.findings-acl.103/. Retrieved August 27, 2024.
205. ↑ Roscoe, Jules (January 17, 2024). "A 'Shocking' Amount of the Web Is Already AI-Translated Trash, Scientists Determine" (in en-US). https://www.vice.com/en/article/a-shocking-amount-of-the-web-is-already-ai-translated-trash-scientists-determine/.
206. ↑ Koebler, Jason (September 19, 2024). "Project Analyzing Human Language Usage Shuts Down Because 'Generative AI Has Polluted the Data'" (in en). https://www.404media.co/project-analyzing-human-language-usage-shuts-down-because-generative-ai-has-polluted-the-data/. ""While there has always been spam on the internet and in the datasets that Wordfreq used, "it was manageable and often identifiable. Large language models generate text that masquerades as real language with intention behind it, even though there is none, and their output crops up everywhere," she wrote. She gives the example that ChatGPT overuses the word "delve," in a way that people do not, which has thrown off the frequency of this specific word.""
207. ↑ Gray, Andrew (March 24, 2024). "ChatGPT "contamination": estimating the prevalence of LLMs in the scholarly literature". arXiv:2403.16887 [cs.DL].
208. ↑ Kannan, Prabha (May 13, 2024). "How Much Research Is Being Written by Large Language Models?" (in en). Stanford University. https://hai.stanford.edu/news/how-much-research-being-written-large-language-models.
209. ↑ Shumailov, Ilia; Shumaylov, Zakhar; Zhao, Yiren; Papernot, Nicolas; Anderson, Ross; Gal, Yarin (July 2024). "AI models collapse when trained on recursively generated data" (in en). Nature 631 (8022): 755–759. doi:10.1038/s41586-024-07566-y. PMID 39048682. Bibcode: 2024Natur.631..755S.
210. ↑ Bhatia, Aatish (August 26, 2024). "When A.I.'s Output Is a Threat to A.I. Itself" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/interactive/2024/08/26/upshot/ai-synthetic-data.html?te=1&nl=the-morning&emc=edit_nn_20240826.
211. ↑ "Self-Consuming Generative Models Go Mad". ICLR. 2024. https://openreview.net/pdf?id=ShjMHfmPs0.
212. ↑ Owen, Sean (April 12, 2023). "Synthetic Data for Better Machine Learning". https://www.databricks.com/blog/2023/04/12/synthetic-data-better-machine-learning.html.
213. ↑ Sharma, Himanshu (July 11, 2023). "Synthetic Data Platforms: Unlocking the Power of Generative AI for Structured Data". https://www.kdnuggets.com/2023/07/synthetic-data-platforms-unlocking-power-generative-ai-structured-data.html.
214. ↑ Stöckl, Andreas (November 2, 2022). "Evaluating a Synthetic Image Dataset Generated with Stable Diffusion". arXiv:2211.01777 [cs.CV].
215. ↑ Mullin, Benjamin; Grant, Nico (July 20, 2023). "Google Tests A.I. Tool That Is Able to Write News Articles" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2023/07/19/business/google-artificial-intelligence-news-articles.html.
216. ↑ Roth, Emma (January 25, 2023). "CNET found errors in more than half of its AI-written stories". The Verge. https://www.theverge.com/2023/1/25/23571082/cnet-ai-written-stories-errors-corrections-red-ventures.
217. ↑ "A magazine touted Michael Schumacher's first interview in years. It was actually AI". NPR. April 28, 2023. https://www.npr.org/2023/04/28/1172473999/michael-schumacher-ai-interview-german-magazine.
218. ↑ "Meet AdVon, the AI-Powered Content Monster Infecting the Media Industry". 2024-05-08. https://futurism.com/advon-ai-content.
219. ↑ Menn, Joseph (2025-04-17). "Russia seeds chatbots with lies. Any bad actor could game AI the same way." (in en-US). The Washington Post. ISSN 0190-8286. https://www.washingtonpost.com/technology/2025/04/17/llm-poisoning-grooming-chatbots-russia/.
220. ↑ Newman, Nic; Fletcher, Richard; Robertson, Craig T.; Arguedas, Amy Ross; Nielsen, Rasmus Fleis (June 2024). "Digital News Report 2024". Reuters Institute for the Study of Journalism. p. 20. doi:10.60625/risj-vy6n-4v57. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2024-06/DNR 2024 Final lo-res-compressed.pdf.
221. ↑ Carpenter, Nicole (2025-06-26). "A real issue: video game developers are being accused of using AI – even when they aren't" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/games/2025/jun/26/video-game-developers-using-ai-even-when-they-arent-stamina-zero.
222. ↑ Covington; Luo, Burling LLP-Yan; Dan, Xuezi (2025-03-18). "China Releases New Labeling Requirements for AI-Generated Content" (in en). https://www.lexology.com/library/detail.aspx?g=1e66cce9-1936-4e63-96f0-5fdf378a8a04.
223. ↑ Holden, Michael (2025-02-25). "AI-detection software isn't the solution to classroom cheating — assessment has to shift" (in en-US). http://theconversation.com/ai-detection-software-isnt-the-solution-to-classroom-cheating-assessment-has-to-shift-246102.
224. ↑ Ha, Anthony (2024-08-04). "OpenAI says it's taking a 'deliberate approach' to releasing tools that can detect writing from ChatGPT" (in en-US). https://techcrunch.com/2024/08/04/openai-says-its-taking-a-deliberate-approach-to-releasing-tools-that-can-detect-writing-from-chatgpt/.
225. ↑ Barnum, Deepa Seetharaman and Matt (August 4, 2024). "Exclusive | There's a Tool to Catch Students Cheating With ChatGPT. OpenAI Hasn't Released It." (in en-US). https://www.wsj.com/tech/ai/openai-tool-chatgpt-cheating-writing-135b755a.
226. ↑ Peters, Jay (2025-05-21). "Google has a new tool to help detect AI-generated content" (in en-US). https://www.theverge.com/news/672013/google-synthid-detector-ai-generated-content-watermark-i-o-2025.

## Further reading

* He, Ran; Cao, Jie; Tan, Tieniu (2025). "Generative Artificial Intelligence: A Historical Perspective". National Science Review 12 (5). doi:10.1093/nsr/nwaf050. PMID 40191253.
* James Gleick, "The Parrot in the Machine" (review of Emily M. Bender and Alex Hanna, The AI Con: How to Fight Big Tech's Hype and Create the Future We Want, Harper, 274 pp.; and James Boyle, The Line: AI and the Future of Personhood, MIT Press, 326 pp.), The New York Review of Books, 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 plagiarism). 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 Heat, he received this 'AI Overview': 'No, Angelina Jolie is not in heat.'" (p. 44.)

* v
* t
* e

Generative AI

Concepts|

* Autoencoder
* Deep learning
* Fine-tuning
* Foundation model
* Generative adversarial network
* Generative pre-trained transformer
* Large language model
* Model Context Protocol
* Neural network
* Prompt engineering
* Reinforcement learning from human feedback
* Retrieval-augmented generation
* Self-supervised learning
* Stochastic parrot
* Synthetic data
* Top-p sampling
* Transformer
* Variational autoencoder
* Vibe coding
* Vision transformer
* Waluigi effect
* Word embedding

Models|

| Text|

* Character.ai
* ChatGPT
* Command A
* Claude
* DeepSeek
* Ernie
* Gemini
* Gemma
* GLM
* GPT
* 1
* 2
* 3
* J
* 3.5
* 4
* 4o
* o1
* o3
* 4.5
* 4.1
* o4-mini
* OSS
* 5
* 5.1
* Grok
* Hunyuan T1
* Kimi
* Llama
* Microsoft Copilot
* MiniMax M2
* Mistral Medium
* Qwen
* Velvet
* Solar Pro

|

Coding|

* Base44
* Claude Code
* Cursor
* Devstral
* GitHub Copilot
* Grok Code Fast 1
* Kimi-Dev
* Qwen3-Coder
* Replit

Image|

* Aurora
* Firefly
* Flux
* GPT Image 1
* Ideogram
* Imagen
* Leonardo
* Midjourney
* Qwen-Image
* Recraft
* Seedream
* Stable Diffusion

Video|

* Dream Machine
* Hailuo AI
* Kling
* Runway Gen
* Seedance
* Sora
* Veo
* Wan

Speech|

* 15.ai
* Eleven
* Gemini Speech
* GPT-4o mini TTS
* MiniMax Speech
* Speechify

Music|

* Eleven Music
* Endel
* Lyria
* MiniMax Music
* Riffusion
* Stable Audio
* Suno
* Udio

Agents|

* Agentforce
* AutoGLM
* AutoGPT
* ChatGPT Agent
* Devin AI
* Manus
* MiniMax Agent
* OpenAI Codex
* Operator
* Replit Agent

Controversies|

* Deepfake pornography
* Generative AI pornography
* Taylor Swift deepfake pornography controversy
* Google Gemini image generation controversy
* Pause Giant AI Experiments
* Removal of Sam Altman from OpenAI
* Statement on AI Risk
* Tay (chatbot)
* Théâtre D'opéra Spatial
* Voiceverse NFT plagiarism scandal

* v
* t
* e

Digital art

Tools|

| Hardware|

* Computer
* CGI
* 2D graphics
* 3D graphics
* Xerox
* 3D printer

|

Software|

* Graphic art software
* Fractal-generating software
* Animation software

Forms|

* Art game
* Computer art scene
* Computer music
* Cyberarts
* Digital illustration
* Digital imaging
* Digital painting
* Digital photography
* Digital poetry
* Digital architecture
* Electronic music
* Evolutionary art
* Fractal art
* Generative art
* Generative music
* GIF art
* Immersion
* Interactive art
* Internet art
* Motion graphics
* Music visualization
* Photo manipulation
* Pixel art
* Render art
* Software art
* Systems art
* Texture mapping
* Tradigital art

Notable Artworks|

* Edmond de Belamy
* Barnsley fern
* A Bird in Flight
* Boat
* Jesus Dress Up
* Remember To Rise

Organizations and conferences|

* Artfutura
* Artmedia
* Austin Museum of Digital Art
* Computer Arts Society
* EVA Conferences
* Los Angeles Center for Digital Art
* Lumen Prize
* onedotzero
* V&A Digital Futures

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