User:AdvaitPanicker/Generative artificial intelligence

History
The academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956, and has experienced several waves of advancement and optimism in the decades since. Since its founding, researchers in the field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. These concepts of automated art date back at least to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music. The tradition of creative automatons has flourished throughout history, such as Maillardet's automaton, created in the early 1800s.

Artificial Intelligence is an idea that has been captivating society since the mid-20th century. It began with science fiction familiarizing the world with the concept but the idea wasn't fully seen in the scientific manner until Alan Turing, a polymath, was curious about the feasibility of the concept. The development of AI was not very rapid at first because of the high costs and the fact that computers were not able to store commands. This all changed during the 1956 Dartmouth Summer Research Project on AI where there was inspiring call for AI research which led it to be a landmark event as it set the precedent for two decades of rapid advancements in the field. (Anyoha).

Since the founding of AI in the 1950s, artists and researchers have used artificial intelligence to create artistic works. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON, the computer program Cohen created to generate paintings.

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, and analyzed the pattern of vowels and consonants in the novel Eugeny Onegin using Markov chains. Once a Markov chain is learned on a text corpus, it can then be used as a probabilistic text generator.

The field of machine learning often uses statistical models, including generative models, to model and predict data. Beginning in the late 2000s, the emergence of deep learning drove progress and research 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.

In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative, rather than discriminative, models of complex data such as images. These deep generative models were the first able to output not only class labels for images, but to output entire images.

In 2017, the Transformer network enabled advancements in generative models compared to older Long-Short Term Memory models, leading to the first generative pre-trained transformer (GPT), known as GPT-1, in 2018. This was followed in 2019 by GPT-2 which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model.

In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts.

In March 2023, GPT-4 was released. A team from Microsoft Research argued that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system". Other scholars have disputed that GPT-4 reaches this threshold, calling generative AI "still far from reaching the benchmark of ‘general human intelligence’" as of 2023.

Audio deepfakes
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. In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and identity verification.

Concerns and fandom have spawned from AI generated music. The same software used to clone voices have been used on famous musicians' voices to create songs which mimic their voices, gaining both tremendous popularity and criticism. Similar techniques have also been used to create improved quality or full-length versions of songs that have been leaked or have yet to been released.

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. 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.

Cybercrime
Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams. Deepfake video and audio have been used to create disinformation and fraud. Former Google fraud czar Shuman Ghosemajumder has predicted that while deepfake videos initially created a stir in the media, they would soon become commonplace, and as a result, more dangerous. Additionally, large-language models and other forms of text-generation AI have been at a broad scale to create fake reviews on ecommerce websites to boost ratings. Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.

Recent research done in 2023 has revealed that generative AI has weaknesses that can be manipulated by criminals to extract harmful information bypassing ethical safeguards. The study presents example attacks done on ChatGPT including Jailbreaks and reverse psychology. Additionally, malicious individuals can use ChatGPT for social engineering attacks and phishing attacks, revealing the harmful nature of these technologies.

https://doi.org/10.48550%2Farxiv.2303.04226

https://www.rollingstone.com/music/music-features/ai-generated-drake-the-weeknd-hip-hop-fandom-1234720440/?sub_action=logged_in

https://www.nytimes.com/2023/04/19/arts/music/ai-drake-the-weeknd-fake.html

https://abcnews.go.com/US/ai-songs-mimic-popular-artists-raising-alarms-music/story?id=104569841

https://www.complex.com/music/a/eric-skelton/fans-using-artificial-intelligence-rap-snippets

https://www.forbes.com/sites/bernardmarr/2023/09/05/virtual-influencer-noonoouri-lands-record-deal-is-she-the-future-of-music/

https://nypost.com/2023/09/08/warner-music-signs-first-ever-record-deal-with-ai-pop-star/

https://www.theguardian.com/money/2023/jul/15/fake-reviews-ai-artificial-intelligence-hotels-restaurants-products