GENERATIVE ARTIFICIAL INTELLIGENCE
Generative artificial intelligence (generative AI, GenAI, or GAI) is artificial intelligence capable of generating text, images or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristic
Improvements in transformer-based deep neural networks enabled an AI boom of generative AI systems in the early 2020s. These include large language model (LLM) chatbots such as ChatGPT, Copilot, Gemini and LLaMA, text-to-image artificial intelligence art systems such as Stable Diffusion, Midjourney and DALL-E, and text-to-video AI generators such as Sora. Companies such as OpenAI, Anthropic, Microsoft, Google, and Baidu as well as numerous smaller firms have developed generative AI models.
Generative AI has uses across a wide range of industries, including software development, healthcare, finance, entertainment, customer service sales and marketing, art, writing, fashion, and product design. However, concerns have been raised about the potential misuse of generative AI such as cybercrime, the use of fake news or deepfakes to deceive or manipulate people, and the mass replacement of human jobs.
The academic discipline of artificial intelligence was established at a research workshop held at Dartmouth College in 1956 and has experienced several waves of advancement and optimism in the decades since. Since its inception, 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. The concept of automated art dates 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, exemplified by 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. Turing's groundbreaking 1950 paper, "Computing Machinery and Intelligence," posed fundamental questions about machine reasoning similar to human intelligence, significantly contributing to the conceptual groundwork of AI. 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 changed during the 1956 Dartmouth Summer Research Project on AI where there was an 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.
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 models, as opposed to discriminative ones, for complex data such as images. These deep generative models were the first to output not only class labels for images but also entire images.
In 2017, the 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.