{"id":500194,"date":"2023-07-04T10:22:45","date_gmt":"2023-07-04T08:22:45","guid":{"rendered":"https:\/\/www.scribbr.nl\/?p=500194"},"modified":"2023-08-15T15:43:27","modified_gmt":"2023-08-15T13:43:27","slug":"generative-ai","status":"publish","type":"post","link":"https:\/\/www.scribbr.com\/ai-tools\/generative-ai\/","title":{"rendered":"What Is Generative AI? | Meaning & Examples"},"content":{"rendered":"

Generative AI <\/strong>is the use of artificial intelligence (AI) systems to generate original media such as text, images, video, or audio in response to prompts from users. Popular generative AI applications include ChatGPT, Bard, DALL-E, and Midjourney.<\/p>\n

Most generative AI is powered by deep learning<\/a> technologies such as large language models (LLMs). These are models trained on a vast quantity of data (e.g., text) to recognize patterns so that they can produce appropriate responses to the user’s prompts.<\/p>\n

This technology has seen rapid growth in sophistication and popularity in recent years, especially since the release of ChatGPT in November 2022. The ability to generate content on demand has major implications in a wide variety of contexts, such as academia and creative industries.<\/p>\n

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How does generative AI work?<\/h2>\n

Generative AI is a broad concept that can theoretically be approached using a variety of different technologies. In recent years, though, the focus has been on the use of neural networks<\/strong><\/a>, computer systems that are designed to imitate the structures of brains.<\/p>\n

Highly complex neural networks are the basis for large language models (LLMs)<\/strong><\/a>, which are trained to recognize patterns in a huge quantity of text (billions or trillions of words) and then reproduce them in response to prompts<\/a> (text typed in by the user).<\/p>\n

An LLM generates each word of its response by looking at all the text that came before it and predicting a word that is relatively likely to come next based on patterns it recognizes from its training data<\/a>. You can think of it as a supercharged version of predictive text. The fact that it generally works so well seems to be a product of the enormous amount of data it was trained on.<\/p>\n

LLMs, especially a specific type of LLM called a generative pre-trained transformer (GPT)<\/strong><\/a>, are used in most current generative AI applications\u2014including many that generate something other than text (e.g., image generators like DALL-E). This means that things like images, music, and code can be generated based only on a text description of what the user wants.<\/p>\n

Types of generative AI<\/h2>\n

Generative AI has a variety of different use cases and powers several popular applications. The table below indicates the main types of generative AI application and provides examples of each.<\/p>\n\n\n\n\n\n\n\n\n\n\n
Types of generative AI<\/caption>\n
Generates …<\/th>\nDescription<\/th>\nExample applications<\/th>\n<\/tr>\n<\/thead>\n
Text<\/th>\nChatbots<\/a>, text generators, or AI writing tools<\/a> generate new text based on a prompt from the user, whether this is an answer to the user’s question or, for example, a summary<\/a>, translation, or paraphrase<\/a> of the prompt. Sometimes, these chatbots are integrated into search engines to provide a more advanced search experience.<\/td>\nChatGPT<\/a>, QuillBot Paraphraser<\/a>, Scribbr Text Summarizer<\/a>,\u00a0Bard<\/a>, Bing AI<\/a>, DeepL Translator<\/a><\/td>\n<\/tr>\n
Code<\/th>\nAs well as natural languages (e.g., English, Chinese), generative AI can also output text in various programming<\/a> languages (e.g., JavaScript). Some chatbots like ChatGPT can already do this, but there are also more specialized applications designed to work exclusively with code.<\/td>\nOpenAI Codex<\/a>, GitHub Copilot<\/a><\/td>\n<\/tr>\n
Images<\/th>\nLLMs have been found to be surprisingly versatile and can sometimes also be used in a modified form to generate images rather than text. These apps generally take a text-based prompt from a user (e.g., “The Mona Lisa<\/em> in the style of Van Gogh”) and turn it into an image. Some instead modify user-submitted images.<\/td>\nDALL-E<\/a>, Midjourney<\/a>, Stable Diffusion<\/a>, Prisma<\/a><\/td>\n<\/tr>\n
Video<\/th>\nGenerative AI applications that can create whole videos have also started to appear. These videos are not necessarily very smooth or coherent yet, but the technology is improving.<\/td>\nSynthesia<\/a>, Make-a-Video<\/a>, Gen-2<\/a><\/td>\n<\/tr>\n
Audio<\/th>\nGenerative AI is starting to be used to generate music and synthesized voices. For example, these tools might create a song based on a text description or generate audio of a specific voice reading the words the user inputs.<\/td>\nMusicLM<\/a>, MusicGen<\/a>, MuseNet<\/a>, Murf AI<\/a><\/td>\n<\/tr>\n
Other<\/th>\nThe application of generative AI is also being explored in other contexts. It has potential, for example, in the hard sciences (e.g., predicting protein structures) and in robotics (e.g., turning text prompts into actions carried out by the robot). Further applications will emerge over time.<\/td>\nAlphaFold<\/a>, UniPi<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Strengths and limitations of generative AI<\/h2>\n

Generative AI is a powerful and rapidly developing field of technology, but it’s still a work in progress. It’s important to understand what it excels at and what it tends to struggle with so far.<\/p>\n

Strengths<\/h3>\n