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Generative AI 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.

Most generative AI is powered by deep learning 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.

This technology has seen rapid growth in sophistication and popularity in recent years, especially since the release of ChatGPT in November 2023. The ability to generate content on demand has major implications in a wide variety of contexts, such as academia and creative industries.

How does generative AI work?

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, computer systems that are designed to imitate the structures of brains.

Highly complex neural networks are the basis for large language models (LLMs), 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 (text typed in by the user).

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. 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.

LLMs, especially a specific type of LLM called a generative pre-trained transformer (GPT), are used in most current generative AI applications—including 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.

Types of generative AI

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.

Strengths and limitations of generative AI

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.

Strengths

Generative AI technology is often flexible and can generalize to a variety of tasks rather than specializing in just one. This opens up opportunities to explore its use in a wide range of contexts.

This technology can make any business processes that involve generating text or other content (e.g., writing emails, planning projects, creating images) dramatically more efficient, allowing small teams to accomplish more and bigger teams to focus on more ambitious projects.

Generative AI tools allow non-experts to approach tasks they would normally be unable to handle. This allows people to explore areas of creativity and work that were previously inaccessible to them.

Limitations

Generative AI models often hallucinate—for example, a chatbot’s answers might be factually incorrect, or an image generator’s outputs might contain incongruous details like too many fingers on a person’s hand. Outputs should always be checked for accuracy and quality.

These tools are trained on datasets that may be biased in various ways (e.g., sexism), and the tools can therefore reproduce those biases. For example, an image generator asked to provide an image of a CEO may be more likely to show a man than a woman.

Although they’re trained on large datasets and draw on all that data for their responses, generative AI tools generally can’t tell you what sources they’re using in a specific response. This means it can be difficult to trace the sources of, for example, factual claims or visual elements.

Implications of generative AI

The rise of generative AI raises a lot of questions about the effects—positive or negative—that different applications of this technology could have on a societal level. Commonly discussed issues include:

Jobs and automation: Many people are concerned about the effects of generative AI on various creative jobs. For example, will it be harder for illustrators to find work when they have to compete with image generators? Others claim that these tools will force various industries to adapt but also create new roles as existing tasks are automated.

Effects on academia: Many academics are concerned about ChatGPT cheating among their students and about the lack of clear guidelines on how to approach these tools. University policies on AI writing are still developing.

Plagiarism and copyright concerns: Some argue that generative AI’s use of sources from its training data should be treated as plagiarism or copyright infringement. For example, some artists have attempted legal action against AI companies, arguing that image generators use elements of their work and stylistic approach without acknowledgement or compensation.

Fake news and scams: Generative AI tools can be used to deliberately spread misinformation (e.g., deepfake videos) or enable scams (e.g., imitating someone’s voice to steal their identity). They can also spread misinformation by accident if people assume, for example, that everything ChatGPT claims is factually correct without checking it against a credible source.

Future developments: There is a lot of uncertainty about how AI is likely to develop in the future. Some argue that the rapid developments in generative AI are a major step towards artificial general intelligence (AGI), while others suspect that we’re reaching the limits of what can be done with current approaches to AI and that future innovations will use very different techniques.