Using Generative AI

There are many different types of generative AI that can create text, images, sound, video, and more. This page describes common types of generative AI and includes examples of tools.

Text generators

Text-based generative AI tools create new text that is similar to the data they were trained on. The training process for these AI chatbots involves consuming large amounts of text from data from webpages, books, and other sources, then analyzing the text to find patterns and relationships in human language. Because of this training process, these tools are commonly referred to as Large Language Models (LLMs). They use probability to predict which words should appear in sequence. As Stephen Wolfram explained, “it’s just saying things that ‘sound right’ based on what things ‘sounded like’ in its training material.”

AI chatbots can produce essays, blogs, scripts, news articles, reflective statements, and even poetry. 

Some chatbots rely on their training data to produce content, while others are grounded in a source of facts.

Examples of generative AI that can create text content include: ChatGPT, Perplexity AI, and Microsoft Copilot.

Image generators

This type of AI learns through analyzing datasets of images with captions or text descriptions. If it knows what two different concepts are, like a cat and a skateboard, it can merge those concepts together when prompted to create an image of a cat on a skateboard.

Generative AI image tools can produce diverse images in a range of media, everything from photorealistic oil painting style to anime.

Examples of generative AI that can create imagery include: DALL·E, Midjourney, and Stable Diffusion.

Sound and music generators

AI music generators analyze music tracks and metadata (artist name, album title, genre, year song was released, associated playlists) to identify patterns and features in particular music genres. They may also be trained on song lyrics. If a music generator has only been exposed to one type of music (e.g., classical), then the music it generates will sound similar to those works.

Examples of generative AI that can create audio content include: AIVA and Soundful.

Video generators

Creating a video typically requires the use of audio, visual, and text elements. Some generative AI video programs have harvested existing videos to learn how to create new ones, while others have sourced the three elements to create video from audio, visual, and text sources. There are even generative AI video programs that have been trained to use video editing software, so they can apply effects to a video that you have created, such as adding captions, transitions, and animations.

Examples of generative AI that can create videos include: Runway Gen-1 and Invideo.

Research discovery and explanation generators

Some generative AI tools can automate parts of the research process and make long, complex texts easier to decipher. This type of AI often analyzes research papers that users upload to extract key information or summarize a paper.

Examples of generative AI that can support research discovery and generate explanations include: Elicit and Scite.