This guide provides a basic overview of generative AI, including ethical issues and potential costs, in order to help you make informed choices about using AI tools.
Generative AI refers to AI models that can create new content, such as text, images, code, audio, or video.
Compared to other types of AI, generative AI can be understood as AI that "generates," rather than "discriminates." (1) AI that "discriminates" is used in classification and prediction tasks, like image recognition.
Generative modeling can be defined as:
"a branch of machine learning that involves training a model to produce new data that is similar to a given dataset." (2, emphasis added)
Here's a plain language definition from The New York Times:
"Generative A.I.: Technology that creates content — including text, images, video and computer code — by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics. Examples include ChatGPT for text and DALL-E and Midjourney for images." (3, emphasis added)
How does it work?
Generative AI models generate content by "statistically analysing the distribution of words, pixels or other elements in the data it has ingested and identifying and repeating common patterns."(4)
Generative and discriminative AI may also be used in combination, in order to refine and iteratively improve outputs, in an approach called Generative Adversarial Networks (GANs).
1. What is Gen AI and How is it Impacting Education? A Presentation by Scott Alfeld, Assistant Professor of Computer Science, Amherst College. Sept 13, 2023.
2. Foster, David. Generative Deep Learning : Teaching Machines to Paint, Write, Compose, and Play. Second edition., O’Reilly Media, Incorporated, 2023.
3. Pasick, Adam. “Artificial Intelligence Glossary: Neural Networks and Other Terms Explained.” The New York Times, 27 Mar. 2023. NYTimes.com, https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html.
4. Guidance for generative AI in education and research. UNESCO. 2023
Text AI / Chatbots
Users provide text or voice prompts to these tools, which are designed to provide fluent, conversational responses. Popular examples include ChatGPT, Google Bard, Microsoft Bing Chat.
Users provide descriptions of images or image effects, with options to modify the output in various ways. Popular examples include DALL-E, Midjourney, Stable Diffusion, and Firefly.
Users can provide specifications to generate code, or review and check code along specific criteria.
Video, audio AI
Users can provide prompts to generate videos or create video effects. Users can provide text to render in audio, or apply effects to audio, including rendering in different "voices."
These tools are continuing to develop, with new integrations in text-based tools such as voice prompting and image recognition. There are also smaller scale models being developed as open source projects.
While generative AI tools may seem intuitive, it can be difficult to get them to produce exactly the output you want. They are often sensitive to slight wording changes in a prompt (ex: "fair" instead of "just"), and may produce different outputs to the same prompt over time.
Prompt-engineering refers to techniques to get generative AI to produce what a user wants in an output. Some general recommendations include:
Guidance for generative AI in education and research. UNESCO. 2023
You can try crafting prompts that include context and specificity by using the PREP framework.
The AI Classroom. 2023. Dan Fitzpatrick, Amanda Fox, Brad Weinstein.
Learn Prompting - an open source curriculum with levels from beginner to advanced to help you learn how to communicate with AI systems
Guidelines for effective prompts are likely to shift as systems develop! Trying multiple prompts and making adjustments based on your outputs is a good first step.