What is Generative Artificial Intelligence?

Generative AI (GenAI), is a class of computer algorithms able to create digital content – including text, images, video, music, and computer code. They work by deriving patterns from large sets of training data that become encoded into predictive mathematical models, a process commonly referred to as ‘learning’. GenAI models do not keep a copy of the data they were trained on, but rather generate novel content entirely from the patterns they encode. People can then use GenAI chatbot interfaces like ChatGPT to input prompts – typically instructions in plain language – to make GenAI models produce new content.

As the development of practical and high-quality generative AI emerges, it can become a helpful tool for our everyday work and has the potential for diverse applications such as art, writing, and software development.

The core of GenAI is a trained deep-learning model that understands and generates text, image, or other media in a human-like fashion based on a given user input, i.e. prompt. This model is trained on massive amounts of data to learn from patterns in the data. For example, it would learn that certain words tend to follow others, or that certain phrases are more common in certain contexts. The model uses the prompt to produce a completion, which is then presented back to users.

The following short video below provides a simple explanation of the mechanism of GenAI.

The quality of the generated output depends on several factors, including the amount and quality of the training data, the prompt’s complexity, and the model’s size. Larger models usually generate better output but require more computing power and resources. Notable examples of GenAI systems include ChatGPT, Copilot from Microsoft, and Google’s Gemini, which focus on language generation, and Midjourney  and DALL-E , which focus on image generation.

Attribution: Adpated from AI in Education University of Sydney licensed under a Creative Commons BY-NC 4.0 licence