AI Across the Curriculum from Johnson, M. Know & Understand AI: A Shared Definition. (2024). https://citt.ufl.edu/articles/know--understand-ai-a-shared-definition-for-uf.html.
Definitions adapted from The Alan Turing Institute, Chapman University, and the National Institute of Standards and Technology (NIST).
| Term | Definition |
| Artificial intelligence (AI) |
The ability of a digital computer or computer-controlled system to perform tasks commonly associated with intelligent beings. This includes learning from experience, reasoning, problem-solving, understanding language, recognizing patterns, and adapting to new situations. The term “artificial intelligence” can also refer more broadly to the field—the design and study of machines that can perform tasks that would previously have required human (or other biological) brainpower to accomplish. |
| Computer vision | A field of research that uses computers to obtain useful information from digital images or videos. Applications include object recognition (e.g. identifying animal species in photographs), facial recognition (smart passport checkers), medical imaging (spotting tumors in scans), navigation (self-driving cars) and video surveillance (monitoring crowd levels at events). |
| Deep learning | A form of machine learning that uses computational structures known as ‘neural networks’ to automatically recognize patterns in data and provide a suitable output, such as a prediction or evidence for a decision. |
| Foundational model | A machine learning model trained on a vast amount of data so that it can be easily adapted for a wide range of applications. A common type of foundation model is large language models. |
| Generative AI | An artificial intelligence system that generates text, images, audio, video or other media in response to user prompts. It uses machine learning techniques to create new data that has similar characteristics to the data it was trained on, resulting in outputs that are often indistinguishable from human-created media. |
| Hallucination | A false or misleading output produced by a generative model that does not align with reality. This is common in large language models when they generate fabricated facts. |
| Large language model (LLM) | A type of foundation model that is trained on a vast amount of textual data in order to carry out language-related tasks. |
| Machine learning | A field of artificial intelligence involving computer algorithms that can ‘learn’ by finding patterns in sample data. The algorithms then typically apply these findings to new data to make predictions or provide other useful outputs. |
| Prompt | An input query or instruction given to a language model to generate a response. Prompt engineering optimizes how prompts are structured to achieve better outputs. |
| Supervised learning | A type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data. |
| Unsupervised learning | A learning strategy that consists in observing and analyzing different entities and determining that some of their subsets can be grouped into certain classes, without any correctness test being performed on acquired knowledge through feedback from external knowledge sources. |
National Institute of Standards and Technology Trustworthy & Responsible AI Resource Center Glossary
The Alan Turing Institute Data science and AI glossary
Chapman University AI Key Terms
IT Modernization Centers for Excellence AI Guide for Government Key AI terminology
MIT Glossary of Terms: Generative AI Basics
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