You should be seeing some content in this space that is currently blocked due to cookie settings. Open the Cookie Management Tool to change your settings.
The Lexicon of Terminology provides a comprehensive overview of key concepts and terms related to AI. By immersing yourself in the Lexicon of Terminology, you will gain valuable insights into the world of AI and its underlying principles.
Please explore the links below to find specific information you are looking for on this page.
Terminology
Explore the links below to delve into specific terms and expand your knowledge of AI concepts.
Computer systems that can perform tasks that usually require human intelligence, like understanding language, recognising images, and making decisions.
Algorithms
In computer science and mathematics, an algorithm refers to a finite series of precise instructions, usually employed to address a particular set of problems or execute a computation.
Bias
Unintended favouritism or unfairness in an AI's decisions or outputs due to the data it was trained on.
Data
Information that the AI uses to learn and make decisions. In generative AI, this could be text, images, or other types of content.
Deep Learning
A method of machine learning that imitates that way humans learn, using layers of artificial neural networks to model and solve complex problems. It can learn and improve its functions by examining data without human intervention
Generative AI (GenAI)
A subset of artificial intelligence that involves algorithms and models designed to generate new, original data, such as text, images, audio, and more.
Hallucination
Hallucination refers to a situation where the AI model generates content that is unrealistic, inaccurate, or doesn't correspond to the patterns it has learned from the training data.
Image Generation
Creating new images based on patterns learned from existing images. This can be used for art, design, or even creating realistic photographs.
Labelled Data
Labelled data is data that has been categorised or "labelled" with the correct answers or outcomes. In supervised learning, each data point in labelled data is associated with a specific label or category. For example, in an image classification task, each image is labelled with the correct class it belongs to (e.g., "cat" or "dog"). Labelled data is essential for training machine learning models to make accurate predictions.
Large Language Model
Large language model is a deep learning algorithm that can recognise, summarise, translate, predict, and generate text and other forms of content based on knowledge gained from massive datasets.
Machine Learning
A type of AI that uses algorithms and data to learn, allowing systems to improve over time with experience without being specifically programmed.
Text Generation
Creating new text based on patterns learned from existing text. This can be anything from writing a story to answering questions.
Training Data
Examples and information used to teach an AI model how to do a specific task.
Training
The process of teaching an AI model by exposing it to examples and allowing it to adjust its parameters to learn from them.
Transfer Learning
A machine learning technique where knowledge gained from one task or domain is applied to another related task or domain. Generative models can benefit from transfer learning to improve their performance on specific tasks.
Unlabelled Data
Unlabelled data lacks predefined labels or categories. It consists of raw data without corresponding correct answers. Unsupervised learning and other techniques are often used to analyse unlabelled data to discover patterns, clusters, or structures within the data.
Unsupervised Learning
A type of machine learning where the model learns patterns and features from data without explicit supervision. GenAI models often utilise unsupervised learning to capture the underlying distribution of the data.
Zero-Shot Learning
A scenario where a model can perform a task without any specific training examples for that task.
Further Information
Critical AI Literacy
Learn about privacy concerns, biases, environmental impact, and worker exploitation in GenAI tools. Develop responsible AI use.
Explore the capabilities and limitations of GenAI tools, including text generation, math problem solving, coding, and more. Consider potential biases and limitations when using these tools.
Generative Artificial Intelligence (GenAI) is a subset of AI that uses patterns from training data to generate new text, audio, or media. Learn how it works and its capabilities.
Toolkit for the Ethical Use of GenAI in Learning and Teaching
(AI)2ed Project
Toolkit for the Ethical Use of GenAI by Loretta Goff and Tadhg Dennehy, UCC Skills Centre. This work is licensed under Attribution-NonCommercial 4.0 International ,
.jpg)