AI glossary

The definitions in this section are reproduced verbatim from The AI Glossary of University College Dublin on 30 August 2026.

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This glossary is therefore also licensed under CC-BY-NC-SA 4.0

Adaptive Learning

Subject or course material is adjusted based on the performance of the learner. The difficulty of material, the pacing, sequence, type of help given, or other features can be adapted based on the learner’s prior responses. See also: Intelligent Tutoring Systems

Agentic AI

AI systems capable of pursuing goals, making decisions, and taking multi-step actions with a degree of initiative, while still operating within human-defined constraints. See also: Autonomy, Swarms, Governance.

Anthropomorphism

Attributing human traits, emotions, or intentions to AI systems, often leading to misinterpretation of their capabilities and limitations. See also: Trustworthy AI, Transparency.

Algorithm

A defined sequence of computational steps used to solve a problem or perform a task. Algorithms are the “brains” of an AI system and what determines decisions in other words, algorithms are the rules for what actions the AI system takes. Machine learning algorithms can discover their own rules (see Machine learning for more) or be rule-based where human programmers give the rules.See also: Machine Learning, Model.

Artificial General Intelligence (AGI)

Artificial general intelligence has not yet been realized and would be when an AI system can learn, understand, and solve any problem that a human can.

Artificial Intelligence (AI)

Systems that display intelligent behaviour by analysing their environment and taking actions—autonomously or semi-autonomously—to achieve specific objectives. AI is a branch of computer science. AI systems use hardware, algorithms, and data to create “intelligence” to do things like make decisions, discover patterns, and perform some sort of action. AI is a general term and there are more specific terms used in the field of AI. AI systems can be built in different ways, two of the primary ways are: (1) through the use of rules provided by a human (rule-based systems); or (2) with machine learning algorithms. Many newer AI systems use machine learning (see definition of machine learning below).See also: Machine Learning, Foundation Model.

Artificial Narrow Intelligence (ANI)

AI can solve narrow problems and this is called artificial narrow intelligence. For example, a smartphone can use facial recognition to identify photos of an individual in the Photos app, but that same system cannot identify sounds.

Autonomy

The ability of an AI system to operate without continuous human intervention, within parameters set by humans. See also: Agentic AI, Human Oversight.

Bias

Systematic distortion in data or models that leads to unfair or discriminatory outcomes. See also: Fairness, Data Governance.

Biometric Data

Data relating to physical, physiological, or behavioural characteristics enabling unique identification of a person. See also: High-Risk AI System, Personal Data.

Black Boxes

We call things we don’t understand, “black boxes” because what happens inside the box cannot be seen. Many machine learning algorithms are “black boxes” meaning that we don’t have an understanding of how a system is using features of the data when making their decisions (generally, we do know what features are used but not how they are used). There are currently two primary ways to pull back the curtain on the black boxes of AI algorithms: Interpretable Machine Learning and Explainable Machine Learning.

Black-Box Model

A model whose internal logic is not interpretable by humans, even if inputs and outputs are observable. See also: Explainability, XAI (Explainable AI).

Chat-based generative pre-trained transformer (ChatGPT) models

A system built with a neural network transformer type of AI model that works well in natural language processing tasks (see definitions for neural networks and Natural Language Processing below). In this case, the model: (1) can generate responses to questions (Generative); (2) was trained in advance on a large amount of the written material available on the web (Pre-trained); (3) and can process sentences differently than other types of models (Transformer). See also: Transformer.

Classification

Assigning inputs (e.g., texts, images, individuals) to predefined categories based on learned patterns. See also: Supervised Learning, Model.

Computer Vision

AI methods that interpret and process visual information from images or video. Computer Vision is a set of computational challenges concerned with teaching computers how to understand visual information, including objects, pictures, scenes, and movement (including video). Computer Vision (often thought of as an AI problem) uses techniques like machine learning to achieve this goal. See also: Multimodal System, Deep Learning.

Conformity Assessment

Verification that an AI system meets specified regulatory or technical requirements (e.g., under the EU AI Act). See also: High-Risk AI System, Validation.

Continuous Learning System

An AI system that continues to update its model after deployment, based on new data. See also: Risk, Governance.

Critical AI

Critical AI is an approach to examining AI from a perspective that focuses on reflective assessment and critique as a way of understanding and challenging existing and historical structures within AI. Read more about critical AI.

Data

Data are units of information about people or objects that can be used by AI technologies.

Data Governance

Policies and processes that ensure the quality, integrity, security, and lawful use of data throughout its lifecycle. See also: Personal Data, Risk.

Data Minimisation

Principle that only the data necessary for a specific purpose should be collected and processed. See also: Personal Data, Ethical AI.

Deep Learning

A subfield of machine learning using multi-layer neural networks to learn complex representations from large datasets. Deep learning models are a subset of neural networks. With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted, but as mentioned earlier there are more layers and more calculations that can make adjustments to the output to determine each decision. The decisions by deep learning models are often very difficult to interpret as there are so many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language). See also: Neural Network, Transformer.

Deepfake

Synthetic or manipulated audio, image, or video content generated by AI that convincingly depicts events or persons that did not occur. See also: Generative AI, Risk.

Emergent Behaviour

Complex system-level behaviour arising from simple interactions among AI agents or components. See also: Swarms, Agentic AI.

Explainability

The degree to which an AI system’s workings and outputs can be understood in human-interpretable terms. See also: XAI (Explainable AI), Transparency.

Ethical AI

Design, development, and use of AI systems in ways that respect human rights, fairness, accountability, and societal values. See also: Trustworthy AI, Governance.

Evaluation

Systematic assessment of an AI system’s performance, robustness, fairness, and compliance with requirements. See also: Validation, Risk.

Fairness

Property of an AI system whereby its outcomes do not systematically disadvantage individuals or groups on unjustified grounds. See also: Bias, Ethical AI.

Federated Learning

Machine learning approach where models are trained across multiple devices or servers holding local data, without centralising the data. See also: Personal Data, Risk.

Foundation Model

A large-scale model trained on broad data at scale, adaptable to a wide range of downstream tasks. Foundation Models represent a large amount of data that can be used as a foundation for developing other models. For example, generative AI systems use large language foundation models. They can be a way to speed up the development of new systems, but there is controversy about using foundation models since depending on where their data comes from, there are different issues of trustworthiness and bias. Jitendra Malik, Professor of Computer Science at UC Berkeley once said the following about foundation models: “These models are really castles in the air, they have no foundation whatsoever.” See also: Large Language Model (LLM), Generative AI.

Generative AI

AI systems capable of producing new content (text, images, audio, code, etc.) that resembles data on which they were trained. See also: Prompt Engineering, Multimodal System.

Governance

Structures, policies, and processes through which AI is directed, controlled, and held accountable in organisations and societies. See also: Risk, Trustworthy AI.

Hallucination

Output generated by an AI system that is plausible-sounding but factually incorrect, incoherent, or unsupported by its input or training data. See also: Large Language Model (LLM), Transparency.

High-Risk AI System

Under the EU AI Act, an AI system whose use poses a significant risk to health, safety, or fundamental rights and is subject to specific regulatory obligations. See also: Conformity Assessment, Risk.

Human-centered Perspective

A human-centered perspective sees AI systems working with humans and helping to augment human skills. People should always play a leading role in education, and AI systems should not replace teachers.

Human Oversight

Measures that enable humans to understand, supervise, and intervene in the operation of AI systems where appropriate. See also: Autonomy, Trustworthy AI.

Impact Assessment (AI Impact Assessment)

Structured process to identify and evaluate potential effects of an AI system on individuals, groups, and society, including risks to rights and freedoms. See also: Risk, Ethical AI.

Inference

Use of a trained model to generate predictions or outputs from new input data. See also: Model, Large Language Model (LLM).

Intelligence Augmentation (IA)

Augmenting means making something greater; in some cases, perhaps it means making it possible to do the same task with less effort. Maybe it means letting a human (perhaps teacher) choose to not do all the redundant tasks in a classroom but automate some of them so they can do more things that only a human can do. It may mean other things. There’s a fine line between augmenting and replacing and technologies should be designed so that humans can choose what a system does and when it does it.

Intelligent Tutoring Systems (ITS)

A computer system or digital learning environment that gives instant and custom feedback to students. An Intelligent Tutoring System may use rule-based AI (rules provided by a human) or use machine learning under the hood. By under the hood we mean the underlying algorithms and code that an ITS is built with. ITSs can support adaptive learning. See also: Adaptive Learning

Interpretable Machine Learning (IML)

Interpretable machine learning, sometimes also called interpretable AI, describes the creation of models that are inherently interpretable in that they provide their own explanations for their decisions. This approach is preferable to that of explainable machine learning (see definition below) for many reasons including the fact that we should understand what is happening from the beginning in our systems, rather than try to “explain” black boxes after the fact.

Interpretability

Extent to which a human can understand the cause of a model’s decision or prediction. See also: Explainability, XAI (Explainable AI).

Large Language Model (LLM)

A type of foundation model trained primarily on text data to predict and generate sequences of words, enabling tasks such as translation, summarisation, and dialogue. Large language models form the foundation for generative AI (GenAI) systems. GenAI systems include some chatbots and tools including OpenAI’s GPTs, Meta’s LLaMA, xAI’s Grok, and Google’s PaLM and Gemini. LLMs are artificial neural networks. At a very basic level, the LLM detected statistical relationships between how likely a word is to appear following the previous word in their training. As they answer questions or write text, LLM’s use the model of the likelihood of a word occurring to predict the next word to generate. LLMs are a type of foundation model, which are pre-trained with deep learning techniques on massive data sets of text documents. Sometimes, companies include data sets of text without the creator’s consent. See also: Transformer, Generative AI.

Lifecycle (AI System Lifecycle)

All stages of an AI system’s existence, from conception and design through development, deployment, monitoring, and retirement. See also: Governance, Risk.

Machine Learning

Subfield of AI in which systems improve their performance on a task through experience (data), rather than explicit programming. Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. These algorithms build a model for decision making as they go through data. (You will sometimes hear the term machine learning model.) Because they discover their own rules in the data they are given, ML systems can perpetuate biases. Algorithms used in machine learning require massive amounts of data to be trained to make decisions.

It’s important to note that in machine learning, the algorithm is doing the work to improve and does not have the help of a human programmer. It is also important to note three more things. One, in most cases the algorithm is learning an association (when X occurs, it usually means Y) from training data that is from the past. Two, since the data is historical, it may contain biases and assumptions that we do not want to perpetuate. Three, there are many questions about involving humans in the loop with AI systems; when using ML to solve AI problems, a human may not be able to understand the rules the algorithm is creating and using to make decisions. This could be especially problematic if a human learner was harmed by a decision a machine made and there was no way to appeal the decision. See also: Supervised Learning, Unsupervised Learning, Reinforcement Learning.

Model

Mathematical or computational representation that maps inputs to outputs, learned from data or specified by design. See also: Model Card, Validation.

Model Card

Documentation describing an AI model’s intended use, performance, limitations, and ethical considerations. See also: Trustworthy AI, Transparency.

Multimodal System

AI system that processes and integrates multiple data types (e.g., text, images, audio) to generate or interpret content. See also: Generative AI, Transformer.

Natural Language Processing (NLP)

AI methods enabling machines to understand, interpret, and generate human language. Natural Language Processing is a field of Linguistics and Computer Science that also overlaps with AI. NLP uses an understanding of the structure, grammar, and meaning in words to help computers “understand and comprehend” language. NLP requires a large corpus of text (usually half a million words).

NLP technologies help in many situations that include: scanning texts to turn them into editable text (optical character recognition), speech to text, voice-based computer help systems, grammatical correction (like auto-correct or grammarly), summarizing texts, and others. See also: Large Language Model (LLM), Prompt Engineering.

Neural Network

Computational architecture inspired by biological neurons, consisting of interconnected nodes organised in layers that transform inputs into outputs. Neural networks also called artificial neural networks (ANN) and are a subset of ML algorithms. They were inspired by the interconnections of neurons and synapses in a human brain. In a neural network, after data enter in the first layer, the data go through a hidden layer of nodes where calculations that adjust the strength of connections in the nodes are performed, and then go to an output layer. See also: Deep Learning, Transformer.

Opacity

Lack of transparency or comprehensibility in how an AI system operates or reaches its outputs. See also: Black-Box Model, Transparency.

Output

Result produced by an AI system, such as a prediction, classification, recommendation, decision, or generated content. See also: Inference.

Personal Data

Any information relating to an identified or identifiable natural person, as defined in European data protection law. See also: Profiling, Data Minimisation.

Profiling

Automated processing of personal data to evaluate certain personal aspects, such as performance at work, economic situation, health, preferences, or behaviour. See also: High-Risk AI System, Risk.

Prompt Engineering

Designing and refining prompts to guide generative AI systems toward desired outputs. See also: [Generative AI] (#generative-ai), Natural Language Processing (NLP).

Reinforcement Learning

Learning paradigm where an agent interacts with an environment, receiving rewards or penalties and learning a policy to maximise cumulative reward. See also: Agentic AI, Risk.

Combination of the probability of an AI-related event and the severity of its potential impact on individuals, organisations, or society. See also: High-Risk AI System, Governance.

Robots

Robots are embodied mechanical machines that are capable of doing a physical task for humans. “Bots” are typically software agents that perform tasks in a software application (e.g., in an intelligent tutoring system they may offer help). Bots are sometimes called conversational agents. Both robots and bots can contain AI, including machine learning, but do not have to have it. AI can help robots and bots perform tasks in more adaptive and complex ways.

Robustness

Ability of an AI system to maintain acceptable performance under varying conditions, including noise, perturbations, or adversarial inputs. See also: Safety, Validation.

Safety (AI Safety)

Condition in which risks to health, safety, and fundamental rights arising from an AI system are reduced to an acceptable level. See also: Risk, Trustworthy AI.

Self-attention mechanism

These mechanisms, also referred to as attention help systems determine the important aspects of input in different ways. There are several types and were inspired by how humans can direct their attention to important features in the world, understand ambiguity, and encode information.

Supervised Learning

Machine learning approach where models are trained on labelled data, learning a mapping from inputs to known outputs. See also: Classification, Unsupervised Learning.

Synthetic Data

Artificially generated data that mimics statistical properties of real data, used for training, testing, or privacy protection. See also: Personal Data, Risk.

Swarms

AI Swarms are distributed collections of AI agents that coordinate their behaviour using principles inspired by biological swarms (e.g., ants, bees, birds). Each agent operates autonomously with limited local information, but collectively the swarm exhibits emergent intelligence, adaptability, and robustness. See also: Agentic AI, Emergent Behaviour.

Training Data

This is the data used to train the algorithm or machine learning model. It has been generated by humans in their work or other contexts in their past. While it sounds simple, training data is so important because the wrong data can perpetuate systemic biases. If you are training a system to help with hiring people, and you use data from existing companies, you will be training that system to hire the kind of people who are already there. Algorithms take on the biases that are already inside the data. People often think that machines are “fair and unbiased” but this can be a dangerous perspective. Machines are only as unbiased as the human who creates them and the data that trains them. (Note: we all have biases! Also, our data reflect the biases in the world.)(Fusco, 2020)

Transformer

Neural network architecture based on self-attention mechanisms, foundational to many modern large language models and multimodal systems. See also: Large Language Model (LLM), Multimodal System.

Transparency

Availability of meaningful information about an AI system’s capabilities, limitations, data use, and decision logic to relevant stakeholders. See also: Explainability, Model Card.

Trustworthy AI

AI that is lawful, ethical, and robust, in line with European frameworks for trustworthy and human-centric AI. See also: Ethical AI, Governance.

Turing Test

A test proposed by Alan Turing evaluating whether a machine’s behaviour is indistinguishable from that of a human in conversation. See also: Artificial Intelligence (AI), Anthropomorphism.

Unsupervised Learning

Machine learning approach that discovers patterns or structures in unlabelled data, such as clustering or dimensionality reduction. See also: Machine Learning.

User (of an AI System)

Any natural or legal person using an AI system under their authority, other than the provider, often with specific responsibilities under regulation. See also: Governance.

User Experience Design/User Interface Design (UX/UI)

User-experience/user-interface design refers to the overall experience users have with a product. These approaches are not limited to AI work. Product designers implement UX/UI approaches to design and understand the experiences their users have with their technologies.

Validation

Process of confirming that an AI system meets the needs and requirements of its intended use in real or representative conditions. See also: Evaluation, Robustness.

Vendor / Provider

Natural or legal person who develops or places an AI system on the market or puts it into service under their name or trademark. See also: User, Conformity Assessment.

XAI (Explainable AI) or EML (Explainable Machine Learning)

Methods and techniques that make the behaviour and decisions of AI systems more understandable to humans. Researchers have developed a set of processes and methods that allow humans to better understand the results and outputs of machine learning algorithms. This helps developers of AI-mediated tools understand how the systems they design work and can help them ensure that they work correctly and are meeting requirements and regulatory standards.

It is important to note that the term “explainable” in the context of explainable machine learning or explainable AI, refers to an understanding of how a model works and not to an explanation of how the model works. In theory, explainable ML/AI means that an ML/AI model will be “explained” after the algorithm makes its decision so that we can understand how the model works. This often entails using another algorithm to help explain what is happening as the “black box.” One issue with XML and XAI is that we cannot know for certain whether the explanation we are getting is correct, therefore we cannot technically trust either the explanation or the original model. Instead, researchers recommend the use of interpretable models. See also: Explainability, Interpretable Machine Learning, Black Boxes, Black-Box Model.

Zero-Shot Learning

Capability of a model to perform tasks or recognise classes it was not explicitly trained on, using generalisation from prior knowledge. See also: Large Language Model (LLM), Foundation Model.