AI glossary of terms

Artificial Intelligence (AI)

The simulation of human intelligence by machines to perform tasks such as learning, reasoning, and problem-solving. Learn more

Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to a type of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a human-like level. Learn more

Agentic AI

Artificial intelligence that demonstrates a capacity to act independently, make its own choices, and pursue goals, often exhibiting characteristics associated with human agency. Learn more

Autonomous Agent

A program or system capable of independent action within a defined environment, often using AI to make decisions and achieve goals. Autonomous agents are used in robotics, self-driving cars, and other applications. Learn more

Algorithm

A set of rules or instructions that a computer follows to solve a problem or complete a task. Learn more

Big Data

Extremely large and complex datasets that are difficult to process using traditional methods. Learn more

Computer Vision

The ability of computers to “see” and interpret images and videos. Learn more

Machine Learning (ML)

A subset of AI that allows computers to learn from data and improve their performance over time without being explicitly programmed. Learn more

Data Mining

The process of extracting knowledge and patterns from large datasets. Data mining is used in various fields, including marketing, fraud detection, and scientific research. Learn more

Deep Learning

An advanced type of machine learning using neural networks with many layers to analyse various factors of data. It is particularly effective for tasks like image and speech recognition. Learn more

Generative AI

A branch of AI focused on creating new content such as images, music, text, or even entire virtual environments by learning from existing data. Learn more

Human-in-the-loop (HITL)

A system where humans are involved in the decision-making process of an AI system. Learn more

Large Language Model (LLM)

A type of AI model trained on a massive dataset of text and code, capable of generating human-like text, translating languages, and answering questions. Learn more

Neural Networks

A series of algorithms that mimic the operations of a human brain to recognise patterns and solve problems in data. Learn more

Natural Language Processing (NLP)

AI techniques used to understand, interpret, and generate human language. Examples include chatbots and language translation services. Learn more

Pattern Recognition

The ability of a computer to identify patterns and regularities in data. Learn more

Generative Adversarial Networks (GANs)

A type of neural network used in generative AI that consists of two parts: a generator that creates new data and a discriminator that evaluates it. The two parts work together to produce increasingly realistic outputs. Learn more

Prompt Engineering

The process of designing and refining the input prompts given to generative AI models to produce the desired output. It involves crafting specific instructions or questions to guide the AI. Learn more

Reinforcement Learning (RL)

A type of machine learning where an agent learns by interacting with an environment and receiving rewards or penalties for its actions. Learn more

Robotics

The field of engineering that deals with the design, construction, operation, and application of robots. Learn more

Training Data

The initial set of data used to teach AI models how to perform tasks. In generative AI, this data can include images, text, audio, or other forms of content. Learn more

Model Fine-tuning

Adjusting a pre-trained AI model with additional data or parameters to improve its performance for a specific task or application. Learn more

Creative AI

AI systems designed to create original content, such as artwork, music, or writing, often used in creative industries and entertainment. Learn more

Synthetic Data

Artificially generated data used to train AI models, often created by generative AI systems. It can help improve model performance and reduce reliance on real-world data. Learn more

Bias in AI

Refers to the unintended and potentially harmful preferences or prejudices that can be introduced into AI systems based on the training data they are fed. Ensuring diverse and representative training data helps mitigate this risk. Learn more

AI Ethics

The study and application of ethical principles to ensure that AI technologies are developed and used responsibly, fairly, and without causing harm. Learn more