Glossary
A plain-English reference for the AI, automation, workflow and agent terms used across The Edge.
Core Concepts and Terminology
- Algorithm: A sequence of rules or instructions given to an AI system to perform a task or solve a problem.
- Artificial Intelligence (AI): The broad field of computer science focused on creating machines that can perform tasks that typically require human intelligence, such as learning, reasoning and decision-making.
- Artificial General Intelligence (AGI): A theoretical form of AI that possesses human-like cognitive abilities across a wide range of tasks, rather than being limited to a specific function.
- Machine Learning (ML): A subset of AI that allows systems to automatically learn and improve from experience and data without being explicitly programmed.
- Neural Network: A computing system inspired by the structure and function of the human brain, made up of interconnected layers of “neurons” that process and transmit information to solve complex tasks.
Automation and Workflows
- Automation: The use of technology to perform tasks or processes with minimal human intervention. Traditional automation follows predefined, rigid rules, whereas AI automation can adapt and make decisions based on data.
- Workflow: A sequence of tasks or steps that are systematically organised to achieve a specific goal or outcome – essentially the “how” a process is accomplished.
- Workflow Automation: The use of software to automate a series of repetitive tasks within a workflow, following predefined rules or conditions.
- AI Workflow (or AI-powered Workflow): A structured sequence of steps where AI capabilities (like machine learning or NLP) are embedded to perform complex operations, make decisions and adapt to changing inputs, making the workflow smarter and more flexible than traditional automation.
- Intelligent Automation (IA): The combination of robotic process automation (RPA) and AI technologies (ML, NLP) to automate complex business processes and emulate human decision-making.
Types of Learning and Language
- Supervised Learning: A method of training an AI model using a labelled dataset, where the desired output is provided for each input, allowing the model to learn the relationship between them.
- Unsupervised Learning: A form of machine learning where the algorithm identifies patterns and structures in unlabelled data on its own, without specific guidance.
- Reinforcement Learning: A training method where an AI agent learns to make decisions by receiving rewards for desired actions and penalties for undesired ones, maximising a defined goal over time.
- Deep Learning: A branch of ML that uses multi-layered neural networks (deep neural networks) to analyse data, enabling more complex pattern recognition and learning.
- Generative AI (GenAI): A subset of AI that uses machine learning techniques to create new, original content such as text, images or music, based on patterns learned from training data.
- Large Language Model (LLM): A type of AI model, often using a transformer architecture, trained on vast amounts of text data to understand and generate human-like text.
- Natural Language Processing (NLP): The branch of AI that enables machines to understand, interpret and generate human language.
- Prompt Engineering: The practice of designing and refining the input (prompts) given to an AI model to guide its output effectively and achieve the desired results.
- Hallucination: When an AI model, particularly an LLM, generates information that is false, nonsensical or not present in its source data, while presenting it as fact.
- Retrieval-Augmented Generation (RAG): A technique that improves the accuracy and relevance of an LLM’s response by allowing it to access and reference an external knowledge base before generating an answer.
Other Important Terms
- AI Ethics: The moral principles and guidelines governing the responsible development and use of AI, considering issues like bias, transparency and accountability.
- Bias: The tendency of an AI system to produce unfair or skewed outcomes due to flawed or prejudiced data used during training.
- Chatbot: A software application designed to mimic human conversation through text or voice interaction.
- Computer Vision: A field of AI that enables computers to interpret and understand content from digital images and videos.
- Explainable AI (XAI): A set of processes and methods focused on making the decision-making processes of AI systems transparent and understandable to humans.
- Token: The fundamental unit of text used by AI models for processing, often equivalent to part of a word, a full word or a punctuation mark.
