Knowledge Graph

Expert System

An AI program that emulates the decision-making ability of a human expert by using a knowledge base and inference rules.

Inference Engine

The component of an AI system that applies logical rules to a knowledge base to derive new information or make decisions.

AI Orchestration

The coordination and management of multiple AI models, services, and workflows to achieve complex end-to-end automation.

AI Pipeline

A sequence of data processing and model execution steps that automate the flow from raw data to AI-driven outputs.

AI Model Registry

A centralised repository for storing, versioning, and managing trained AI models across an organisation.

Retrieval-Augmented Generation

A technique combining information retrieval with text generation, allowing AI to access external knowledge before generating responses.

AI Accelerator

Specialised hardware designed to speed up AI computations, including GPUs, TPUs, and custom AI chips.

AI Chip

A semiconductor designed specifically for AI and machine learning computations, optimised for parallel processing and matrix operations.

AI Democratisation

The movement to make AI tools, knowledge, and resources accessible to non-experts and organisations of all sizes.

AI Agent Orchestration

The coordination and management of multiple AI agents working together to accomplish complex tasks, routing subtasks between specialised agents based on capability and context.

Synthetic Data Generation

The creation of artificially produced datasets that mimic the statistical properties of real-world data, used for training AI models while preserving privacy.

AI Memory Systems

Architectures that enable AI agents to store, retrieve, and reason over information from past interactions, providing continuity and personalisation across conversations.

The branch of ethics examining moral issues surrounding the development, deployment, and impact of artificial intelligence on society.

An inference strategy that starts with a goal and works backward through rules to determine what facts must be true.

Artificial intelligence algorithms processed locally on edge devices rather than in centralised cloud data centres.

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.

A form of logic that handles approximate reasoning, allowing variables to have degrees of truth rather than strict binary true/false values.

The challenge in AI of representing the effects of actions without having to explicitly state everything that remains unchanged.

A measure of machine intelligence proposed by Alan Turing, where a machine is deemed intelligent if it can exhibit conversation indistinguishable from a human.

An extension of the World Wide Web that enables machines to interpret and process web content through standardised semantic metadata.