AI Democratisation

Expert System

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

Knowledge Graph

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

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 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.

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

The ability of AI models to perform tasks they were not explicitly trained on, using generalised knowledge and instruction-following capabilities.

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

A simplified alternative to RLHF that directly optimises language model policies using preference data without requiring a separate reward model.

Empirical relationships describing how AI model performance improves predictably with increases in model size, training data volume, and computational resources.

Computing systems that simulate human thought processes using self-learning algorithms, data mining, pattern recognition, and natural language processing.

Area Under the ROC Curve, a single metric summarising a classifier's ability to distinguish between classes.

A machine learning approach where models are trained across decentralised devices without sharing raw data, preserving privacy.