Expert System

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 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.

A specialised processor designed to accelerate neural network computations in edge devices and mobile platforms.

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

Automated machine learning that automates the end-to-end process of applying machine learning to real-world problems.

Standardised tests and datasets used to evaluate and compare the performance of AI models across specific tasks.

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.

The capability of AI agents to invoke external tools, APIs, databases, and software applications to accomplish tasks beyond the model's intrinsic knowledge and abilities.

A statistical approach to AI that uses Bayes' theorem to update probability estimates as new evidence becomes available.

The process of optimising the external configuration settings of a machine learning model that are not learned during training.