AI Chip

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 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.

Safety mechanisms and constraints implemented around AI systems to prevent harmful, biased, or policy-violating outputs while preserving useful functionality.

AI systems designed and trained for a specific task or narrow range of tasks, such as image recognition or language translation.

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

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

The AI capability to make inferences based on everyday knowledge that humans typically take for granted.

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

The principle of ensuring AI systems make equitable decisions without discriminating against any group based on protected attributes.

A computational approach where problems are defined as a set of variables, domains, and constraints that must all be simultaneously satisfied.

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.