AI Agent Orchestration

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 Democratisation

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

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.

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

Problem-solving techniques that use practical rules of thumb to find satisfactory solutions when exhaustive search is impractical.

The ability to describe AI decision-making processes in human-understandable terms, enabling trust and regulatory compliance.

An AI algorithm that generates a sequence of actions to achieve a specified goal from an initial state.

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

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

The research field focused on ensuring AI systems act in accordance with human values, intentions, and ethical principles.