AI Accelerator

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

When an AI model generates plausible-sounding but factually incorrect or fabricated information with high confidence.

The process of determining cause-and-effect relationships from data, going beyond correlation to establish causation.

The interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.

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

Querying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.

The field of AI dedicated to representing information about the world in a form that computer systems can use for reasoning.

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