AI Model Registry

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.

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.

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

An inference strategy that starts with known facts and applies rules to derive new conclusions until a goal is reached.

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

The practice of making AI systems' operations, data usage, and decision processes openly visible to stakeholders.

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

Reducing the precision of neural network weights and activations from floating-point to lower-bit representations for efficiency.

A centralised platform for storing, managing, and serving machine learning features consistently across training and inference.

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