AI Glossary

An automated process that uses machine learning algorithms to discover and optimise neural network architectures for specific tasks and applications.

A virtual replica of a physical system that provides real-time monitoring, simulation and optimisation through continuous data synchronisation and modelling.

A decentralised system where multiple simple agents follow basic rules to create sophisticated collective behaviours and solve complex problems through emergent intelligence.

A hybrid approach combining quantum computing with machine learning algorithms to achieve computational advantages beyond classical methods through quantum phenomena.

A systematic method for AI systems to explore and evaluate possible paths through a problem space to find optimal solutions to defined goals.

Software technology that automates repetitive digital tasks by mimicking human interactions with computer systems through predefined workflows and rules.

A machine learning system where two neural networks compete to generate and validate synthetic content, progressively improving through adversarial training.

A technology that identifies and converts printed or handwritten characters from images and documents into machine-readable text through pattern recognition.

The deployment of AI processing capabilities directly on local devices and sensors, enabling real-time analysis without relying on cloud computing.

A machine learning approach enabling models to learn from very limited training examples while maintaining strong predictive performance through specialised learning strategies.

Computer-generated information that mimics real-world data characteristics while preserving privacy and enabling large-scale AI training scenarios.

The process of automating tasks based on understanding and interpreting semantic meaning and context rather than following rigid predefined rules.