Commonsense Reasoning

Artificial Intelligence

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

Artificial General Intelligence

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

Artificial Narrow Intelligence

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

Artificial Superintelligence

A theoretical level of AI that surpasses human cognitive abilities across all domains, including creativity and social intelligence.

AI Ethics

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

Cognitive Computing

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

Ontology

A formal representation of knowledge as a set of concepts, categories, and relationships within a specific domain.

Semantic Web

An extension of the World Wide Web that enables machines to interpret and process web content through standardised semantic metadata.

Turing Test

A measure of machine intelligence proposed by Alan Turing, where a machine is deemed intelligent if it can exhibit conversation indistinguishable from a human.

Chinese Room Argument

A thought experiment by John Searle arguing that executing a program cannot give a computer genuine understanding or consciousness.

Weak AI

AI designed to handle specific tasks without possessing self-awareness, consciousness, or true understanding of the task domain.

Strong AI

A theoretical form of AI that would have consciousness, self-awareness, and the ability to truly understand rather than simulate understanding.

The frameworks, policies, and regulations that guide the responsible development and deployment of AI technologies.

A graphical plot illustrating the diagnostic ability of a binary classifier as its discrimination threshold is varied.

Techniques for embedding imperceptible statistical patterns in AI-generated content to enable reliable detection and provenance tracking of synthetic outputs.

A technique where a language model is given a small number of examples within the prompt to guide its response pattern.

A form of logical inference that seeks the simplest and most likely explanation for a set of observations.

An initial instruction set provided to a language model that defines its persona, constraints, output format, and behavioural guidelines for a given session or application.

A degradation phenomenon where AI models trained on AI-generated data progressively lose diversity and accuracy, converging toward a narrow distribution of outputs.

A prompting technique that encourages language models to break down reasoning into intermediate steps before providing an answer.