Artificial Superintelligence

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.

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.

Symbolic AI

An approach to AI that uses human-readable symbols and rules to represent problems and derive solutions through logical reasoning.

The process of using a trained AI model to make predictions or decisions on new, unseen data.

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

A computational approach where problems are defined as a set of variables, domains, and constraints that must all be simultaneously satisfied.

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

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

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

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

A training paradigm where AI models are refined using human preference signals, aligning model outputs with human values and quality expectations through reward modelling.