Artificial General Intelligence

Artificial Intelligence

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

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.

Symbolic AI

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

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

An approach to AI modelling cognitive processes using artificial neural networks inspired by biological neural structures.

Problem-solving techniques that use practical rules of thumb to find satisfactory solutions when exhaustive search is impractical.

Abilities that appear in large language models at certain scale thresholds that were not present in smaller versions, such as in-context learning and complex reasoning.

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

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

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

A method of problem-solving that represents all possible states of a system and searches for a path from initial to goal state.