Strong AI

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.

Symbolic AI

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

A metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.

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

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

Systematic errors in AI outputs that arise from biased training data, flawed assumptions, or prejudicial algorithm design.

The coordination and management of multiple AI models, services, and workflows to achieve complex end-to-end automation.

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

The practice of designing and optimising input prompts to elicit desired outputs from large language models.

The field of AI dedicated to representing information about the world in a form that computer systems can use for reasoning.