Knowledge Representation

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 process of optimising the external configuration settings of a machine learning model that are not learned during training.

The ratio of true positive predictions to all positive predictions, measuring accuracy of positive classifications.

Empirical relationships describing how AI model performance improves predictably with increases in model size, training data volume, and computational resources.

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

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

The movement to make AI tools, knowledge, and resources accessible to non-experts and organisations of all sizes.

A specialised processor designed to accelerate neural network computations in edge devices and mobile platforms.

The process of removing redundant or less important parameters from a neural network to reduce its size and computational cost.