AI Ethics

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.

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 determining cause-and-effect relationships from data, going beyond correlation to establish causation.

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

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

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

The challenge in AI of representing the effects of actions without having to explicitly state everything that remains unchanged.

A machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.

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

The process of reducing the numerical precision of a model's weights and activations from floating-point to lower-bit representations, decreasing memory usage and inference latency.