Connectionism

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.

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

A table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

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

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

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.

A semiconductor designed specifically for AI and machine learning computations, optimised for parallel processing and matrix operations.

Google's custom-designed application-specific integrated circuit for accelerating machine learning workloads.