Recall

AI Benchmark

Standardised tests and datasets used to evaluate and compare the performance of AI models across specific tasks.

BLEU Score

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

Perplexity

A measurement of how well a probability model predicts a sample, commonly used to evaluate language model performance.

F1 Score

A harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives.

Confusion Matrix

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

ROC Curve

A graphical plot illustrating the diagnostic ability of a binary classifier as its discrimination threshold is varied.

AUC Score

Area Under the ROC Curve, a single metric summarising a classifier's ability to distinguish between classes.

Precision

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

TinyML

Machine learning techniques optimised to run on microcontrollers and extremely resource-constrained embedded devices.

Quantisation

Reducing the precision of neural network weights and activations from floating-point to lower-bit representations for efficiency.

The process of teaching an AI model to recognise patterns by exposing it to large datasets and adjusting its parameters.

A sequence of data processing and model execution steps that automate the flow from raw data to AI-driven outputs.

An extension of the World Wide Web that enables machines to interpret and process web content through standardised semantic metadata.

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

An AI program that emulates the decision-making ability of a human expert by using a knowledge base and inference rules.

An inference strategy that starts with known facts and applies rules to derive new conclusions until a goal is reached.

The ability to describe AI decision-making processes in human-understandable terms, enabling trust and regulatory compliance.

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