Precision

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.

Recall

The ratio of true positive predictions to all actual positive instances, measuring completeness of positive identification.

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 systematic adversarial testing of AI systems to identify vulnerabilities, failure modes, harmful outputs, and safety risks before deployment.

A centralised repository for storing, versioning, and managing trained AI models across an organisation.

The frameworks, policies, and regulations that guide the responsible development and deployment of AI technologies.

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

The principle of ensuring AI systems make equitable decisions without discriminating against any group based on protected attributes.

Querying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.

An initial instruction set provided to a language model that defines its persona, constraints, output format, and behavioural guidelines for a given session or application.

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