F1 Score

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.

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.

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

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

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

The branch of ethics examining moral issues surrounding the development, deployment, and impact of artificial intelligence on society.

An inference acceleration technique where a small draft model generates candidate token sequences that are verified in parallel by the larger target model.

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

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

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.