Confusion Matrix

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.

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 creation of artificially produced datasets that mimic the statistical properties of real-world data, used for training AI models while preserving privacy.

Computing systems that simulate human thought processes using self-learning algorithms, data mining, pattern recognition, and natural language processing.

A form of logic that handles approximate reasoning, allowing variables to have degrees of truth rather than strict binary true/false values.

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

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

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

A statistical approach to AI that uses Bayes' theorem to update probability estimates as new evidence becomes available.

The interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.