TinyML

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.

Recall

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

Quantisation

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

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

AI systems designed and trained for a specific task or narrow range of tasks, such as image recognition or language translation.

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

The process of removing redundant or less important parameters from a neural network to reduce its size and computational cost.

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

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

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

The degree to which humans can understand the internal mechanics and reasoning of an AI model's predictions and decisions.

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.