BLEU Score

AI Benchmark

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

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.

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.

A centralised platform for storing, managing, and serving machine learning features consistently across training and inference.

The component of an AI system that applies logical rules to a knowledge base to derive new information or make decisions.

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

Architectures that enable AI agents to store, retrieve, and reason over information from past interactions, providing continuity and personalisation across conversations.

The coordination and management of multiple AI agents working together to accomplish complex tasks, routing subtasks between specialised agents based on capability and context.

The process of using a trained AI model to make predictions or decisions on new, unseen data.

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

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