Tensor Processing Unit

Neural Processing Unit

A specialised processor designed to accelerate neural network computations in edge devices and mobile platforms.

Model Distillation

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

Model Pruning

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

Neural Architecture Search

An automated technique for designing optimal neural network architectures using search algorithms.

Model Quantisation

The process of reducing the numerical precision of a model's weights and activations from floating-point to lower-bit representations, decreasing memory usage and inference latency.

Sparse Attention

An attention mechanism that selectively computes relationships between a subset of input tokens rather than all pairs, reducing quadratic complexity in transformer models.

Model Collapse

A degradation phenomenon where AI models trained on AI-generated data progressively lose diversity and accuracy, converging toward a narrow distribution of outputs.

Neural Scaling Laws

Empirical relationships describing how AI model performance improves predictably with increases in model size, training data volume, and computational resources.

Speculative Decoding

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

Artificial intelligence algorithms processed locally on edge devices rather than in centralised cloud data centres.

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

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

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

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

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

A machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.

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.

Machine Learning

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