Speculative Decoding

Tensor Processing Unit

Google's custom-designed application-specific integrated circuit for accelerating machine learning workloads.

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.

Middleware that manages routing, fallback, load balancing, and model selection across multiple AI providers to optimise cost, latency, and output quality.

A computational approach where problems are defined as a set of variables, domains, and constraints that must all be simultaneously satisfied.

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

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

A theoretical level of AI that surpasses human cognitive abilities across all domains, including creativity and social intelligence.

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

The ability to describe AI decision-making processes in human-understandable terms, enabling trust and regulatory compliance.

The practice of making AI systems' operations, data usage, and decision processes openly visible to stakeholders.

Token

A digital asset created and managed on a blockchain, representing value, utility, or ownership rights.