Neural Architecture Search

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.

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.

A metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.

A technique where a language model is given a small number of examples within the prompt to guide its response pattern.

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

Automated machine learning that automates the end-to-end process of applying machine learning to real-world problems.

The economic model governing the pricing and allocation of computational resources for AI inference, including per-token billing, rate limiting, and credit systems.

The capability of AI agents to invoke external tools, APIs, databases, and software applications to accomplish tasks beyond the model's intrinsic knowledge and abilities.

A simplified alternative to RLHF that directly optimises language model policies using preference data without requiring a separate reward model.

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

Neural Network

A computing system inspired by biological neural networks, consisting of interconnected nodes that process information in layers.