Model Pruning

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.

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.

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.

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

The process of teaching an AI model to recognise patterns by exposing it to large datasets and adjusting its parameters.

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 table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

The process of optimising the external configuration settings of a machine learning model that are not learned during training.

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

Neural Network

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