Neural Processing Unit

Tensor Processing Unit

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

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.

When an AI model generates plausible-sounding but factually incorrect or fabricated information with high confidence.

Querying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.

The ratio of true positive predictions to all positive predictions, measuring accuracy of positive classifications.

The degree to which humans can understand the internal mechanics and reasoning of an AI model's predictions and decisions.

The ratio of true positive predictions to all actual positive instances, measuring completeness of positive identification.

A thought experiment by John Searle arguing that executing a program cannot give a computer genuine understanding or consciousness.

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

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

Neural Network

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