Look up any term in AI.

Inference

Running a trained model to get an answer. Training is paid once; inference is paid on every request — and it is where most AI money goes.

Optimization

In machine learning, optimization means minimizing a loss function to fit a model's parameters — the iterative, gradient-driven mechanism behind training.

Diffusion Language Models (DLMs)

Text models that unmask many tokens per pass instead of writing one at a time — what that actually buys in speed, and what it costs in quality.

Vector Database

A vector database stores embeddings with metadata, builds an approximate-nearest-neighbour index over them, and serves filtered similarity queries at scale.

Training

Training fits a model's parameters to data: forward pass, measure the loss, backward pass, nudge every weight downhill, repeat until the loss stops falling.

Matrix Multiplication (GEMM)

The one operation that consumes most of an AI model's compute — and the reason AI chips are built the way they are, from tensor cores to systolic arrays.