Look up any term in AI.

Normalization (LayerNorm, RMSNorm, BatchNorm)

Normalization rescales a layer's activations to a stable mean and scale so deep networks train faster. How BatchNorm, LayerNorm and RMSNorm differ.

Graph Neural Networks (GNN)

A GNN takes adjacency as data, not as an architectural assumption. Message passing worked by hand, why k layers reach d^k nodes, and why deep GNNs collapse.

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.

LLM-as-a-Judge

Using one model to grade another's output against a rubric: how it works, the biases that break it, and the human agreement rate you can actually reach.

Ambient Clinical Documentation

AI scribes draft the clinical note from consultation audio. What the largest deployment and the randomised trials measured — and who stays liable.

Model Deployment

What has to happen between a model that trains well and users calling it: replica sizing, latency budgets, cold starts, and the rollback nobody built.