Look up any term in AI.

Image Generation

How AI turns noise into a picture: the diffusion loop, the noise schedule on numbers, why it runs in a 48x-compressed latent space, and what guidance costs.

Disaggregated Serving (Prefill/Decode)

Disaggregated serving runs LLM prefill and decode on separate GPU pools because the two phases hit opposite limits: compute versus memory bandwidth.

Reinforcement Learning from Verifiable Rewards (RLVR)

RLVR replaces the learned reward model with a program that checks the answer. What that buys, where it stops working, and how it differs from RLHF.

Class Imbalance

Why a fraud model can score 99% accuracy and catch nothing — the imbalance trap, why precision/recall/PR-AUC replace accuracy, and how to fix it.

Feature Selection (FS)

Which columns to keep and how to decide: filter vs wrapper vs embedded, why 2^n subsets rules out brute force, and the CV mistake that fakes accuracy.

Symbolic AI

Symbolic AI (GOFAI) builds intelligence from human-written symbols and logic rules rather than learned data - the paradigm behind expert systems like MYCIN.