Glossary
A plain-English glossary of the methods and ideas behind modern AI. Search, sort, or jump to a letter.
9 terms
| Definition | ||
|---|---|---|
| Scaling Laws | Empirical relationships predicting model quality from compute, data, and size. | May 21, 2026 |
| Self-Play | Improving by training against copies of oneself. | May 21, 2026 |
| Self-Supervised Learning | Learning from unlabeled data by predicting part of it from the rest. | May 21, 2026 |
| Sequence-to-Sequence | An encoder-decoder framework that maps one sequence to another. | May 21, 2026 |
| Speculative Decoding | Speeding up generation with a small draft model the big one verifies. | May 21, 2026 |
| State Space Models | Sequence models with linear-time scaling, an alternative to attention. | May 21, 2026 |
| Supervised Fine-Tuning (SFT) | Training a pretrained model on labeled input→output examples to teach a specific behavior, format, or task. | May 22, 2026 |
| Supervised Learning | Learning from labeled input-output examples. | May 21, 2026 |
| Synthetic Data | Training data generated by models rather than collected from humans. | May 22, 2026 |