Today’s sequence models (such as large language models) in machine learning (AI) arose from a blend of principle-based design and empirical discovery, spanning several fields. This talk describes how many of the main ideas could have emerged from an elementary signal-processing approach. This viewpoint offers some features:

1. Signal processing folks can quickly learn what is happening in a motivated way
2. Machine-learning experts might benefit from signal-processing insights
3. Obvious suggestions for things to try next naturally arise