Course in preparation · Coming soon
Interpretability: Probes, Features and Circuits
Look inside a small open model and test what you think you see. Train probes, find features with a sparse autoencoder, trace a circuit, and run the control that could prove each claim wrong.
Who it is for. Researchers and engineers drawn to interpretability teams, who want hands-on work with model internals rather than reading about it.
The planned syllabus
Planned lessons will help you understand each method, apply it to an AI problem, and judge whether the result holds up.
- 01
Activations and the residual stream
Hook a small model, record its activations, and know what each one is.
- 02
Linear probes and their controls
Train a probe, then a control task, so a high accuracy means something.
- 03
Sparse autoencoders
Train one on a small model's activations and judge which features are real.
- 04
Circuits and patching
Activation patching and ablation: find which components carry a behavior.
- 05
Claims that hold up
Write an interpretability result with its evidence, its controls and its limits.
