Listen: Mechanistic Interpretability
Techniques for tracing what happens inside a model — logging activations and patching them — to see how and where it forms an output.
Transcript
To truly understand artificial intelligence, we have to look inside the model rather than just observing what it outputs. This practice is called mechanistic interpretability. It allows us to treat a network's internal activations as data we can record, probe, and even change.
Two core techniques make this possible. First is activation logging. This records the internal states of the model during a run. It lets us see exactly where a specific fact or brand name first appears in the network. A tool called a logit lens can even project these mid-layer states onto the model's vocabulary, letting us watch a word become more and more probable in real time.
The second technique is activation patching. This goes a step further by swapping internal activations between different runs. By doing this, we can establish direct causation, not just correlation, to see what actually drives the model's decisions.
For anyone looking to understand AI visibility, these techniques are incredibly powerful. If you can pinpoint the exact layer where a model decides to associate a specific term with your brand, you can figure out how to strengthen that connection. It links the complex mathematics of attention mechanisms directly to observable behavior.