The Sequence Knowledge #732: A Powerful Idea: A Transformer for AI Interpretability
Can we build a universal architecture for interpreting AI models.
Today we will Discuss:
A powerful idea: a transformer for AI interpretability.
Anthropic’s famous paper about the biology of language models.
💡 AI Concept of the Day: : A Transformer for AI Interpretability
Today we would like to challenge with an interesting hypothesis. Will we see a transformer for AI interpretability?
The idea might not be as crazy as it sounds.
Across language, images, audio, and even simulated worlds, one recipe keeps winning: train a Transformer on raw streams with self‑supervision, scale it up, and structure emerges. Text models learn syntax and semantics, vision models learn objectness and spatial composition, audio models learn pitch and rhythm, and world models learn latent dynamics—mostly from predicting what comes next or what was masked out. The proposal here is to apply that same playbook inward. Treat a model’s own activations as the data stream and train a general interpreter that predicts the missing or counterfactual pieces of computation, so circuits and features “fall out” the way grammar does in language.