AI Interpretability Explained: How Researchers Actually Test a Model's "Thoughts"
Ask a large language model a question and it gives you an answer with total confidence. Ask it how it got there, and it will happily make something up — the model has no built-in ability to narrate its own internal process, because its internal process was never written in a language anyone can read. That gap is exactly what the field of AI interpretability exists to close, and the way researchers are closing it looks less like reading code and more like neuroscience. Why You Can't Just "Read" a Neural Network A modern language model's actual substance is a long list of numbers — weights — connecting artificial neurons across dozens of layers. Even a modestly sized open model has billions of these weights, and none of them were hand-written by an engineer. They emerged from training: the model read enormous amounts of text, repeatedly guessed the next word, and had its weights nudged a tiny amount every time it was wrong. Multiply that nudge by trillions of guesses...