Google DeepMind has a new way to look inside an AI's "mind"

"I want to be able to look inside a model and see if it's being deceptive," says Neel Nanda, who runs the mechanistic interpretability team at Google DeepMind. "It seems like being able to read a model's mind should help." Mechanistic interpretability, also known as "mech interp," is a new research field that aims to understand how neural networks actually work. At the moment, very basically, we put inputs into a model in the form of a lot of data, and then we get a bunch of model weights at the end of training. These are the parameters that determine how a model makes decisions. We have some idea of what's happening between the inputs and the model weights: Essentially, the AI is finding patterns in the data and making conclusions from those patterns, but these patterns can be incredibly complex and often very hard for humans to interpret.