Watch: Understanding and Control

AI optimization relies on two core pillars: understanding how a model works, and controlling what it does. In the machine learning world, these are known as mechanistic interpretability and model steering.

First, let's look at mechanistic interpretability. This is the quest for deep model understanding. Instead of just looking at what a model outputs, researchers try to reverse-engineer the system. They look inside the neural network at individual components, like neurons and circuits, to map out the actual algorithms the model has created. The goal is to explain exactly how and why a model behaves the way it does.

Once we understand a model, we can control it. This is where model steering comes in. Steering is the practice of actively shaping a model's behavior, either while it is training or when it is running. We can do this in a few ways. We can use prompts to guide the output, or make direct interventions by modifying the model's internal states. We can even use our mechanistic understanding to target specific circuits, turning certain capabilities on or off. Ultimately, steering ensures that artificial intelligence aligns with our goals, safety rules, and values. Together, understanding and control turn AI from a black box into a tool we can reliably guide.