👉 Supervisor computing is a framework for training and deploying machine learning models in a safe, controlled environment. It allows developers to define safety constraints and operational policies that guide the learning process, ensuring that the model behaves appropriately under various conditions. By using a supervisor, developers can specify what actions are permissible and what should be avoided during training and inference, effectively creating a "guardian" that monitors the model's behavior and intervenes if it deviates from safe or expected norms. This approach is particularly valuable in high-stakes applications like autonomous driving, healthcare, and finance, where safety and reliability are paramount. Supervisor computing combines elements of reinforcement learning, constraint satisfaction, and formal verification to provide a robust mechanism for developing trustworthy AI systems.
