Forecasting
Summary
Forecasting is a critical area of AI alignment research that involves predicting future observations based on past data. A key challenge in this field is dealing with incomplete models, which are convex sets of probability measures that may partially describe the underlying data-generating process. This approach bridges the gap between realizable settings (where the true probability measure is known to belong to a specific set) and unrealizable settings (where the measure is entirely arbitrary). Recent research has developed methods for forecasting that can guarantee convergence to the correct incomplete model when the true probability measure satisfies one of the models in a given countable set. This convergence is measured using the Kantorovich-Rubinstein metric, which provides a weaker but still meaningful form of convergence compared to total variation. Such advancements in forecasting techniques are crucial for improving AI systems’ ability to make accurate predictions in complex, uncertain environments, which is essential for ensuring their safe and reliable operation in real-world applications.