Understand the ideas behind AI capabilities: reasoning, training, personalization and efficiency. We explain what each study changes, what the experiments establish and where its results stop, so model and product teams can choose more useful tests and development paths.
Why flexible models can generalize: original polynomial experiments explain capacity, regularization and what teams should test before trusting a model.
