Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective

This research review examines methodologies for addressing AI systems' challenges with limited training data through uncertainty quantification and synthetic data augmentation. The paper presents formal approaches including Bayesian learning frameworks, information-theoretic bounds, and conformal prediction methods to improve AI performance in data-scarce environments like robotics and healthcare.

• →Limited training data introduces epistemic uncertainty that fundamentally constrains AI system performance across critical applications.
• →Generalized Bayesian learning frameworks can characterize uncertainty through model parameter space analysis.
• →Information-theoretic bounds provide formal relationships between training data quantity and predictive uncertainty.
• →Conformal prediction methods offer finite-sample statistical guarantees for uncertainty quantification.
• →Combining limited labeled data with synthetic data augmentation can improve data efficiency in AI systems.