Focused Learning: AI Training Using Small Datasets

This paper critiques the current over-reliance on large labeled datasets in artificial intelligence, especially in visual domains. Despite advances in pattern recognition, modern AI systems struggle to generalize in low-data, ambiguous, or real-world environments—where humans excel. The paper explores the overlooked cognitive mechanisms behind human learning. It invites a reevaluation of how learning models should be structured—emphasizing that true generalization may depend less on data volume and more on how knowledge is organized and retained.