The world presents massive amounts of data for learning but the data relevant to any one thing or event is sparse. I will present evidence from the egocentric experiences of infants and young children in daily lives at home that demonstrate this sparsity, focusing on the case of early visual object recognition and object name learning. I will show how the statistics of infant self-generated experiences present solutions to the problem: learner control and optimization of the input, a developmentally constrained curriculum of spatial and temporal properties of the input, and the coherence statistics of individual episodes of experience. I will present evidence with respect to both low-level visual statistics and higher-level semantic categories. I conclude with a discussion of the alliance of the neural mechanisms that generate the statistics at any point in development and the neural mechanisms do the learning. I will the implications of the findings for artificial intelligence including studies using infant egocentric experiences as training data.