<ul><li>Human learning relies on specialization with distinct cognitive mechanisms, while most neural networks rely on gradient descent over an objective function.</li><li>Research investigates if human learners' faster learning with fewer examples compared to data-driven deep learning is due to using multiple specialized mechanisms in combination.</li><li>A study on inductive human learning simulations in tutoring environments shows that decomposing learning into multiple mechanisms significantly improves data efficiency, aligning it with human learning.</li><li>Efforts to improve machine learning efficiency should consider integrating multiple specialized learning mechanisms to bridge the efficiency gap between data-driven approaches and human learning.</li></ul>

Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency

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