FREE AI & machine learning LESSON · AI & machine learning
Learn useful representations
Neural architectures, inductive bias, and scaling evidence
A neural architecture encodes assumptions about how useful structure can be learned.
Convolutions share local detectors, attention routes information across positions, recurrence carries sequential state, and embeddings place learned relationships in vector geometry. Depth composes transformations, but optimization, data, normalization, regularization, and compute determine whether capacity becomes generalization. Architecture choice should follow the structure of the signal and the decision, not fashion.
More parameters expand possible functions; evidence must show that training and data select a useful one.
Scale can improve averages while hiding dependence on shortcuts.
Models often exploit background, annotation artifacts, demographic proxies, duplicated data, or benchmark contamination. Counterfactual tests, challenge sets, attribution used cautiously, nearest-neighbor inspection, and targeted data interventions help reveal what signal drives predictions. A larger model can learn both the intended mechanism and a more powerful shortcut.
Performance does not tell you which feature the model used.