Programming AI models is akin to developing calculus over traditional mathematics.

Just as calculus handles continuous, infinite sets and fuzzy boundaries, AI development manages ambiguous inputs and outputs.

Traditional software translates explicit human logic into binary code, producing deterministic results.

In contrast, AI development transforms vast, unstructured data through probabilistic methods. The process involves preparing, featurizing, training, and evaluating data, resulting in a model that can generate varied, probabilistic outputs.

This paradigm shift allows AI to handle open-ended tasks, producing different results each time, much like how calculus addresses the complexities of continuous change beyond traditional mathematics’ scope.