The evolution of Artificial Intelligence marks a definitive paradigm shift from Symbolic AI (GOFAI) to Connectionism. Historically, AI relied on "top-down" deductive reasoning, where human-coded rules governed every outcome. This approach, while precise for logic puzzles, failed at Moravec’s Paradox—the realization that high-level reasoning is computationally easy, while low-level sensorimotor skills (like recognizing a face) are nearly impossible to hard-code.

In contrast, Biologically-Inspired Computing adopts a "bottom-up" inductive learning strategy. Rather than following static instructions, artificial neural networks utilize distributed representations to discern patterns from raw data. While these architectures mimic the parallel processing and plasticity of the human brain, it is vital to recognize them as mathematical abstractions. They leverage linear algebra and calculus to achieve functional results, prioritizing computational efficiency over biological fidelity.