AI’s Brain‑Like Roots Explain Its Hallucinations and Unpredictability

Large language models do not function as simple databases; they learn from examples in a way that mirrors human memory. The field’s origins in cognitive science—starting with Frank Rosenblatt’s Perceptron and later deep‑learning breakthroughs by Rumelhart, Hinton, and Williams—grounded AI in neural‑network architectures inspired by the brain. This design allows models to generalize to new questions but also makes them probabilistic and prone to confabulation, producing “hallucinations” that differ from deterministic lookup systems. Understanding this lineage helps users form realistic mental models, encourages verification of outputs, and guides researchers toward interpretability methods that probe the opaque internals of these models.