AI-rewritten: This is a summary of an article from The Conversation, rewritten by AI (Qwen, running locally) to make it easier to read. The facts come from the original article – read it for the full story.

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People often imagine AI systems like databases that retrieve stored facts to answer questions. However, cognitive science reveals that modern artificial intelligence behaves more like a human brain because its origins lie in the study of the mind rather than pure computer logic. The term "artificial intelligence" was coined in 1956 at a Dartmouth College workshop, where researchers believed machines could be made intelligent by following strict rules.

This view changed when psychologist Frank Rosenblatt built the Perceptron in 1958, an early artificial neural network designed to learn from examples instead of adhering to fixed instructions. Later, in the 1980s, cognitive scientists David Rumelhart, Geoffrey Hinton, and Ronald Williams developed deep learning by training multilayered neural networks. These systems, combined with modern computing power like graphics chips and transformer architecture, allow AI to generalize from data rather than simply recalling stored information.

This brain-like origin explains why AI frequently "hallucinates" or provides false answers. Psychologist Elizabeth Loftus has shown that human memory is reconstructive and prone to creating false memories, a trait shared by AI systems that operate probabilistically rather than deterministically. Unlike a calculator that gives the same answer every time, an AI might provide different responses to the same question because it fills in gaps with plausible details, much like human memory does.

Understanding this distinction helps users manage their expectations and avoid overtrusting AI outputs as verified facts. It also clarifies why educators face challenges integrating these tools into education, as institutions struggle between banning them or treating them as reliable collaborators. As computer science moves toward interpretability to understand opaque models, the fields of cognitive science and artificial intelligence continue to exchange questions and methods to better define how intelligence works.