AI Reference 011 History & Foundations Wikipedia guided Primary sources linked

Stored-Program Computers and the Von Neumann Architecture

Definition

Stored-program computing places instructions in memory so they can be fetched and executed rather than requiring a machine to be rewired for every task.

What is a stored-program computer?

A stored-program computer keeps program instructions in machine-accessible memory rather than encoding the program entirely in plugboards, fixed wiring or external mechanical configuration. Instructions can therefore be loaded, changed and processed as stored information.

What is the von Neumann architecture?

The phrase usually refers to a computer architecture in which instructions and data share a common memory and a processor fetches instructions from that memory. John von Neumann's 1945 draft report on EDVAC helped spread the architecture, although several researchers contributed to the underlying stored-program concept and historians treat simple attribution cautiously.

Why the idea changed computing

Stored programs made general-purpose computers dramatically easier to reuse. A machine could switch from numerical work to text processing or an AI program by loading different instructions. This is the basic software model behind most modern computing: hardware remains general while programs define the job.

Related terms, defined

History & Foundations Cybernetics and Feedback Systems Cybernetics studied control and communication in animals and machines, especially feedback loops in which systems sense state, compare it with a goal and adjust behavior. History & Foundations The Turing Test and the 1950 Question of Machine Intelligence In 1950 Alan Turing published “Computing Machinery and Intelligence” and proposed the imitation game as a behavioral way to discuss machine intelligence rather than trying to define the word “thinking” directly. History & Foundations The Dartmouth Workshop and the Birth of Artificial Intelligence The 1956 Dartmouth Summer Research Project on Artificial Intelligence is widely treated as a founding event of AI as a research field. The proposal used the term “artificial intelligence” and brought together researchers working on machine reasoning, learning and symbolic problem solving. History & Foundations The Perceptron and Early Machine Learning The perceptron is an early trainable model associated with Frank Rosenblatt. It learns a linear decision boundary from examples and became historically important in the development of neural-network research. History & Foundations From the Abacus to Artificial Intelligence AI sits at the end of a much longer history of calculation, logic, programmability, electronics, statistics and learning systems. The abacus itself is not AI; its relevance is that it is an early tool for externalizing arithmetic procedures.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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