Edward Feigenbaum, 2012

“Knowledge is power, and the computer is an amplifier of that power.”

Feigenbaum summarized the expert-systems insight that useful AI depends heavily on structured domain knowledge, not inference alone. Reading Edward Feigenbaum in the setting of “Computer History Museum Fellow Profile” changes how the quotation lands. The year 2012 identifies the documented source, while July 29 is only this series calendar position. Returning to the original work verifies authorship, clarifies the problem under discussion, and marks where modern interpretation begins.

Computers can extend human capability when knowledge is represented, shared, checked, and applied at scale. For a present-day team, the idea becomes useful when it changes a decision instead of decorating a slide. A reviewer can apply it to an interface, architecture, dataset, workflow, classroom, or governance rule, naming the desired outcome and the evidence that could challenge a preferred story.

Amplification also magnifies errors, omissions, secrecy, and unequal control over which knowledge counts. This qualification keeps the sentence from becoming a universal slogan. Technical results live inside organizations, markets, laws, and communities, where a narrow benchmark rarely settles the whole question. Responsible use makes assumptions visible, records tradeoffs, and asks who gains convenience and who inherits risk.

Retrieval systems, knowledge graphs, and AI assistants again raise questions about provenance and authority at scale. The quotation endures as a method rather than a formula: understand the source, test the claim in its new environment, disclose important limits, and revise the implementation when real conditions disagree. What matters is whether the idea helps make technology more understandable, dependable, useful, and answerable to the people it affects.