What happens when you put AI to work deciphering lost languages?
Every ancient language that has ever been deciphered needed an anchor. Usually that’s a bilingual text, like the Rosetta Stone, or a known relative to compare it to. Linear A, the writing system of the Bronze Age Minoan civilization on the Greek island of Crete, has neither. Linear A is described as a “language isolate” because it has no confirmed link to any known language, living or dead. As such, it has posed a significant challenge for linguists over the past century. Etruscan, a language of
AI is being used to help linguists decipher lost languages like Linear A and Etruscan by rapidly testing hypotheses against large collections of ancient texts, work that would take humans months or years to complete by hand. AI excels at spotting patterns and cross-checking information across archives, but it cannot create meaning from statistical patterns alone and still requires an anchor point, such as a known related language or bilingual text, to determine which patterns are actually meaningful. The challenge with undeciphered languages is that there is no way to verify proposed translations against native speakers or expert consensus, making independent expert review and peer scrutiny critical rather than relying on AI confidence scores. AI functions most effectively as a research assistant that accelerates human investigation rather than as an independent solver of these linguistic puzzles.
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