But the AI system still wouldn’t be able to hand a human a translation, because fluency and meaning aren’t the same thing here. The model can learn which signs follow which, and which words cluster together, without ever knowing what any of them actually refer to.
This is also why verifying any AI-assisted claim about these languages is so difficult, and it’s a problem bigger than any one case. Normally, you’d check a proposed translation or interpretation against native speakers, other texts, or expert consensus built up over decades. None of that exists for a genuinely undeciphered language. Short of a time machine, there’s no way to check.
Small surviving corpora make this worse: Linear A’s entire surviving corpus is about 7,500 characters, short enough to fit on a single screen, and, with that little data, almost any hypothesis can find scattered matches to support it.
That is why claims in this space lean so heavily on independent expert scrutiny and peer-review rather than statistical confidence scores. It is also why “AI found a pattern” and “AI found the correct meaning” are very different claims that are easy to blur together.
None of this means that AI is a dead end for these languages. Quite the opposite: it’s a real accelerant, able to compress years of manual cross-referencing into mere minutes and let more people attempt these problems than institutional resources ever allowed.
But it doesn’t remove the two ingredients that decipherment has always required. One is a genuine comparative anchor. The other is rigorous human review, to tell a real breakthrough from an appealing coincidence.
Until one of those shows up for Linear A or Etruscan, AI’s role in deciphering languages will stay what it is today: a very fast assistant to a very old, very human puzzle.
Jane Adkins is a PhD candidate at the School of Computing, Dublin City University.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
