AI can spot patterns in lost languages, but can't prove it found meaning

The article frames a fundamental tension in applying AI to the study of lost languages. Traditional decipherment relies on two pillars: a comparative anchor—a known language or script that bridges to the unknown—and accumulated human expertise built over decades of study. AI excels at one specific task within that workflow: it can compress years of manual cross-referencing and pattern-spotting into minutes, democratizing access to these problems beyond institutional resources.

However, the article emphasizes that this acceleration does not replace the human judgment required to validate a finding. The problem is epistemological: in an undeciphered language, there is no ground truth outside the expert community itself. A model's ability to find consistent statistical patterns—signs that co-occur, words that cluster—does not prove those patterns correspond to real semantic or phonetic meaning. The scarcity of surviving data (exemplified by Linear A at 7,500 characters total) means that false positives are not just possible but likely; any hypothesis can find scattered numerical support by chance. Thus the article concludes that AI's role is and will remain assistive rather than transformative: it is "a very fast assistant to a very old, very human puzzle."