Cracking the code: can AI help us decipher ancient languages?
Ancient languages such as Linear A and Etruscan lack the bilingual anchors that have traditionally enabled decipherment. AI tools can accelerate hypothesis testing, pattern detection, and reconstruction of fragmentary texts, but they cannot generate meaning without an external reference. A recent AI‑assisted effort claimed to assign values to dozens of Linear A signs, a claim that remains under scholarly review.
- ▪Linear A and Etruscan have no known bilingual texts or related languages to serve as anchors for decipherment.
- ▪AI excels at large‑scale pattern matching, quickly checking human hypotheses across extensive corpora.
- ▪Cross‑lingual transfer allows AI models trained on known languages to infer patterns in related unknown scripts, as demonstrated with Ugaritic.
- ▪A self‑taught engineer used AI to propose a Semitic affiliation for Linear A, assigning values to 40 signs, but the proposal is still being evaluated by experts.
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Record
| Original publisher | The Conversation |
| Canonical URL | https://theconversation.com/cracking-the-code-can-ai-help-us-decipher-ancient-languages-288238 |
| Publication time | Tue, 28 Jul 2026 06:59:35 +0000 |
| Retrieval time | 2026-07-28T07:14:57.182Z |
| Last seen | 2026-07-28T07:14:57.182Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | R8jvBdWTio17 · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
An inscription in Etruscan hangs above the heads of a noble couple in this wall painting from the Tomb of the Shields, Tarquinia, Italy. Prisma Archivo Cracking the code: can AI help us decipher ancient languages? Published: July 27, 2026 8:35am EDT https://theconversation.com/cracking-the-code-can-ai-help-us-decipher-ancient-languages-288238 https://theconversation.com/cracking-the-code-can-ai-help-us-decipher-ancient-languages-288238 Link copied Share article Share article Copy link Email Bluesky Facebook WhatsApp Messenger LinkedIn X (Twitter) Print article 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at The Conversation.
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