Google DeepMind's Aeneas Lets Historians Decode Ancient Rome Without Writing Code

Epigraphy -- the study of ancient inscriptions -- has always been a discipline that demands years of specialized training. Deciphering a damaged Latin altar, dating a fragmentary Greek oracle tablet, or mapping religious practices across the Roman Empire requires not just historical knowledge, but the ability to navigate complex computational tools. Google DeepMind just removed that last barrier with the Predicting the Past Skill, a new addition to Google Antigravity that lets historians run sophisticated AI-powered analysis through plain English conversation.

A decade of work, now accessible to everyone

This release is the culmination of nearly a decade of collaboration between DeepMind and epigraphers. The milestones are two specialized models, both published in Nature:

• Ithaca (2022) -- a deep neural network for ancient Greek inscriptions. While Ithaca alone achieves 62% accuracy when restoring damaged texts, historians using Ithaca improved their own accuracy from 25% to 72%. It can also attribute inscriptions to their original location with 71% accuracy and date them to within 30 years of ground-truth ranges.
• Aeneas (2025) -- the successor model, focused on Latin inscriptions. Aeneas can process both text and image input and outperforms other state-of-the-art models at restoring missing characters in damaged inscriptions. It is designed to assist historians with epigraphy, automating key tasks: dating an inscription, identifying the region of origin, reconstructing partial inscriptions, and identifying parallels -- inscriptions with similar words or phrasing.

While trained for Latin, Aeneas can be adapted to other ancient languages, scripts and media, from papyri to coinage. An interactive version is freely available to researchers, students and educators at predictingthepast.com, and its code and dataset are open source.

The bottleneck this solves

Even with Ithaca and Aeneas available online, using them at research scale was painful. The DeepMind team identified three concrete blockers that historians kept hitting: