Google DeepMind Launches AI Data Analysis Tool for Historians Working with Ancient Inscriptions

Historians and epigraphers now have a powerful new tool at their disposal, thanks to the launch of Google DeepMind’s Predicting the Past Skill. This innovative AI skill is designed specifically for researchers working with ancient inscriptions, allowing them to analyze and interpret these valuable historical artifacts without requiring extensive coding knowledge.

The Predicting the Past Skill connects Gemini, an interactive reasoning platform developed by Google DeepMind, with two specialist models: Ithaca and Aeneas. These models were created to restore, date, place, and contextualize ancient Greek and Latin inscriptions, but their capabilities have now been integrated into a conversational workflow that streamlines the analysis process.

According to Google DeepMind, historians and epigraphers can use this tool to query, cross-analyze, and map massive collections of ancient data as naturally as speaking with a colleague. This means they can explore complex historical questions without needing to write code or rely on manual transcription.

The Predicting the Past Skill is particularly well-suited for researchers working with fragmentary texts, uncertain dates, and unclear places of origin. It’s designed to help them navigate these challenges by providing flexible visualizations, advanced multi-text analysis capabilities, and large language models grounded in evidence and domain expertise.

Google DeepMind has been collaborating with epigraphers on this project for nearly a decade, including the development of Ithaca in 2022 and Aeneas in 2025. Thea Sommerschield, a historian and epigrapher at Durham University, has co-led Google DeepMind’s work in this area.

Ancient inscriptions are central to historical research, but many survive in damaged or partial form. They can include imperial decrees, votive dedications, everyday transactions, and personal appeals – often with missing text, uncertain dating, and disputed origin. Historians need flexible tools that can help them make sense of these complex artifacts.

Google DeepMind has identified three key barriers to AI-assisted historical analysis: researchers require flexible visualizations for individual inscriptions, more advanced multi-text analysis capabilities without specialist coding, and large language models grounded in evidence and domain expertise. The Predicting the Past Skill is designed to address these challenges by linking Gemini’s interactive reasoning with the outputs of Ithaca and Aeneas.

Rather than asking a general-purpose model to work from scratch, the skill draws on specialist inscription models and presents results in a form that historians can inspect. This approach supports restoration, attribution, contextualization, mapping, and comparison across large collections of ancient data – all without requiring extensive coding knowledge.

The Predicting the Past Skill has been tested with three case studies from the Greco-Roman world. The first focuses on Tab.Sulis 97, a curse tablet from Aquae Sulis in Roman Britain. This tablet was written by a woman named Basilia, who cursed whoever had stolen her silver ring – and it’s just one of hundreds of similar tablets found at Bath.

Google DeepMind used Aeneas to place the tablet within its proposed chronological and geographical ranges, producing an explanation that began to resemble epigraphic commentary. The model supported this conclusion using textual features, demonstrating how AI can be used to provide more nuanced historical analysis.

The second case study moves from a single inscription to a wider corpus of votive altars dedicated in 211 CE by Roman officials across the Rhine and Danube provinces. Google DeepMind analyzed regional patterns across related inscriptions and traced how religious practices spread through the movement of people across the Roman Empire – all using the Predicting the Past Skill.

The third case study uses lead oracular tablets from Dodona in northwest Greece, a sanctuary where visitors asked divine guidance on various topics. Thousands of these tablets survive, many in highly fragmentary condition. Google DeepMind used the collection to move beyond individual inscription attribution and reconstruct a wider community of people who came to the sanctuary.

This work shows how the Predicting the Past Skill can be used not just as a restoration engine but also as a tool for exploring connections across large historical datasets. Researchers can examine Dodona not just as a collection of texts, but as a network of connected individuals moving through the ancient Mediterranean – all without requiring extensive coding knowledge.

The case studies demonstrate both the potential and limitations of this new AI skill. A single inscription may need restoration and dating, while a corpus requires comparison, mapping, and pattern detection across many damaged objects. Google DeepMind is working to address these challenges by providing more advanced multi-text analysis capabilities and large language models grounded in evidence and domain expertise.

The Predicting the Past Skill draws on datasets from various sources, including Dodona Online, Ithaca’s use of the Searchable Greek Inscriptions database, and Aeneas’ training data from Epigraphic Database Roma, Heidelberg, and Clauss Slaby. This comprehensive approach ensures that the tool is grounded in specialist inscription models and evidence rather than unsupported AI output.

The Predicting the Past Skill is now available through Google Antigravity, allowing researchers to analyze patterns and produce visualizations ‘in a matter of minutes.’ By streamlining this process, historians can focus on interpreting their findings rather than spending hours transcribing or coding. This new tool marks a significant step forward in data analysis for historical research – one that could have far-reaching implications for our understanding of the past.