The understanding of ancient inscriptions is challenging as they have been damaged over the centuries or moved from their original location. A recent paper by DeepMind proposes Ithaca, a deep neural network that can restore the missing text of damaged inscriptions, identify their original location, and help establish the date they were created. In order to work with the damaged and missing chunks of text, the model is trained using both words and the individual characters as inputs. Ithaca generates several prediction hypotheses for the text restoration task for historians to choose. It also shows its uncertainty by giving a probability distribution over all possible predictions of geographical and chronological distribution. Saliency maps identify which input sequences contribute most to a prediction.

Machine learning techniques are providing new tools that could help archaeologists understand the past -- particularly when it comes to deciphering ancient texts. The latest example is an AI model created by Alphabet-subsidiary DeepMind that helps not only restore text that is missing from ancient Greek inscriptions but offers suggestions for when the text was written (within a 30-year period) and its possible geographic origins. "Inscriptions are really important because they are direct sources of evidence ... written directly by ancient people themselves," Thea Sommerschield, a historian and machine learning expert who helped created the model, told journalists in a press briefing. Due to their age, these texts are often damaged, making restoration a rewarding challenge. And because they are often inscribed on inorganic material like stone or metal, it means methods like radiocarbon dating can't be used to find out when they were written. "To solve these tasks, epigraphers look for textual and contextual parallels in similar inscriptions," said Sommerschield, who was co-lead on the work alongside DeepMind staff research scientist Yannis Assael.