Wolfson Alumni featured on the cover of Nature Magazine

Historians Thea Sommerschield, (Marie Curie Fellow at Ca' Foscari University of Venice and fellow at Harvard University’s CHS, formerly in the Faculty of Classics, University of Oxford) and Yannis Assael, (Staff Research Scientist, DeepMind) both Alumni of Wolfson College, have been featured on the cover of Nature Magazine, a leading multi-disciplinary science journal, with their state-of-the-art AI technology.

Ithaca is the first deep neural network that can aid historians in not only restoring the missing text of damaged inscriptions, but also identifying their original location, and establishing the date they were written.

In a new research paper, published by the scientific journal, Nature, the researchers have already used Ithaca to redate a series of important Athenian decrees from the 5th century BCE. Using the new model, the research team has shed light on current disputes in Greek history, including the dating of a series of important Athenian decrees thought to have been written before 446/445 BCE. New evidence recently presented by historians suggests the 420s BCE as a more appropriate time period. Remarkably, Ithaca’s average predicted date for the decrees is 421 BCE, aligning with the new evidence and demonstrating how machine learning might contribute to historical debates.

The team is currently working on versions of Ithaca trained on other ancient languages and historians can already use their datasets in the current architecture to study other ancient writing systems, from Akkadian to Demotic and Hebrew to Mayan.

To make the research widely available to researchers, educators, museum staff and others, DeepMind has partnered with Google Cloud and Google Arts & Culture to launch a free interactive version of Ithaca. To aid further research, they have also open sourced their code, the pretrained model, and an interactive Collaboratory notebook.

Read the full paper published in Nature: 'Restoring and attributing ancient texts using deep neural networks': https://www.nature.com/articles/s41586-022-04448-z