Machine learning and AI may be deployed on such grand tasks as finding exoplanets and creating photorealistic people, but the same techniques also have some surprising applications in academia: DeepMind has created an AI system that helps scholars understand and recreate fragmentary ancient Greek texts on broken stone tablets.
These clay, stone or metal tablets, inscribed as much as 2,700 years ago, are invaluable primary sources for history, literature and anthropology.
They’re covered in letters, naturally, but often the millennia have not been kind and there are not just cracks and chips but entire missing pieces that may comprise many symbols.
Such gaps, or lacunae, are sometimes easy to complete: If I wrote “the sp_der caught the fl_,” anyone can tell you that it’s actually “the spider caught the fly.” But what if it were missing many more letters, and in a dead language, to boot?
Not so easy to fill in the gaps.
Doing so is a science (and art) called epigraphy, and it involves both intuitive understanding of these texts and others to add context; one can make an educated guess at what was once written based on what has survived elsewhere.