A deep neural network trained to restore ancient Greek texts can do so with 72% accuracy when used by historians, suggests a Nature paper. The findings could assist with the restoration and attribution of newly discovered or uncertain inscriptions with improved speed and accuracy, advancing our understanding of ancient history.
To understand the history of ancient civilizations, historians study the inscriptions created by past individuals, written directly on materials — such as stone, pottery or metal — that have survived until today. However, many inscriptions have been damaged over the centuries. Their texts are now illegible and their date of writing is uncertain. Specialists in the study of inscriptions, known as epigraphers, can reconstruct missing texts, but their traditional methods are highly complex and time-consuming.
To overcome the constraints of current epigraphic methods, Yannis Assael, Thea Sommerschield and their colleagues tested a deep neural network (named Ithaca), a type of artificial intelligence that was trained to restore, date and place ancient Greek inscriptions. The authors found that Ithaca could achieve 62% accuracy when used alone to restore damaged texts, and 72% accuracy when it is used by a historian. Additionally, Ithaca could also help to determine inscriptions’ place and date of writing; in their experiments, it attributed inscriptions to their original locations with 71% accuracy and dated them to less than 30 years from the date ranges proposed by historians.
The findings could unlock the cooperative potential between artificial intelligence and historians, and improve our understanding of human history.
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