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Unveiling the Past: How AI is Reading the Herculaneum Scrolls
In October 2023, a pivotal moment in the field of papyrology unfolded for Federica Nicolardi, a researcher at the University of Naples, Italy. An email appeared on her phone featuring an image that hinted at a transformative breakthrough in her work—an AI-assisted reading of a fragment from the papyrus scrolls scorched in the eruption of Mount Vesuvius in AD 79. This revelation signals not only a scientific milestone, but also a profound shift in how scholars will access and understand ancient texts.
The Herculaneum Scrolls: A Treasure Trove of History
The fragile scrolls recovered from the Herculaneum villa, buried under volcanic ash, have long posed a challenge for historians and scientists. These burnt artifacts, discovered in the 18th century, contain writings that have remained unreadable for nearly 2,000 years. Until now, most experts accepted that the delicate layers of these carbonized texts could not be peeled apart without causing significant damage.
One of the most significant breakthroughs in recent years is the Vesuvius Challenge, which actively harnesses artificial intelligence to unlock the messages trapped within charred papyrus. The illuminated lines of Greek writing from the latest scroll image represent not just a fragment of text, but a glimpse of a previously unknown philosophical discourse, possibly linked to the Epicurean philosopher Philodemus.
A New Era in Papyrology: Artificial Intelligence to the Rescue
AI has already begun revolutionizing a range of fields from medicine to finance, and now it is breathing new life into our understanding of ancient texts. By employing sophisticated machine learning techniques, researchers are not only extracting the contents of the Herculaneum scrolls, but they are also redefining the questions scholars can ask about these artifacts.
Nicolardi notes the remarkable potential of AI, stating, “This technology can uncover not just what we want to know, but also what we are capable of discovering.” The promise of AI tools lies in their ability to recognize patterns, fill in missing characters, and analyze texts written in languages rarely spoken today.
The Science Behind AI Text Decoding
The foundation of this project relies on neural networks—computational models inspired by the human brain and capable of learning from vast datasets. For ancient texts, these networks utilize various techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze images of damaged scripts and suggest missing words or characters.
Early Innovations
The journey to melding AI with papyrology began in 2017 at the University of Oxford. Researchers Yannis Assael and Thea Sommerschield were among the first to combine machine learning with ancient inscriptions. Initially training RNN-based models on thousands of Greek texts, they found that the model could predict missing characters with surprising accuracy.
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The Ithaca model, which followed, utilized transformer architecture to comprehend intricate language patterns and context more effectively. This model achieved an impressive 62% accuracy rate in filling gaps in ancient texts, outperforming human efforts significantly.
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Across the Globe: AI in Action
While Italian researchers focus on the scrolls from Herculaneum, their peers worldwide are also employing AI methods to decode ancient languages. In South Korea, scholars are digitally analyzing extensive archives written in Hanja, an ancient script, dating back to the 14th century. Teams utilize multilingual approaches to translate records that no one can easily access today.
Across the Mediterranean, researchers are working on very different challenges. For instance, Katerina Papavassileiou’s team in Greece uses RNN techniques to restore missing elements from Mycenaean tablets—demonstrating the global trend of AI reshaping our understanding of the past.
The Limitless Potential of AI in Archaeology
The scope of AI’s impact extends beyond the Herculaneum scrolls. Computer scientist Brent Seales and his colleagues have pioneered ‘virtual unwrapping’ technologies to read texts hidden within unopenable scrolls. By scanning scrolls in high-resolution computed tomography (CT) and using algorithms to analyze surface textures, researchers aim to capture scores of texts waiting to be read.
The collaborative efforts of the Vesuvius Challenge have resulted in a grand prize for innovative AI models, reflecting a growing recognition of the need to speed up legibility efforts. A recent competition showcased contributions from over 1,000 international research teams striving to decode the Herculaneum scrolls using neural networks.
Looking Ahead: A Flood of New Discoveries
As AI continues to evolve, scholars like Seales envision a bright future for papyrology. With the possibility of deciphering an entire library of Herculaneum scrolls concealed in collections around the world, researchers anticipate a dramatic increase in ancient texts available for interpretation.
Seales’ ambition is clear: he wishes to read every scroll he can find, tapping into ‘the invisible library’ of hidden texts in various cultures. With such advancements, he states, “We are on the cusp of what could be the biggest discovery in ancient studies.”
Key Takeaways:
- AI and Papyrology: Artificial intelligence is transforming how researchers decode ancient texts, promising significant advancements in historical studies.
- Collaborative Efforts: The Vesuvius Challenge exemplifies how global collaboration can lead to significant breakthroughs in archaeology and historical linguistics.
- The Future of Discoveries: Researchers are optimistic that they will soon unveil vast amounts of newfound knowledge from ancient civilizations, fundamentally altering our understanding of their histories.
As machine learning technology progresses, the landscape of historical research will undoubtedly change, revealing insights that humanity has long yearned to know. The story of the Herculaneum scrolls is just the beginning of a new chapter in uncovering the enigmas of our past.