The long-standing mystery of the unread ancient scrolls of Mount Vesuvius has finally been solved after centuries, thanks to AI. The credit for it goes to artificial intelligence (AI) and the winners of a challenge that began a year ago.
What Happened: The Vesuvius Challenge, initiated in March by Nat Friedman, Daniel Gross, and Brent Seales, was designed to decode the Herculaneum Papyri, a set of ancient papyrus scrolls charred by the eruption of Mount Vesuvius almost two millennia ago.
The challenge, which boasted over $1M in rewards, drew a worldwide pool of competitors and collaborators to address the issue using computer vision, machine learning, and sheer determination.
The mystery was solved in December, less than a year after the challenge’s launch.
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The winning team, comprising Youssef Nader, Luke Farritor, and Julian Schilliger, received the Vesuvius Challenge Grand Prize of $700,000 for their entry.
Their solution employed three distinct model architectures, each corroborating the others’ results, with the most compelling images often produced by a TimeSformer-based model.
The team’s work, now available as open source, has enabled the world to access once again the thoughts of our ancestors, which have been preserved in mud and ash for nearly 2,000 years.
The victorious team will be honored at the Getty Villa Museum in Los Angeles in March this year.
Why It Matters: The Vesuvius Challenge’s success underscores the power of artificial intelligence and machine learning in solving historical mysteries.
The winning team’s use of computer vision aligns with the recent trend of using AI to protect artists’ work from unauthorized usage by AI image generators, as seen with the Nightshade tool.
Furthermore, the application of AI in interpreting ancient texts mirrors the use of AI in transforming human thoughts into written words, as demonstrated by the AI-powered helmet developed by scientists.
This achievement in the Vesuvius Challenge marks a significant milestone in the field of AI, opening up new possibilities for historical and scientific research.
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