
© Faigenbaum-Golovin et al.Graphical representation of the team's results. By comparing word usage and sentence patterns, their AI-based statistical model identified three distinct writing styles, or scribal traditions, shown here in yellow, blue and green.
AI is transforming every industry, from medicine to film to finance. So, why not use it to study one of the world's most revered ancient texts, the Bible?
An international team of researchers, including Shira Faigenbaum-Golovin, assistant research professor of Mathematics at Duke University, combined
artificial intelligence, statistical modeling and
linguistic analysis to address one of the most enduring questions in biblical studies: the identification of its authors.
The study is
published in the journal PLOS One
.By analyzing subtle variations in word usage across texts, the team was able to distinguish between
three distinct scribal traditions (writing styles) spanning the first nine books of the Hebrew Bible, known as the Enneateuch.
Using the same AI-based
statistical model, the team was then able to determine the most likely authorship of other Bible chapters. Even better, the model also explained how it reached its conclusions.
But how did the mathematician get here?
In 2010, Faigenbaum-Golovin began collaborating with Israel Finkelstein, head of the School of Archaeology and Maritime Cultures at the University of Haifa, using mathematical and statistical tools to determine the authorship of lettering found on pottery fragments from 600 B.C. by comparing the style and shape of the letters inscribed on each fragment.
Their discoveries were featured on the front page of
The New York Times."We concluded that the findings in those inscriptions could offer valuable clues for dating texts from the Old Testament," Faigenbaum-Golovin said. "That's when we started putting together our current team, who could help us analyze these biblical texts."
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