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ORGANIZER:MAILTO:avner.algom@gmail.com
TITLE:Deep thoughts on not-so-deep data: what between AI, archeology, and biblical studies
DTSTART:20250904T071500Z
DTEND:20250904T073000Z
SUMMARY:Deep thoughts on not-so-deep data: what between AI, archeology, and biblical studies
DESCRIPTION:In the era of data proliferation, there are many domains, where collecting large datasets is simply not feasible. How, then, can we extract meaningful patterns and insights from limited data? This talk explores how machine learning offers powerful tools for analyzing "not-so-deep" datasets.We will show how studying the geometry of data and comparing statistical distributions - whether derived from images or textual sources - can lead to compelling, statistically significant conclusions. Two case studies will illustrate this approach: the first analyzes ancient handwriting from around 600 BCE to investigate levels of literacy at the time of the Iron age; the second focuses on biblical text attribution, demonstrating how computational methods can uncover linguistic layers, infer authorship, and help disentangle the composite nature of sacred texts.Join us as we explore how AI can illuminate the ancient world, even when working with sparse data - bridging the gap between cutting-edge technology and long-standing historical questions.
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