Comparing Scrolls and Codes: From ELS Inspiration to AI Discovery of Thematic Lineages in Genesis and Ruth
Presenter Type
UNO Undergraduate Student
Major/Field of Study
History
Other
Multidisciplinary Studies
Advisor Information
Dr. Curtis Hutt
Location
CEC RM #201/205/209
Presentation Type
Poster
Poster Size
48" x 36"
Start Date
22-3-2024 2:30 PM
End Date
22-3-2024 3:45 PM
Abstract
Comparing Scrolls and Codes: From ELS Inspiration to AI Discovery of Thematic Lineages in Genesis and Ruth
This research compares the thematic similarities of two books from the Hebrew Bible, Genesis and Ruth by leveraging multi-dimensional artificial intelligence techniques and drawing initial inspiration from the mysticism of the technique of Equidistant Letter Sequences (ELS) popularized in "The Bible Code".
"Equidistant Letter Sequences in the Book of Genesis" by Witztum et al., 1994, subjectively reported that pairs of conceptually related words tend to appear in close proximity to one another in a sort of intentionally-encoded way. Although this finding is largely debunked, the analysis of conceptually related words holds value in literary and computational criticism. For example, Word2Vec, a machine learning technique, can be used to find words that are used in similar contexts and organize them in high-dimensional space. It works by converting words into numerical vectors, capturing their semantic and contextual relationships.
When ancient texts are organized in this way in high-dimensional space, it can be shown that words and phrases used in similar contexts are "closer". Furthermore, it can be shown that Genesis and Ruth are very close neighbors in such a high-dimensional space. This means modern machine learning techniques are capable of helping bible scholars uncover patterns in ancient texts that are not obvious in 2D space.
This research showcases how modern machine learning techniques such as Word2Vec and generative AI enable quick analysis of ancient texts by generating potentially insightful computations. Using these computations, questions can be asked. The responses can then be analyzed and validated through slower and more manual traditional research methods. This research showcases how combining machine learning techniques with slower research methods can lead to a richer understanding of the patterns found in ancient texts.
In summary, this research looks at how a simple machine learning technique can help uncover thematic similarities in two books of the Hebrew Bible that may not be overtly obvious, how a deeper exploration of biblical texts can be enhanced through machine learning, and how Word2Vec, a technology evolved by Google researchers circa 2013, can be used to map out semantic proximities between words in high-dimensional space.
This research does not focus on the technicalities of the machine learning models used. It focuses on the insights they have facilitated. This study underscores the potential of machine learning as a tool for uncovering and articulating the nuanced linguistic and semantic threads that weave through ancient texts, offering fresh perspectives on their intertextual relationships.
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Comparing Scrolls and Codes: From ELS Inspiration to AI Discovery of Thematic Lineages in Genesis and Ruth
CEC RM #201/205/209
Comparing Scrolls and Codes: From ELS Inspiration to AI Discovery of Thematic Lineages in Genesis and Ruth
This research compares the thematic similarities of two books from the Hebrew Bible, Genesis and Ruth by leveraging multi-dimensional artificial intelligence techniques and drawing initial inspiration from the mysticism of the technique of Equidistant Letter Sequences (ELS) popularized in "The Bible Code".
"Equidistant Letter Sequences in the Book of Genesis" by Witztum et al., 1994, subjectively reported that pairs of conceptually related words tend to appear in close proximity to one another in a sort of intentionally-encoded way. Although this finding is largely debunked, the analysis of conceptually related words holds value in literary and computational criticism. For example, Word2Vec, a machine learning technique, can be used to find words that are used in similar contexts and organize them in high-dimensional space. It works by converting words into numerical vectors, capturing their semantic and contextual relationships.
When ancient texts are organized in this way in high-dimensional space, it can be shown that words and phrases used in similar contexts are "closer". Furthermore, it can be shown that Genesis and Ruth are very close neighbors in such a high-dimensional space. This means modern machine learning techniques are capable of helping bible scholars uncover patterns in ancient texts that are not obvious in 2D space.
This research showcases how modern machine learning techniques such as Word2Vec and generative AI enable quick analysis of ancient texts by generating potentially insightful computations. Using these computations, questions can be asked. The responses can then be analyzed and validated through slower and more manual traditional research methods. This research showcases how combining machine learning techniques with slower research methods can lead to a richer understanding of the patterns found in ancient texts.
In summary, this research looks at how a simple machine learning technique can help uncover thematic similarities in two books of the Hebrew Bible that may not be overtly obvious, how a deeper exploration of biblical texts can be enhanced through machine learning, and how Word2Vec, a technology evolved by Google researchers circa 2013, can be used to map out semantic proximities between words in high-dimensional space.
This research does not focus on the technicalities of the machine learning models used. It focuses on the insights they have facilitated. This study underscores the potential of machine learning as a tool for uncovering and articulating the nuanced linguistic and semantic threads that weave through ancient texts, offering fresh perspectives on their intertextual relationships.