TorahML

TorahML applies machine learning to the Hebrew Bible to surface deeper patterns in the Torah — and tests every one against chance. Free alpha by SHEMIM.

Machine learning for deeper Torah study. Read the Tanakh. Test the patterns. Keep the nulls.

TorahML (Torah + Machine Learning) uses machine learning, information theory and statistics to search the Hebrew text of the Tanakh for structure a reader can miss: shared roots across books, word-play, recurring key words, numeric correspondences, parallel passages and unusual letter sequences. Its purpose is to reveal deeper layers of Torah knowledge — honestly. Every candidate pattern is compared with baselines built from shuffled or comparable text, and only patterns that beat chance are presented as findings. As AI grows more capable, TorahML is built to put stronger models to work under the same rule: models may propose; only evidence decides.

AI Torah study here means surfacing, measuring and testing candidate patterns. Humans interpret their meaning. Untested candidates remain exploratory; failed null tests stay on record. Some methods require optional models or data. Discovery concept and phrase retrieval has no independent significance test and is not presented as a finding.

• Search Hebrew, Aramaic and comparative dictionaries
• Learn the 26 gematria methods with worked examples
• Read about TorahML and its null-first research approach
• Check sources, attribution and licenses

Alpha: use at your own risk. Free non-commercial research; source-specific licenses apply.