AI Decodes Centuries-Old Manuscripts and Ciphers Researchers are using machine learning and neural networks to read damaged, encrypted, and hard-to-decipher historical texts, according to reporting by Digital Trends, the BBC, and Nature. The BBC reports a team led by computational linguist Beáta Megyesi used machine learning to help decode a 408-page Vatican manuscript encoded with 34 obscure symbols and some Arabic, revealing medicinal recipes and remedies; Megyesi is quoted saying, "It is like detective work where every symbol, pattern, and partial solution may bring us closer to someone's secrets and to a lost historical world," (BBC). Nature reports neural-network pipelines and projects such as Fragmentarium are helping to recover text from carbonized Roman scroll fragments from Herculaneum and to digitize tens of thousands of cuneiform tablets. Digital Trends describes broader efforts to train models on historical handwriting and linguistic patterns so systems can restore missing or damaged words. Editorial analysis: These developments expand the volume of readable historical data and create new interdisciplinary workflows for digital-humanities practitioners. What happened Researchers are increasingly applying machine learning and neural networks to recover text from damaged, encrypted, or otherwise unreadable historical documents, as reported by Digital Trends, the BBC, and Nature. The BBC reports a team that includes computational linguist Beáta Megyesi used machine learning to help decode a 408-page Vatican manuscript coded with 34 obscure symbols and some Arabic, revealing recipes and remedies; Megyesi said, "It is like detective work where every symbol, pattern, and partial solution may bring us closer to someone's secrets and to a lost historical world," (BBC). Nature reported that neural-network pipelines and digitization efforts such as Fragmentarium are being used to read carbonized papyrus fragments from Herculaneum and to aggregate tens of thousands of cuneiform records for analysis (Nature). Technical details Editorial analysis: Public reporting highlights two