How Deep Learning Deciphers Historial Documents NVIDIA Blog

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Deep learning researchers are hitting the books. By building AI tools to transcribe historical texts in antiquated scripts letter by letter, they're creating an invaluable resource for researchers who study centuries-old documents. Many old documents have been digitized as scans or photographs of physical pages. But while obsolete scripts like Greek miniscule or German Fraktur may be readable by experts, the text on these scanned pages is neither legible to a broad audience nor searchable by computers. Hiring transcribers to turn manuscripts into typed text is a lengthy and expensive process.


Why AI and deep learning are the perfect tools to help us understand the past

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The world of academia is generally not known for being on the cutting edge of technology. However, as technology rapidly advances, historians and researchers can utilize both artificial intelligence (AI) and deep learning to make their jobs easier. Artificial intelligence is the ability for a machine to imitate intelligent human behavior. Deep learning, a subset of machine learning, is the intermediary between machine learning and neural networks. Deep learning provides a fast and relatively easy way to process massive amounts of data, much of which would be tedious and time consuming for a human to process; because of this, pattern recognition is one of deep learning's greatest strengths.


Machine learning can offer new tools, fresh insights for the humanities

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Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance. Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature. As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet.


Machine learning can offer new tools, fresh insights for the humanities

#artificialintelligence

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance. Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature. As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet.


What's Next for Digital Humanities?

Communications of the ACM

Father Roberto Busa, whose project to index the works of St. Thomas Aquinas marked the beginning of Digital Humanities. In 1946, an Italian Jesuit priest named Father Roberto Busa conceived of a project to index the works of St. Thomas Aquinas word by word. There were an estimated 10 million words, so the priest wondered if a computing machine might help. Three years later, he traveled to the U.S. to find an answer, eventually securing a meeting with IBM founder Thomas J. Watson. Beforehand, Busa learned Watson's engineers had already informed him the task would be impossible, so on his way into Watson's office, he grabbed a small poster from the wall that read, "The difficult we do right away; the impossible takes a little longer."