The Future of AI Is Helping Us Discover the Past
In February, Google released an upgraded version of its Gemini artificial intelligence model. It quickly became a publicity disaster, as people discovered that requests for images of Vikings generated tough-looking Africans while pictures of Nazi soldiers included Asian women. Building in a demand for ethnic diversity had produced absurd inaccuracies.
Academic historians were baffled and appalled. “They obviously didn’t consult historians,” says Benjamin Breen, a historian at the University of California, Santa Cruz. “Every person who cares about the past is just like, ‘What the hell’s going on?'”
Rewriting the past to conform with contemporary political fashions is not at all what historians have in mind for artificial intelligence. Machine learning, large language models (LLMs), machine vision, and other AI tools instead offer a chance to develop a richer, more accurate view of history. AI can decipher damaged manuscripts, translate foreign languages, uncover previously unrecognized patterns, make new connections, and speed up historical research. As teaching tools, AI systems can help students grasp how people in other eras lived and thought.
Historians, Breen argues, are particularly well-suited to take advantage of AI. They’re used to working with texts, including large bodies of work not bound by copyright, and they know not to believe everything they read. “The main thing is being radically skeptical about the source text,” Breen says. When using AI, he says, “I think that’s partly why the history students I’ve worked with are from the get-go more sophisticated than random famous people I’ve seen on Twitter.” Historians scrutinize the results for errors, just as they would check the claims in a 19th century biography.
Last spring Breen created a custom version of ChatGPT to use in his medieval history class.
Writing detailed system prompts, he generated chatbots to interact with three characters living during an outbreak of bubonic plague in 1348: a traveler passing through Damascus, a disreputable apothecary in Paris, and an upstanding city councilor in Pistoia. The simulation worked like a vastly more sophisticated version of a text-based adventure game—the great-great-great-great-grandchild of the 1970s classic Oregon Trail.
Each student picked a character—say, the Parisian apothecary—and received a description of their environment, followed by a question. The apothecary looks out the window and sees a group of penitents flagellating themselves with leather straps. What does he do? The student could either choose one of a list of options or improvise a unique answer. Building on the response, the chatbot continued the narrative.
After the game, Breen assigned students to write papers in which they analyzed how accurately their simulation had depicted the historical setting. The combined exercise immersed students in medieval life while also teaching them to beware of AI hallucinations.
It was a pedagogical triumph. Students responded with remarkable creativity. One “made heroic efforts as an Italian physician named Guilbert to stop the spread of plague with perfume,” Breen writes on his Substack newsletter, while another “fled to the forest and became an itinerant hermit.” Others “became leaders of both successful and unsuccessful peasant revolts.” Students who usually sat in the back of the class looking bored threw themselves enthusiastically into the game. Engagement, Breen writes, “was unlike anything I’ve seen.”
For historical research, ChatGPT and similar LLMs can be powerful tools. They translate old texts better than specialized software like Google Translate can because, along with the language, their training data include context. As a test, Breen asked GPT-4, Bing in its creative mode, and Anthropic’s Claude to translate and summarize a passage from a 1599 book on demonology. Written primarily in “a highly erudite form of Latin,” the passage included bits of Hebrew and ancient Greek. The results were mixed but Breen found that “Claude did a remarkable job.”
He then gave Claude a big chun
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