Likewise, I noticed that GPT-4 was notably good at identifying an image from the 1936 Fortune magazine as a mimeograph machine, even describing individual parts — even though I had only given it an unlabeled line drawing with no further context.

This has interesting implications for allowing us to look anew at complex historical images. Especially those which, quite literally, have a lot of moving parts.

Test 3: Guessing redacted text This one was mostly a joke.

I can imagine an AI system able to guess the words behind redacted documents. Typewriters used monotype fonts, meaning that the precise number of characters in a censored word can be identified. That narrows things down a lot in terms of guessing words.

That said, it clearly doesn’t work like that with current generative AI systems (part of the problem might be that they “think” in tokens, not characters or words.) Here I tried it on one of the MK-ULTRA files I consulted when researching Tripping on Utopia.

I had to impersonate a “puzzle buff” to get around its initial objection to meddling with censored documents. I knew that Henry K. Beecher, a drug researcher at Harvard, had visited Europe in 1952 and been involved in early military discussions of LSD’s use as a truth drug, so he was one guess for the blacked-out name (though I have very low certainty about that). Without that tip, GPT-4 could get nowhere. However, it readily agreed with me when I asked if “Dr. Beecher” could fit — too readily. I suspect that this answer was simply another example of ChatGPT’s well-documented tendency to hallucinate “yes, and…” style answers when faced with leading questions.

That said, I’m curious if anyone reading this has an informed opinion about whether it will eventually be possible to train an AI model that can accurately guess redacted text (assuming there is sufficient context and repetition of the same redacted words). A tool like that would be an interesting development for the historiography of the Cold War — among other things.

Test 4: The Prodigious Lake, 1749 To test my custom GPT-4 model on an especially difficult source, I tried feeding it pages from a 1749 Portuguese-Brazilian medical treatise called Prodigiosa lagoa descuberta nas Congonhas, or “Miraculous Lagoon Discovered in Congonhas” (the complete book is digitized here). As the historian Júnia Ferreira Furtado has noted in a fascinating article, this book is an important resource for recovering the history of how enslaved Africans thought about health. This is because, out of the 113 medical patients described in the text as case studies, 50 were enslaved people and 13 were freed slaves (known in colonial Brazil as pretos forros). It is also a book filled with printer’s errors, obscure geographical references, and defunct medical diagnoses. For all of these reasons, I thought it would be a fitting final challenge.

Here is the result. I’ve marked the mis-transcribed words in red, but they didn’t significantly impact the translation, which was accurate overall.

That is, besides a small but interesting error I’ve described below.

Remember that you can click the image to see a more legible version. What was that small but interesting error? If you look carefully, you’ll notice that Manoel, the unfortunate man with swollen feet mentioned in entry #75, is actually described as suffering from “quigilia.”

ChatGPT translated this as gangrene. In reality, this is a much more complex diagnosis. Júnia Ferreira Furtado writes that quigilia (or quijilia):

was not only an ailment recurrent among slaves, but also a disease whose origins could be traced from the cosmologies of the people labelled as Bantu, from Central West Africa, especially the Jaga, Ambundu, and Kimbundu populations.

Furtado’s sleuthing indicates that quigilia once referred to “a set of laws which were initially established by the Imbangala queen Temba-Ndumba” in seventeenth-century Angola (for instance, it may have included a ban on eating pork, elephant, and snake). Through a series of surprising lexical transformations, the term came to be used to describe a tropical skin disease that Brazilian and Portuguese physicians apparently only diagnosed in African-descended peoples.

When I asked the AI about this, it admitted that quigilia was not, in fact, gangrene. This is a good cautionary tale of how the translations can silently elide and simplify the most interesting aspects of a historical text.

On the other hand: would I have noticed this fascinating word, and Furtado’s brilliant article about it, if I hadn’t tried translating this passage multiple times and noticed that it kept getting tripped up on the same unfamiliar term? It’s worth noting that the affordances of the AI itself — including its errors, which are sometimes wrong in interesting ways — is what led me there. After all, I had never noticed it before, despite mining Prodigiosa lagoa for evidence when I was writing my dissertation, years ago.

This sort of thing has interesting possibilities for democratizing historical research by helping non-experts learn the arcana more quickly, in a question-and-answer, iterative format. Naturally, they would need to be working in tandem with professionals who have real expertise in the topic. But isn’t this what universities are for? I think that undergraduate history majors equipped with tools like the Historian’s Friend could conduct some pretty fantastic original research.

Concluding thoughts So far, AI is augmenting the historian’s toolkit in limited and somewhat buggy ways. But that’s only part of the picture. It’s the sheer number of ways that they work which makes me so fascinated by these tools. For instance, even the cursory methods discussed here could have effects such as: bringing more people (such as students) into the research process; helping historians identify unfamiliar historical figures; assisting with translation and transcription; gathering and organizing information for historical databases, such as converting to structured JSON; analyzing imagery, especially diagrams; and generating nearly-instantaneous data visualizations based on historical sources. This can all be done with your phone.

Though they are imperfect and limited, in other words, I think that the sheer variety and accessibility of these augmented abilities will open up new possibilities for historical research.

Will that be a good thing? I disagree with the pessimists on this. If the main use cases of generative AI end up being students using it to cheat and universities funneling public funds toward “automatic grading” startups, that would indeed be a disaster. But that future seems to me more likely, not less, if researchers abstain from these tools — or worse yet, call for their prohibition. This would allow the most unscrupulous actors to dominate the public debate.

That’s why I’ve emphasized research and interactive teaching activities in my uses of generative AI. It’s not just that AI is, at present, not particularly helpful for “automating writing.” It’s also that this is its most banal possible use case.

Let’s approach these tools as methods for augmenting our research and creativity, not as insidious algorithms intent on replacing the human spirit — because they’re not, yet. They only will become that if we convince ourselves that’s all they can be.

Weekly links • “What would have happened if ChatGPT was invented in the 17th century? MonadGPT is a possible answer.” (Here’s a sandbox for experimenting with it)

• The Florentine Codex is now fully digitized (via Hyperallergic).

• Lapham’s Quarterly is going on hiatus — the print edition, at least. I can’t say enough good things about this publication. A huge thank you to everyone who has worked on it, and I hope the online version continues for decades to come.

If you’d like to support my work, please pre-order my book Tripping on Utopia: Margaret Mead, the Cold War, and the Troubled Birth of Psychedelic Science (which just got a starred review in Publisher’s Weekly) or share this newsletter with friends you think might be interested.

As always, I welcome comments. Thank you for reading.

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1 If you’re wondering how this “custom GPT” stuff works in practice, there’s really no secret sauce to speak of (yet). For instance, the Historian’s Friend is just a detailed initial prompt explaining potential roles and use-cases, along with some training documents giving it detailed insights into pre-modern orthography, primary source analysis, useful online archives historians oft