Original Post: https://activehistory.ca/2023/03/todays-ai-tomorrows-history-doing-history-in-the-age-of-chatgpt/#more-32790
Cover Image – Prompt by Bing, “Self-portrait of ‘Sydney,’ Microsoft’s Bing Chat, based its description of itself as imagined through AI image generator,” MidJourney
Mark Humphries and Eric Story
You have probably heard about OpenAI’s ChatGPT, Microsoft’s Bing Chat or Google’s Bard. They are all based on Large Language Model (LLM) architectures that produce human-like text from user prompts. LLMs are not new, but they seem to have recently crossed a virtual threshold. Suddenly, artificial intelligence—or AI for short—is everywhere. While it is true that they sometimes “hallucinate,” producing factual errors and quirky responses, the accuracy and reliability of LLMs is improving exponentially. There is no escaping it: generative AI like ChatGPT is the future of information processing and analysis, and it will change the teaching and practice of history. Although some of its effects can be felt already, its long-term implications are not as clear.
Generative Pre-trained Transformer (GPT)-based LLMs are new and powerful tools that have only been around for about five years. The rapidity with which they have evolved to produce remarkably cogent prose, complete complex tasks, and pass theory of mind tests have astonished even those that created the technology. When prompted correctly, ChatGPT—which is based on the GPT-3.5 model—can write effectively, with an engaging style, good organization, and clarity. For context, its 45 terabytes of training data alone is the equivalent of about 215 million e-books, but it cannot access the Internet.
We have had access to the beta-mode of Microsoft’s new AI-enabled Bing since 14 February and it is another leap ahead of ChatGPT. It has a similar training base but can search for information on the web and analyze large bodies of text, as well as write essays, summaries, and emails right in a new Edge browser sidebar. Most importantly, it does these tasks in seconds through a conversational approach that like ChatGPT, on a powerful neural network––that is, a series of computer processors arranged to mimic the synapses in the human brain. Using the new Bing truly feels like stepping into the future.
Transformer-based LLMs are very quickly changing the face of writing. Their appeal to plagiarists becomes clear when you realize generative AI can write a pretty good review of Face of Battle that takes a critical look at the tendency of the author, John Keegan, “to pathologize soldiers’ experiences, rather than exploring the ways in which they were coping with and adapting to the stresses of combat […arguing] that soldiers who experienced symptoms of shell shock were often engaged in a process of creative adaptation, using their symptoms as a means of coping with the stresses and traumas of war.” That is a direct quote from a ChatGPT-generated review, based on a simple prompt to analyze how Keegan’s views on shell shock have been challenged by subsequent scholars. It can produce 1,500 words of analysis (albeit without citations) to support this argument with a few carefully crafted prompts.
There is already a cottage industry on blogs, Discord, and Reddit devoted to teaching the prompt syntax necessary to produce good results. There are, of course, a number of apps promising to detect AI-generated content too, but the reality is that most of them are highly ineffectual. In fact, OpenAI’s CEO, Sam Altman, has said recently that effective software would be nearly impossible to develop: bad human writing is easy to detect, but AI writing can be almost indistinguishable from good or excellent human writing. The University of Waterloo agrees, telling its faculty that “controlling the use of AI writing through surveillance or detection technology is not recommended; AI will continue to learn and if asked, will itself help to avoid the things its own architecture is using to detect it.” Let that sink in.
Media have been quick to point out the quirks and limitations of these new LLMs, but by overemphasizing their downsides we also risk overlooking the fact that they are impressive tools and for many applications are already “good enough”. Consider that although ChatGPT is a good technical writer, it is an even better editor. The program can take original but poorly written text and make it cogent while still preserving the author’s original ideas. It can also take text written in one language and output it in another, as it translates quite well—much better than Google Translate. This holds real potential to level the playing field for ESL students and academics especially. In less intrusive ways, ChatGPT can also help experienced writers brainstorm, overcome writer’s block, or make their prose more concise.
Many friends and colleagues who have expressed skepticism of generative AI’s utility often point to its inclination to make factual errors—sometimes elaborate fabrications. Most alarmingly, the false statements it generates can be expressed with remarkable confidence—a natural result of the predictive process that poses serious problems for the spread of misinformation. While this is true, it misses, in our minds, a more important point: generative AI is a powerful tool but like any tool, it must have a skilled operator. And when the predicted text aligns with the facts (which it most often does), it actually works quite well.
This is why scholars are already starting to use it in the classroom and in their publications. A recent poll of Nature readers found that 80% had already tried generative AI tools and 43% had already used them in their research, mainly for writing code, helping to write manuscripts, conducting literature reviews, or producing presentations. History is a different discipline, of course, but the last three are things that historians do too—and because generative AI can write code, maybe more historians will want to explore digital approaches in future!
The 2nd part of this article will be published next week.
Mark Humphries is a professor of history at Wilfrid Laurier University and Eric Story is a PhD Candidate in the same department. Humphries is launching a Substack on AI and history: https://generativehistory.substack.com