I will soon be posting an update here on my Geographic Lens on Stories (GLOS) project as a follow-up to this one, and it will be co-authored with OpenAI’s ChatGPT (!).

GLOS has very clearly become a collaboration with both ChatGPT and Anthropic’s Claude, one that has evolved in unexpected ways I reflect on here (unaided by a bot…well apart from a word tweak or two). But first, as a reminder, the broad goals for GLOS include:

• Developing a methodology for extending the dimensions of place computationally to include the conceptual content of text and spoken word emerging from places—beginning with folkloric text;
• Learning about the recently emerging NLP and cultural analytic methods based on LLMs, embeddings, and machine learning generally;
• Experiencing, evaluating, and grappling with the practice of collaborating with a machine in the design and execution of a research project and publication of its results and tools.

I use the term “collaborate” advisedly. Collaboration with a machine is on its face an odd concept, but as the project has evolved over several months it has become clear that is what is happening. With all (deserved) humility, what has been accomplished so far could not have happened without both ChatGPT and Claude (“the bots”). The “Path Forward” described in the forthcoming project update post will likewise rely on both. That is, while I am capable of designing and building a GLOS project alone, it would be inferior and take far longer.

Some roles the bots have played so far include:

• providing helpful background and references to help familiarize me with the field of folkloristics (I have no training in it), and more specifically, the computational folkloristics practiced by a relative handful of scholars to date.
• tutoring me in (i) the ins and outs of LLM-based methods, contrasting them with my decade-old NLP methods; (ii) the API services provided by OpenAI and Anthropic; (iii) approaches to statistical validation of results
• providing encouraging and at times helpful feedback to my conceptual framing of the GLOS project
• generating (at impossible speed) unlimited high quality Python scripts to implement tasks I design, then troubleshooting and/or refining them to my specification to make them work as required
• drafting elaborate natural language prompts to (i) derive categorical structure from a natural language text corpus; (ii) derive natural language prompts used to create embeddings, from the data held in that categorical structure

This entire exercise has not been without challenges, hiccups, and a bit of nonsense. As everyone knows, bots using LLMs can and do get things wrong sometimes. To the extent I can catch errors they will earnestly try to fix them.

It is hard for me to express how extraordinary this new technology’s impact has been. I think folks who don’t write code may not appreciate the bots’ capabilities in that respect. ChatGPT is no slouch at coding, but Claude is astounding. It is not only a question of incredible time savings, but quality. Both know more Python than I ever will (NB: I’m pretty handy with it). Beyond that, they know what is “pythonic,” a principle defined by ChatGPT as code that is “beautiful, idiomatic, clear, and respectful of Python’s strengths.”

In short, while the bots I’m working with are not partners in any human sense, they have become indispensable in ways I hadn’t anticipated. In the next post you’ll see how they "think" of what we’ve done so far and plan to do going forward.