This is a somewhat different ramble. Normally I ramble on about things I want to work on but can’t quite prioritize them. So I gather them together in one place so I can look at them all, all at once. And then sort things out.
This time I’m looking at what’s been going on in the last two months or so and reminding myself what I’ve been doing with my two interlocutors, ChatGPT and Claude. It’s really quite amazing, and exhausting. I’ve been having them review work I’ve done, starting with having Claude review reports of the experiments I’d done with ChatGPT in 2023 and 2024. I’ve also had both of them look at papers by David Hays, some things we did together and other work I’ve done.
So, for a long time I’ve thought of myself as in the business of investigating arenas of qualitative research and figuring out how to get quantitative research out of them. Literature has been my main arena. Working with both Claude and ChatGPT has advanced me on various fronts. It’s been amazing. The chatbots have been able to work out (some of) the implications of these ideas more rapidly than I could have done, and even pushed into unexpected territory.
Thus I can now begin to think about Rank 5 cognition in a coherent way. It turns out that Miriam Yevick’s 1975 paper on holographic vs. sequential logic is Rank 5. Why? because it takes two different computational regimes as objects of thought, placing them in relation to different informatic environments. Since Hays and I put Yevick’s work at the center of one of our five principles of natural intelligence, does that make that paper Rank 5?
A discussion of my paper about ChatGPT’s inability to generate a semantic network diagram led me to a long discussion about how to train AIs to perform a task like that. That came in the wake of our discussion of virtual reading [File: Notes from Virtual Reading.docx]. This requires explicit instruction comparable to what, for example, Hays gave me when I first learned how to do it. The problem is that there is a normative element involved in learning that the AI cannot pick up simply by reading articles using semantic networks. It actually has to attempt to create such networks and have those attempts critiqued by a human, or, conceivably, someday, another AI. That same discussion led to discussions of learning about sentence diagramming, constituent structures, symbolic logic, semantic networks, and close reading.
Then I had Chatgpt look at my Coleridge work, particularly my paper about “This Lime Tree Power My Prison,” the 2003 rework of my analysis of “Kubla Khan,” and my unpublished working paper indicating how they are two different trajectories through the same mental terrain. The “Lime-Tree Bower” paper has a table indicating how the mapping between agents in the poem and a hypothetical underlying attachment mechanism changes from one section of the poem to the next. ChatGPT suggested how to construct a similar table for “Kubla Khan.” Whether or not that will work out, that’s another matter which I’ll take up in a year or three. If that works out then I’m clear to work out how these two different poems related to the same underlying neural state space (HA!).
We did similar work with Shakespeare’s plays, starting with my analysis of Much Ado About Nothing, Othello, and The Winter’s Tale. & the Chatster had helpful remarks on the relationship between The Winter’s Tale and Pandosto, from which it is derived. That is, once an explicitly mechanistic framework is established, like I have for “Lime-Tree Bower,” other things fall into place. So the theory goes. Details need to be worked out, but I’m not going to get around to that anytime soon.
And then we have cultural evolution again, which I ChatGPT and I discussed in the Virtual Reading paper. Just as you can conceive of an individual text as a trajectory through the high-dimensional state space of a human mind, so you can conceive of the long-term change in a corpus of texts, such as the 19th century Anglophone novels Matt Jockers has analyzed, that evolution is, in effect, a trajectory through an approximation to a collective mind, the Geist, or spirit, of an age. It all makes sense. Sure, there are lots of details to be worked out. But it is all now possible. We have the technical means.
And almost none of this was on my intellectual agenda for th3e first quarter of this year. It just happened. Not passively. It didn’t happen to me. Rather, it emerged through interaction with ChatGPT and Claude.
More later.
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