That title links two different assertions:
- Using chatbots increases my sense of intellectual agency.
- Because I am an intellectual outsider, being fluent in using chatbots becomes a superpower.
To understand those two statements properly you need to know something about my background and my position in today’s intellectual ecosystem. First I’ll give you the fanciful version, which you can also find at the bottom of the column to your right, and then I’ll give you the straight version. They we’re ready to look at those two statements.
The myth and its explication
In the early 1970s I discovered that “Kubla Khan” had a rich, marvelous, and fantastically symmetrical structure. I'd found myself intellectually. I knew what I was doing. I had a specific intellectual mission: to find the mechanisms behind “Kubla Khan.” As defined, that mission failed, and still has not been achieved some 40 odd years later.
It's like this: If you set out to hitch rides from New York City to, say, Los Angeles, and don't make it, well then your hitch-hike adventure is a failure. But if you end up on Mars instead, just what kind of failure is that? Yeah, you’re lost. Really really lost. But you’re lost on Mars! How cool is that!
Of course, it might not actually be Mars. It might just be an abandoned set on a studio back lot.
Who knows? Does it matter? It's been one hell of a journey so far.
* * * * *
As I’ve explained in various documents my career started when I encountered Coleridge’s “Kubla Khan” in my senior year at Johns Hopkins (1968-1969). I wrote a term paper about it and then used it as the subject of my master’s thesis. The idea was to do a structuralist analysis of the poem. Structuralism was just coming in vogue and Johns Hopkins was at the center of the movement. Unfortunately, I “broke” structuralism if you will, and found myself in conceptual limbo. I’ve written about that several times, but you can find one version in this document, Xanadu, GPT, and Beyond: An adventure of the mind, which sketches most of my intellectual career (it skips my interest in cultural evolution). I found myself forced to study computational semantics with David Hays, in linguistics, while ostensibly getting a PhD in English Literature at SUNY Buffalo. The upshot is that by the time I completed my PhD in 1978 I had a set of skills and interests that didn’t fit into any discipline recognized by the academic world. I held a faculty position in the department of Language, Literature, and Communication at the Rensselaer Polytechnic Institute in Troy, NY, for a few years. After I left I became a ronin scholar ranging freely across literature, cognitive science, cultural evolution, neuroscience, and music.
I am thus an intellectual outsider. I’m not so far outside that my work is utterly unintelligible. Not at all. I have been able to publish, and in some very fine places, too. But it’s a stretch. I’ve found intellectual allies and fellow travelers as well. But my work has not been taken up either by my peers or by younger researchers. And that has consequences for the large language models at the center of the current AI revolution.
Any training corpus based on the contents of the web must necessarily contain many documents that I’ve written. I know that my 2001 book on music, Beethoven’s Anvil, is in the Anthropic copyright suit. I’ve been placing both published articles and unpublished working papers in several document repositories (SSRN, Academia.edu, ResearchGate) since 2009 and I’ve been blogging since 2006, first at a group blog, The Valve (now defunct, though you can find it on the Wayback Machine), and then at my own New Savanna since 2010. So, my ideas have entered into the various web-wide LLMs that have been created but, since they’ve not been taken up by other scholars, they will not have had much of an impact on the language models.
And THAT’s what makes working with ChatGPT and Claude so interesting to me.
Me and the chatbots
I have probably generated a thousand or more pages (2K?) of text files through my interactions with the bots in the last month. Sessions will typically last half an hour to an hour or more, fill 10s of pages of documents, and leave me exhausted. ChatGPT and Claude work out the implications of my ideas, as given in my prompts or uploaded documents, much more rapidly than I would be able to do, and often more thoroughly and extensively as well. What I find particularly satisfying is that they work out implications that I lack the skills to do.
For my entire career I’ve been working on topics that have not been given a formal technical treatment. My goal is to identify what mathematical ideas can be applied to them, mathematical ideas that I do not myself possess. “How,” you might ask, “can you possibly do that?” While I don’t have much technical training in math beyond high school – I satisfied my undergraduate math requirement with a course in symbolic logic – I have sophisticated mathematical intuitions, often visually based, that I’ve developed through reading and through interacting with researchers who have mathematical skills that I don’t have. Guess, what? ChatGPT and Claude have such skills as well.
Thus I’m having a lot of fun working out the implications of some of my ideas. There’s a good example right around the corner: Toward a Biophysics of Poetry. As I explain in the post, I have a long-term interest, not only in “Kubla Khan,” (KK) but it “This Lime-Tree Bower My Prison,” (LTB) which shares some motifs with KK. Otherwise they are very different poems. LTB is a narrative written in blank verse. KK is not a narrative – what it is, is not clear – and has an elaborate prosody, with lines of varying lengths and an elaborate rhyme scheme. I told ChatGPT 5.2 that I thought KK needed the elaborate prosody to hold it together, to make the content cohere, and that that was a function of the physical nature of the system. After all, both the sound and the sense of a poem, or any text, are supported by the nervous system, which is a physical system.
ChatGPT picked up on that immediately and suggested that the sound structure functioned as a carrier wave for the content. I wouldn’t have occurred to me to think about a poem’s sound structure as a carrier wave (& I’ve been thinking about that for decades), but as soon as ChatGPT said that it made sense. And we were off to the races with a nice conversation about the biophysics of poetry, which I asked ChatGPT to summarize in the short essay included in the post. Now, if you look through that essay, you’re not going to see any math. But it’s there, lurking behind the talk of dynamical stability and one-dimensional projections of high-dimensional semantics and so forth.
And this happens to me all the time. It’s exciting. Some more extensive examples:
From Mirror Recognition to Low-Bandwidth Memory, A Working Paper, https://www.academia.edu/143347141/From_Mirror_Recognition_to_Low_Bandwidth_Memory_A_Working_Paper
What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet, https://www.academia.edu/126773246/What_Miriam_Yevick_Saw_The_Nature_of_Intelligence_and_the_Prospects_for_A_I_A_Dialog_with_Claude_3_5_Sonnet_Version_2
Rough Notes on Virtual Reading, On literary study in the Fourth Arena V2, https://www.academia.edu/150286029/Rough_Notes_on_Virtual_Reading_V2
The creative potential
Think about what’s going on. The two chatbots I use, ChatGPT and Claude, are trained on the entire web. To a first approximation, their underlying LLMs model all of human knowledge to date. But what about the knowledge that be developed from existing knowledge by exploiting connections and resonances that are not anywhere explicit in those models? That, I believe, is what I am able to do precisely because I am an intellectual outsider. I have built up a career’s worth of potential connections and resonances that have yet to be exploited in the creation of new knowledge. Will all these connections and resonances work out? Of course not. But surely some of them will.
And I’m certainly not the only one in this position. There are others.
What I’m suggesting is that the SOA chatbots are most useful to those who understand the existing literature in their fields, but have managed to go beyond it, to get just far enough “outside the (proverbial) box” that they can see new pathways to knowledge. If your ideas exist comfortably within the envelope of existing knowledge, then your work must necessarily be limited to incremental addition of detail. If you are so far outside the box that you can’t connect with the existing body of knowledge, then you’re in crazy-land. No, to get maximum creative benefit from the LLMs you need to be just outside, but not too outside.
NOTE: My guess is that creativity is of relatively little concern for most of the applications being developed using chatbots. I assume that the objective in most cases is to automate relatively routine intellectual work, work that doesn’t require creativity, but which may be time-consuming and exacting.
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