Some notes on how I discovered formal structure in some literary texts, Part 1: Four cases

What is intelligence? How does it work? Those and related questions have been with us for a while, but their salience has been amplified in the wake of ChatGPT, which is given us a demonstration of machine intelligence, whatever that is, that is accessible to anyone with access to the web. Those questions have ceased to be of interest primarily to psychologists, philosophers, and AI researchers. Now “everyone” is interested in them.

Perhaps the single most pressing issue is: While machine intelligence ever reach, or even surpass, human intelligence? The concept of intelligence is so obscure, however, that it’s difficult to produce compelling answers. Earlier in this year I decided to take another crack at the problem, AI, Chess, and Language 1: Two VERY Different Beasts. That post has, in turn, led to a series of posts on chess and language.

More recently, I did a long post centered on analogy, Intelligence, A.I. and analogy: Jaws & Girard, kumquats & MiGs, double-entry bookkeeping & supply and demand, and another in which I talked about wanting an AI that could determine the formal structure of literary texts, What do I personally want from an AI? [as soon as possible, too, NOT in the distant future]. Neither of those posts says anything about chess, but they’re certainly on the language side of the ledger. That’s led to the idea of reflecting on what I’ve had to do to discover formal structures in texts.

I’ve done a lot of this. Which texts should I use?

Kubla Khan

“Kubla Khan” is the most important example. That’s where I started, and it’s the most richly developed example. Furthermore, I’ve published that work in the formal literature and I’ve already written quite a bit (informally) about how I came to do that work.

Here's the articles about the poem:

Articulate Vision: A Structuralist Reading of "Kubla Khan", Language and Style, Vol. 8: 3-29, 1985, https://www.academia.edu/8155602/Articulate_Vision_A_Structuralist_Reading_of_Kubla_Khan_

“Kubla Khan” and the Embodied Mind, PsyArt: A Hyperlink Journal for the Psychological Study of the Arts, Article 030915, November 29, 2003, https://www.academia.edu/8810242/_Kubla_Khan_and_the_Embodied_Mind

These papers have background:

Touchstones • Strange Encounters • Strange Poems • the beginning of an intellectual life, November 2015, https://www.academia.edu/9814276/Touchstones_Strange_Encounters_Strange_Poems_the_beginning_of_an_intellectual_life

Beyond Lévi-Strauss on Myth: Objectification, Computation, and Cognition, Working Paper, February 2015, pp. 20-27, https://www.academia.edu/10541585/Beyond_L%C3%A9vi_Strauss_on_Myth_Objectification_Computation_and_Cognition

I originally published the “Touchstones” piece in a journal edited by one of my teachers in graduate school, the late Art Efron: Paunch 42–43: 4–16, December 1975. It’s about my years at Johns Hopkins and how I came to do a Master’s Thesis on “Kubla Khan.” I’ve linked to a version which I’ve updated with notes that I’ve inserted into the text, notes commenting one what’s happened since then. The Lévi-Strauss piece is generally about the intellectual significance of his work on myth while the section, “Into Lévi-Strauss and Out Through ‘Kubla Khan’” is specifically about how Lévi-Strauss’s structuralism guided my approach to the poem.

Three Shakespeare Plays

This is about three Shakespeare plays, a comedy (Much Ado About Nothing), a tragedy (Othello), and a so-called romance or tragi-comedy (The Winter’s Tale). In each of these plays a man wrongly suspects his beloved of betraying him with another man. Consider the following diagram:

Along the left I've listed five dramatic functions which some character must play. In the columns for each play I've indicated the character that takes these functions. The point of the diagram as the we move from one genre to the next (in this order) one function seems to disappear. Othello has no mentor, but he has a deceiver; and Leontes has neither a mentor nor a deceiver. What I think is going on is that functions are, in effect, being absorbed into the protagonist. At the play's opening, Othello is senior enough in the world that he has no need of a mentor. Leontes is king in his world, so there is no one higher. As for being deceived about his wife, he does that to himself, no external agent required.

There's more to the pattern than that, but that’s enough to give you an idea about what’s going on. The analytic point is that this is a pattern which becomes visible only when you compare texts. That’s something that Lévi-Strauss did in his work on myth.

Here's the article I published about that:

At the Edge of the Modern, or Why is Prospero Shakespeare's Greatest Creation? Journal of Social and Evolutionary Systems, 21 (3): 259-279, 1998, https://www.academia.edu/235334/At_the_Edge_of_the_Modern_or_Why_is_Prospero_Shakespeares_Greatest_Creation

Metropolis

I’m talking about the manga originally published by Osamu Tezuka in 1949 and not Fritz Lang’s movie of the same title. As you know, manga are Japanese comic books, or graphic novels. I’m including this as a case in the first place because it is an example of ring-form construction, which I depict in the following diagram:

Notice the numbering at the left and the descriptive labels at the right. I’m including it in the second place because it is the first time I used to whole-text table to conduct an analysis. The diagram is reduced from that table.

Here’s the article:

Tezuka's Metropolis: A Modern Japanese Fable About Art and the Cosmos, in Uta Klein, Ktaja Mellmann, Steffanie Metzger, eds. Heurisiken der Literaturwissenschaft: Disciplinexterne Perspektiven auf Literatur. mentis Verlag GmbH, 2006, pp. 527-545, https://www.academia.edu/7959634/Tezuka_s_Metropolis_A_Modern_Japanese_Fable_about_Art_and_the_Cosmos

Here's the analytic table:

Descriptive Tables for Tezuka’s Metropolis, A Note on Descriptive Method, Working Paper, 2016, 8 pp., https://www.academia.edu/27370076/Descriptive_Tables_for_Tezukas_Metropolis_A_Note_on_Descriptive_Method

Heart of Darkness

It turns out that Joseph Conrad’s Heart of Darkness is also a ring-form text, or a close variant. Consider the following chart:

Each bar represents a single paragraph in the text. The length of the bar corresponds to the number of words in the text. The bars are arranged in the order they exist in the text, first to last, from left to right.

Notice that there is one paragraph that is noticeably longer than all the others. It has 1502 words. It is the structural center of the text. There’s much more to be said about that paragraph and its place in the text. But this is not the place to do it.

You can find those discussions, and others, in this working paper:

Heart of Darkness: Qualitative and Quantitative Analysis on Several Scales, Version 3, Working Paper, August 9, 2018, 49 pp., https://www.academia.edu/8132174/Heart_of_Darkness_Qualitative_and_Quantitative_Analysis_on_Several_Scales_Version_3

I have two main reasons for including this case as an example of discovery. In the first case, that chart depicts something that is clearly an objective fact of Conrad’s text. It’s not something I conjured out of the mist through interpretive wizardry. I make the same claim about the formal structures in the other three cases. That’s why I’m calling them discoveries; they’re as real as a vein of gold in the Klondike.

And the process by which I discovered that structure in Heart of Darkness was, if anything, more serendipitous than a gold strike. The prospector who gets a gold strike was looking for gold. I wasn’t looking for a ring-form pattern in Conrad’s text. Not only that, but charting paragraph lengths is not something one normally does with literary texts or, as far as I know, any other texts. I did it on a whim. While I did have a formal reason for looking at that particular paragraph, it didn’t have anything to do with ring-composition. While examining that paragraph I noticed that it seemed unusually long. Perhaps it was even the longest one in the text. So I decided to take a look. Out of mere curiosity.

But I’ve said enough about that so far. I’ll say more later. Here’s the point at issue: How do you construct at AI to discover something of a kind that it didn’t know exists? That’s what I did in this case and in “Kubla Khan” as well. The pattern I discovered across those three Shakespeare plays is arguably related to the sort of thing I’d seen in Lévi-Strauss’s work on myth, though quite different in the details. As for the ring-composition in Metropolis, ring-composition is well-enough known, though not much studied by literary critics. I was alerted to it by the late Mary Douglas. 

The importance of visualization

I don't see how I could have done any of that work without visualization. I could have spotted that something was going on in the Shakespeare case, and maybe, just maybe, I could have made an argument without that simple diagram. But the fact is that that's not how I did it. More precisely, I might have imagined the argument I ended up making without drawing that table. But I can't imagine trying to write it up without making the table. The table allows you to see the structural phenomenon at a glance.

In the other three cases diagrams were an essential part in seeing the phenomenon at all. Since I didn't include any diagram from the "Kubla Khan" work in this post, I will here explicitly state that diagramming was essential to my "Kubla Khan" work from the beginning.

All of which is to say that in each case, two distinctly different methods of thought were important. What does that imply about the possibilities of artificial intelligence?

Ramble on ChatGPT, GOATLiC, Intelligence, and stuff

Once again my brain is all jammed up so I’m having trouble getting anything done. Why? Because there are these things I want to do, things I know I should do, and they keep colliding into one another whenever I attempt to actually do something. So it’s time to ramble on through to see where I am.

Report on ChatGPT

That’s still hanging over my head. I’ve been working on it since December and it’s still not done. Most of it, 90%, maybe 95%, but it’s still not done. Why haven’t I done it?

I don’t know. Maybe at this point it just bores me. But maybe I’m afraid to finish it. It’s not like that’s the last thing I’m going to do on ChatGPT. I’ve already done a fair amount of work since the cutoff point for research-to-be-included. And maybe I don’t want to finish because I know that when I’m done it will likely end up in the same bottomless pit everything else does. It’ll be out there on the internet, but who cares?

That’s always the question: Who cares?

Anyhow, I need to say something about metalingual definition of Anthropic’s idea of Constitutional AI and something about prompt engineering and barriers to entry. Alan Kay thought we made a mistake in the promulgation of computing by making everything so “user friendly” that too few people learned to program. I suspect that may have been a barrier-to-entry problem. Some professionals learned to program because it was a useful skill, though they otherwise had little interest in programming. That’s mostly in technical disciplines. For the rest of us, low-to-moderate programming skill simply didn’t get us enough to be worth the opportunity cost. Does the utility of prompt engineering change that. If all you want is to look up stuff, then the answer is “no, it isn’t.” But maybe some skill in prompt engineering might be more widely useful.

The discipline of literary criticism

I’ve been working on this thing since December as well. This is my series on the greatest literary critics (Greatest of All Time, Literary Critics, aka GOATLiC). I’m still hung up on Howard Bloom, which is where I was the last time I rambled, back in March. This time, though, I may have found a way out: Susan Sontag. I want to use her early essay, “Against Interpretation,” as a fulcrum on which to lever my treatment of Bloom.

Why? In the first place, that essay is very well known, and justly so, and it seems people are still thinking about it, a half century after it was originally published in 1964. In a way, then, it’s current, whereas I’m not sure that anything by Bloom is. In that essay, to put it crudely, she says that criticism – for she’s talking about art, film, and literature – is divided between interest in form and interest in interpretation. Interpretation is evasion and dismissal. We need to pay more attention to form. In particular (p.8):

What is needed, first, is more attention to form in art. If excessive stress on content provokes the arrogance of interpretation, more extended and more thorough descriptions of form would silence. What is needed is a vocabulary—a descriptive, rather than prescriptive, vocabulary—for forms.

YES, of course. That’s what I’ve been working on. And the profession may be waking up to that.

Thus, in writing, or attempting to write, an obituary for Theory and Critique (Bloom’s School of Resentment) Elizabeth S. Anker and Rita Felski and say (from their introduction to Critique and Postcritique, 2017, p. 6):

In what might appear to be a reprise of Susan Sontag’s well-known argument in “Against Interpretation”—a stirring manifesto for an erotics rather than a hermeneutics of art—critics have questioned the value of reducing art to its political utility or philosophical premises, while offering alternative models for engaging with literary and cultural texts.

What’s this have to do with Bloom? On the one hand, as far as I can tell, he’s made no contribution a critical “erotics,” if by that one means attention to description and form. Interestingly enough, though, he wasn’t very interested in interpretation either, at least not since The Anxiety of Influence in the early 1970s. He was doing something else, something which, I need to argue, or more likely, merely assert, hasn’t proved to be very fruitful.

The other thing is that in The Western Canon, he keeps asserting that he’s doing all this in the name of “the aesthetic.” But he says next to nothing about what that is. We’d all have been off if he’d channeled his “inner Sontag,” if I may, and said explicitly just what that is and then used that as a means of examining his chosen texts. Note that I don’t mean he should have adopted Sontag’s ideas, but rather he should have adopted her mode and intellectual register and said something intelligible about the aesthetic. That would have been valuable. But he didn’t do that. Instead, he stuck us with his ex-cathedra pronouncements. 

But in the end, why should anyone care about Bloom’s pronouncements? That he’s a very smart guy, perhaps as brilliant as any literary critic of the last half-century, that’s not enough in itself. He didn’t use his brilliance in a fruitful way.

Chess, Language, and AI

Here’s another on-going project that’s left over from March’s ramble. The idea is to think about what intelligent does, where I’m interested in the processes of search and evaluation. I’m thinking of this as case studies in the operation of intelligence. The history of science, and intellectual history more generally, is full of such material. But I want to reflect on work that I’ve done for the simple reason that I have better access to records of process: What have I had to do in the course of my work? Thus I’ve just sketched out one potential post:

Seven discoveries I’ve made in literature [form]

• Kubla Khan
  
• Sir Gawain and the Green Knight
  
• The Cat and the Moon
  
• Shakespeare Triad
  
• Metropolis
  
• Heart of Darkness
  
• Obama’s Eulogy for Clementa Pinckney

We’ll see how that does.

Finally...

Metalingual definition, constitutional AI, and interpretability

That’s a post about large language models that needs to be done, real soon now.

What do I personally want from an AI? [as soon as possible, too, NOT in the distant future]

I can think of two things off the top of my head: 1) an assistant to deal with my computing needs, and 2) a system to examine literary texts, movies, and other expressive texts to determine, A) whether or not they exhibit ring-form composition, and if not that, then B) what form do they have.

Computing assistant

I’ve already written a post about this: What do I want from my AI Assistant? [control, that's what]. Here’s a chunk of that post:

The fact is, I’m wedded to my computer and to the internet, email and world-wide web. I really couldn’t function very well without them, not as an intellectual. And for the most part I don’t have to spend all that much time fiddling around with things in order to keep them working. But I do have to spend some time. And, yes, I probably could use some changes. But I don’t have the skills I’d need to make those changes, much less the time.

It's obvious that I need an AI Assistant to take care of all of this. Some years ago I sketched out ideas for a PowerPoint Assistant I could control through natural language. I also imagined that what I was thinking about for PowerPoint could be generalized:

The PowerPoint Assistant is only an illustrative example of what will be possible with the new technology. One way to generalize from this example is simply to think of creating such assistants for each of the programs in Microsoft’s Office suite. From that we can then generalize to the full range of end-user application software. Each program is its own universe and each of these universes can be supplied with an easily extensible natural language assistant. Moving in a different direction, one can generalize from application software to operating systems and net browsers.

Back then – the notes originally date from 2002-2003 – the technology we’d need to do that didn’t exist. Now it does.

Who’s going to control these AI Assistants? The end-users or the MegaCorps?

I don’t have much more to say about that at this point beyond observing that I think privacy and security will be real problems here. If I were to think about writing detailed specs for that I might start with this working paper I prepared some years ago about how my use of personal computers has changed over the years: Personal Observations on Entering an Age of Computing Machines.

Determining the formal structure of texts

The thing is, this isn’t a deep problem, this isn’t rocket science. There are these discussions that talk about AIs that will one day find a cure for cancer, figure how to make a practical fusion reactor, discover a grand unified theory, and solve climate change. Determining the form of a literary text isn’t like that. It’s not easy, but it doesn’t take anything like genius either.

I specify ring-form composition in particular because it is something fairly specific to look for. I think that’s easier than simply requestion: Tell me the form of this text. I’ve spent a lot of time looking for ring-form composition, in narratives, poems, and movies and blogged about it quite a bit. I’ve prepared a number of working papers on it as well.

What makes it tricky is that I can’t come up with a list of specific indicators that can be quickly identified as signs that the text has a ring-form. Nor can a specify exactly what feature to look for. It varies from one text to another. The only general thing I can say is that the text have this general form:

A, B, C...X...C’, B’, A’

The first section of the text is echoed by the last, the second is echoed by the next to last, and so forth, and there is a central section that serves as a turning point.

I have a post where I presented ChatGPT with a text of “St. George and the Dragon,” which does have ring-form, and asked it to analyze the text. The results of that experiment are, at best, inconclusive. The form is more obvious than in post texts, and the text is a short one.

What would it have done with Shakespeare’s Hamlet, which is known to exhibit ring-form? I have no idea. James Ryan has identified ring-composition in 26 of Shakespeare’s plays. I’d like an AI that could verify his work, or at least rough out a description which Shakespeare experts could then check.

And so forth and so on through every text in the canon, however you want to identify the canon. But, by all, non-canonical texts as well. I’ve found ring-composition in a manga by Osamu Tezuka, Metropolis (certainly in the Japanese pop-culture canon), and in Obama’s “Eulogy for Clementa Pinckney,” which is not normally within the compass of specifically literary texts. I’ve also found it in films, such as the 1954 Japanese film, Gojira, or the “Pastoral Symphony” episode of Disney’s Fantasia. Films present a particular challenge as LLMs can’t view them and I suspect we’ve got a way to go to create AIs that can view films and parse the themes and action.

Identifying ring-composition is one thing. Identifying other formal structures is something else. You want to identify formal structures that appear in text after text, whether verbal or filmic. Structural description may not be rocket science, but it’s not obvious either. You have to compare texts with one another to see what makes sense. “What makes sense,” that’s a vague methodological prescription if ever there was one. But it’s the best I can do in a short post.

I could go on and on. But this is quite enough to state the problem. Perhaps I’ll say some more later.

Intelligence, A.I. and analogy: Jaws & Girard, kumquats & MiGs, double-entry bookkeeping & supply and demand

Think of this post as an adjunct to my series on A.I., chess, and language, which is about the structure of computation in relation to difficult problems.

NOTE: It runs long, so sit back, relax, pour a Diet Coke, some San Pellegrino, a scotch, light up a spliff (assuming it’s legal where you live) — whatever you do to make online reading tolerable — and settle in for the duration. Or you could just print it out.

I’m interested in the general question of what it would mean to say that an A.I. is more intelligent than the most intelligent human, something like that. That’s an issue that’s being debated extensively these days. For the most part I don’t think the issue is very well formulated.

To be honest, I don’t find it to be a very compelling issue. It doesn’t nag at me. If others weren’t discussing it, I wouldn’t bother.

The notion of intelligence itself remains vague despite all the discussion that it has occasioned. I rather expect that as A.I. becomes more developed, we’ll develop a more sophisticated understanding the issue. The general notion seems like it can be captured in a simple analogy:

Intelligence is to a mind’s capacity for dealing with cognitive tasks, such as finding a cure for cancer

AS

Horsepower is to an engine’s ability deal with mechanical tasks, such as the acceleration of an automobile.

But I don’t want to take up the general issue in this post. Rather, I want to look at analogical reasoning. I start with 1) a specific kind of analogical reasoning, interpreting narratives, 2) use some remarks Geoffrey Hinton made about analogy to introduce some more general remarks, 3) move on to another specific example, an analogy between double-entry bookkeeping and supply and demand, and then 4) conclude by wrapping things up with a quick comparison to computing chess, which can be implemented in a well-defined search space, while searching for and explicating analogies cannot.

Interpreting Texts: Girard and Jaws

A lot of literary interpretation proceeds by constructing analogies between events that happen in texts and patterns of behavior specified in some theory about human behavior. Psychoanalysis has theories about how individuals behave in their personal lives; Marxism has theories about how social class and roles in the economy affect individual action. René Girard has a theory about imitation and sacrifice. That’s what I want to look at.

Early in 2022 I did a Girardian interpretation of Spielberg’s Jaws. At the end of that year I had ChatGPT do the same. Of course ChatGPT can’t watch movies, but Jaws is well-known there’s lots of stuff about it on the web, including scripts, though I don’t know whether or not any of the available scripts give the dialog word-for-word as it is in the film (the scripts I downloaded, for free, did not). Now, I didn’t just ask ChatGPT to use Girard’s ideas to interpret the film. First I prompted it about the film itself, and specifically about Quint. Once I’d established that it had a reasonable grasp of the film I then brought up Girard, generally at first, then specifically about mimetic desire. Then I asked it: “How does mimetic desire work in Jaws?” It gave a reasonable answer: 

Mimetic desire is a concept developed by the French philosopher and literary theorist Rene Girard. It refers to the idea that human desire is often based on the desire of others, and it can be seen at work in the film Jaws in a number of ways.

One example of mimetic desire in the film is the way that the townspeople of Amity Island desire the safety and security that the town’s police chief, Martin Brody, is able to provide. When the town is terrorized by a great white shark, the townspeople look to Brody to protect them and to find a way to stop the shark. This desire for protection and security is mimetic, as the townspeople are basing their desire on Brody’s ability to provide it.

Another example of mimetic desire in the film is the way that the shark itself is driven by its own insatiable hunger and desire. The shark is driven to attack and kill the townspeople because it desires their flesh and blood, and it is unable to control its own desire. This mimetic desire ultimately leads to the shark’s own destruction, as it is killed by the town’s police chief in a final confrontation.

Overall, Girard’s concept of mimetic desire can be seen as a useful way of understanding the dynamics at play in the film Jaws, and the ways in which desire can be both destructive and transformative.

The second paragraph is reasonable, but the third is problematic. Hunger is not mimetic on the face of it. But I was happy that it ChatGPT seemed to at least have some idea of that nature of the reasoning required, where to look and what to look for in the film. I decided not to pursue that and turned my attention to sacrifice, first Girard, then the application to Jaws.

The form of the required reasoning is analogical. In effect, is there an analogy between Girard’s general statements about mimetic desire and sacrifice and the specific events in Jaws? I would argue that setting up the question is more difficult than working out an answer to it. I note that it’s not the kind of question that has only one answer; thus the argument I actually made in my paper is more sophisticated than the one ChatGPT came up with through my prompting. What is it that brought me to pose the question in the first place?

I watch a fair amount of streaming video, but I don’t write about most of the titles nor do routinely a watch a particular title with the intention of writing about it. That decision is made later. I had no intention of writing about Jaws when I decided to watch it. I was simply filling a hole in my knowledge of movies – I’d never seen the film, which I knew to be an important one. Once I’d watched the film, I read the Wikipedia article about it, something I routinely do, mostly to ‘calibrate’ my viewing experience. The article noted that the sequels were not as good as the original. I decided to see for myself. I was unable to finish watching that last two sequels (of four), but I watched Jaws 2 at least twice, and the original three or more times. It was obvious that the original was better than the others. I did the multiple viewings in part to figure why the original was better. I was on the prowl, though I hadn’t yet decided to write anything.

I decided there were two reasons the original was best: 1) it was well-organized and tight while the sequel sprawled, and 2) Quint, there was no character in the sequel comparable to Quint. I have no all but decided that I would write about Jaws.

I posed a specific question: Why did Quint die? Oh, I know what happened in the film; that’s not what I was asking. The question was an aesthetic one. As long as the shark was killed the town would be saved. That necessity did not entail the Quint’s death, nor anyone else’s. If Quint hadn’t died, how would the ending have felt? What if it had been Brody or Hooper?

It was while thinking about such questions that it hit me: sacrifice! Girard! How is it that Girard’s ideas came to me? I wasn’t looking for them, not in any direct sense. I was just asking counter-factual questions about the film.

With Girard on my mind I smelled blood. I had a focal point for an article. I started reading articles from various sources, making notes, and corresponding with my friend, David Porush, who knows Girard’s thinking much better than I do. Can I make a nice tight article? That’s what I was trying to figure out. It was only after I’d made some preliminary posts, drafted some text, and run it by David, that I decided to write an article. It turned out well enough that I decided to publish it.

Now, when we’re thinking about whether or not A.I.s will come to exceed our intelligence, are we imagining them going through such a process? For this kind of search and exploration is central to human thinking. I certainly do this sort of exploration when thinking about other things, such as the structure of human cognition, the nature of cultural evolution, the functioning of the nervous system, and so forth. This blog is a 14-year record of my explorations, during which I’ll gather some of them together in a more formal way and write a working paper which I’ll then post at Academia.edu, SSRN and ResearchGate. Every once in a while I’ll write an article which I’ll submit for publication in the formal academic literature – a few of those have gotten published. And then there are the monthly pieces I publish in 3 Quarks Daily, which is quite different from the formal academic literature. And of course I’ve got pages and pages of unpublished notes that support all this activity.

Is this kind of exploratory work part of the routine of the superintelligent A.I., or does it go straight for the good stuff, cranking out fully-realized work without need of exploratory effort? If so, how does it know where to dig for the good stuff? Is that what superintelligence is, knowing where the good stuff is without having to nose around? No one says anything about this. Perhaps they’re thinking about the Star Trek computer. But it knows where to look because Spock points it in the right direction.

This brings us back to Jaws. There is a world of difference between what I did in writing about Jaws and what ChatGPT did. I did the hard part, figuring out that there was a specific intellectual objective there, Jaws and Girard. Once I’d done that there was still work to do, quite a bit of work, but it was of a different kind. I was no longer prospecting for intellectual gold. I was now constructing a system for mining the ore and then refining it into gold. ChatGPT only had to do the last part, dumping the ore into the hopper and cranking out the refined metal. I told it where to look, Jaws, what to look for, Girard’s ideas, and gave it some help turning the crank.

A year later, in January of 2023, I decided to see how ChatGPT would do without all of my prompting. I gave it this prompt:

Stephen Spielberg is an important film-maker. Jaws is one of his most important films because it is generally considered to be the first blockbuster. Rene Girard remains an important thinker. Can you use Girard’s ideas mimetic desire and sacrifice to analyze Jaws?

It didn’t do so well. It needed my prompting to get it through the exercise.

Now, no one is claiming that ChatGPT is superhuman in any respect but its ability to discourse on anything. But GPT-5, who knows, maybe it’ll be superhuman in some interesting way. If not GPT-6, or GPT-7, or maybe we’ll need a more sophisticated architecture, but surely at some point an A.I. will surpass us in the way that we surpass mice. Perhaps so.

But I have no sense that these breezy predictions are supported by thinking about how human intelligence actually goes about solving problems. I does no good to say, but it’s an A.I.; it works differently. Well, maybe yes, maybe no, but there has to be some kind of process. At the moment the human process is the only example we have. Perhaps we should think about it.

Just how is it that Girard popped into my mind in the first place? How do we teach a computer to look around for nothing in particular and come up with something interesting?

Analogy: Kumquats and MiGs

As I remarked above, the process of interpreting Jaws is an analogical one. So let’s think about analogy more generally. I’m thinking in particular of some remarks Geoffrey Hinton made at a panel discussion in October of 2023. You can find the video here. I’ve transcribed some remarks:

1:18:28 – GEOFFREY HINTON: We know that being able to see analogies, especially remote analogies, is a very important aspect of intelligence. So I asked GPT-4, what has a compost heap got in common with an atom bomb? And GPT-4 nailed it, most people just say nothing.

DEMIS HASSABIS: What did it say ...

GEOFFREY HINTON: It started off by saying they're very different energy scales, so on the face of it, they look to be very different. But then it got into chain reactions and how the rate at which they're generating energy increases– their energy increases the rate at which they generate energy. So it got the idea of a chain reaction. And the thing is, it knows about 10,000 times as much as a person, so it's going to be able to see all sorts of analogies that we can't see.

DEMIS HASSABIS: Yeah. So my feeling is on this, and starting with things like AlphaGo and obviously today's systems like Bard and GPT, they're clearly creative in ... New pieces of music, new pieces of poetry, and spotting analogies between things you couldn't spot as a human. And I think these systems can definitely do that. But then there's the third level which I call like invention or out-of-the-box thinking, and that would be the equivalent of AlphaGo inventing Go.

OK. Let’s start from there. Given that GPT-4 “knows about 10,000 times as much as a person,” what procedure will it use “to see all sorts of analogies that we can't see”? I’m thinking of that procedure as roughly analogous to the exploratory process I undertake whenever I decided to watch some video. Every once in a while I decide to write about one of the titles. Most of the time, time, though, what I write isn’t as elaborate as my article about Jaws and Girard – I’ve collected many of those pieces under the rubric of Media Notes, though most of those pieces do not focus on analogical reasoning.

What’s the procedure by which an GPT-4 would search through all those things it knows and come up with the interesting analogies? There isn’t one and I suppose it’s a bit churlish of me to suggest that Hinton should specify one. But really, if there he has no procedure to suggest, then what’s he talking about? We know how chess programs search the chess tree. How do we search through concept space for analogies? Alas, while the chess tree is a well-defined formal object, the same cannot be said of concept space, which is little more than a phrase in search of and explication. And how do we evaluate possible analogy-pairs?

Perhaps the simplest procedure is simply to ask. That’s something I recently tried. Here’s the prompt I gave to ChatGPT:

Who’s the GOAT of Economics? Tyler Cowen on His New AI Book & More! [Bonus: from double-entry bookkeeping to supply and demand]

The influence of AI on economics:

59:06: I think we will start with small economies – you know take a village of 50 Native Americans up in Alaska – and we'll just gather all the data we have from that Village – we'll start with numbers – but then we'll go around and we'll talk to each person about what they do – their living – their income – what they buy – and we'll take all that and feed it into some supersized meta large language model and we'll have a model of that economy.

now how large we can make those models I'm not sure – but we'll start very small and it will progress – and it will be a fundamentally different way of doing economics – not sure how well it will work – at first it will be attacked but we won't be able to resist – we'll do it I do think we'll learn some things from that.

Why is early economics so poor?

1:29:20: look at 17 century economics it's pretty piss poor – I mean it's barely anything – so the 17th century Salamancans have some basic pieces – the early mercantilists have some basic pieces – but it's still fairly primitive.

there's something about economics that is more counterintuitive than we moderns realize I think – that's my conclusion – but I still find this a big puzzle

On the need for conceptual equipment

Obviously those early thinkers don't have the necessary conceptual equipment. Ideas can be constructed over other ideas, almost layer by layer. There is an inner architecture to thought. We may not understand that architecture at all well, but it is real nonetheless. Just what is required for economics, I don't know.

But I've thought about it a little. In my second piece in the GOAT Literary Critics series I do a quick and dirty comparison between economics and literary criticism. I settle on Malthus as my economist (not realizing that Adam Smith was earlier) because he clearly was worried about the future, worried about the future in a way that was new to the late 18th century. I don't know where that conception of the future came from, but it is new.

Cowen was talking about writing a paper about the conceptualization of pricing. The 17th century writing on the issue was not very good. For example, they don't have a conception of supply and demand. 

That's when rank 3 thought was emerging, to invoke the scheme David Hays and I wrote about in The Evolution of Cognition. There we argue that the early modern conceptual revolution was precipitated by the assimilation of Arabic arithmetic, leading to algorithms (derived from the name of the Arabic thinker, al-Khowarizmi) and effective calculation. It's downstream of that that we get conceptual foundations capable of supporting economic thought. That's where I'd look for the solution of Cowen's problem. 

Double-entry bookkeeping, calculus and other things

It's the next day (Dec. 12) and I've been thinking about these matters, including querying ChatGPT on related matters: Supply and Demand, Equilibrium & Calculus, Exponential Growth, Marginal Revolution, and Double-entry book-keeping. I've also skimmed the Adam Smith chapter in Cowen's book. The focus on time that I had in the literary critics post (linked above), is correct. Change over time is central to economic thinking. 

In his comments, Cowen mentioned Euclid and the calculus as being brilliant and difficult, so what's so difficult about supply and demand? – something like that. Well, math is one thing, applying it to the world in an illuminating way is another. First, you've got to conceptualize phenomena in the world in a way that can accept the appropriate mathematical formalization. Calculus may have been formulated in the 17th century, but it wasn't linked to economics until the 19th. It took Adam Smith and others to get the subject matter into a form that could accept mathematical formulation, no?

So, as I've said above, ideas can be constructed over other ideas, layer by layer. Here's the opening sentence of the Wikipedia article on calculus: "Calculus is the mathematical study of continuous change, in the same way that geometry is the study of shape, and algebra is the study of generalizations of arithmetic operations." What's the order these subjects are taught in school? Geometry, algebra, and then calculus. Why? Because the earlier subject provides conceptual foundations for the latter, no? The study of geometry gets you used to thinking within a formal system. Algebra then applies that to the task of generalizing over arithmetic, thus laying the foundations for the study of continuous change. Layer by layer.

But how do we get to supply and demand? I'm not prepared to give a detailed answer to that question. It's too difficult and I don't have the necessary conceptual equipment, though Hays and I gave some indications of what's needed in The Evolution of Cognition. Here's the question I'd ask myself: What does double-entry bookkeeping have in common with supply and demand? Double-entry bookkeeping dates back to the late 15th century and is a practical discipline. What becomes visible by 'going meta over that practice?

Double-entry bookkeeping is a system for maintaining closure over a set of transactions that grow over time as transactions are added to the list. We've got closure and change over time. Supply and demand change over time. Where's the closure, the dynamic closure? How do supply and demand complement one another in a say similar to the balance of debit and credit accounts in double-entry bookkeeping? How was Adam Smith able to get "on top" of that in the way that previous thinkers could not? How does the famous metaphor of the invisible hand do its work? 

I note that there are various uses to which metaphor is put. Some metaphors are used to explain technical concepts in a non-technical way. Other metaphors are used to explain concepts in the only way the thinker was capable of at the time. Smith's invisible hand is of the latter kind.

On the importance of algorithmic thinking

Let me conclude by quoting a passage from The Evolution of Cognition:

It is easy enough to see that algorithms were important in the eventual emergence of science, with all the calculations so required. But they are important on another score. For algorithms are the first purely informatic procedures which had been fully codified. Writing focused attention on language, but it never fully revealed the processes of language (we’re still working on that). A thinker contemplating an algorithm can see the complete computational process, fully revealed.

The amazing thing about algorithmic calculation is that it always works. If two, or three, or four, people make the calculation, they all come up with the same answer. This is not true of non-algorithmic calculation, where procedures were developed on a case-by-case basis with no statements of general principles. In this situation some arithmeticians are going to get right answers more often than others, but no one can be sure of hitting on the right answer every time.

This ad hoc intellectual style, moreover, would make it almost impossible to sense the underlying integrity of the arithmetic system, to display its workings independently of the ingenious efforts of the arithmetician. The ancients were as interested in magical properties of numbers as in separating the odd from the even (Marrou 179-181). By interposing explicit procedures between the arithmetician and his numbers, algorithmic systems contribute to the intuition of a firm subject-object distinction. The world of algorithmic calculations is the same for all arithmeticians and is therefore essentially distinct from them. It is a self-contained universe of objects (numbers) and processes (the algorithms). The stage is now set for experimental science. Science presents us with a mechanistic world and adopts the experimental test as its way of maintaining objectivity. A theory is true if its conceptual mechanism (its "algorithm") suggests observations which are subsequently confirmed by different observers. Just as the results of calculation can be checked, so can theories.

Not only experimental science, but economics as well.

Appendix: ChatGPT on the analogy between double-entry bookkeeping and supply and demand

I asked ChatGPT to explicate the analogy between double-entry bookkeeping and supply and demand. As you can see below, it did an excellent job, more complete and thorough than I had done. Note, however – and this is very important, I'd already done the (difficult) job of noticing that there is a (worthwhile) comparison to be made. I noticed this possibility shortly after I asked myself: What's so difficult about understanding supple-and-demand? Why was I able to pose that question? Because I knew that double-entry bookkeeping is something that was invented in the late-medieval early modern era and that it would have been common practice in the commercial world at the time thinkers were struggling to conceptualize supply and demand.

What kind of relationship am I implying there? Nothing in particular beyond the fact that double-entry bookkeeping was around. It may or may not have played a direct role in conceptualizing supply and demand dynamics. As ChatGPT pointed out in its response, we're dealing with two different domains, accounting and economics. They may be related, but they are by no means the same. It would take a bit of intellectual imagination to apply the pattern of actions in accounting practice to conceptualizing the relationship between buyers and sellers in the open market. 

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