Analogical thinking: How to succeed in “wicked” environments

A Hungarian psychologist raised three daughters to prove that any child could become a chess grandmaster through early specialization. He succeeded. Two of them became grandmasters. One became the greatest female chess player who ever lived.

Then a sports scientist looked at the… pic.twitter.com/5fzvRHMnSC

— Ihtesham Ali (@ihtesham2005) May 9, 2026

From down in the article:

Inventors with the most patents had worked in multiple unrelated fields before their breakthrough work. Comic book creators with the longest careers had drawn for the most different genres before settling. Scientists who won Nobel Prizes were dramatically more likely than their peers to be serious amateur musicians, painters, sculptors, or writers. 

The skill that mattered in wicked environments was not depth in one pattern. It was the ability to recognize when a pattern from one domain applied unexpectedly in another. That kind of thinking cannot be built by drilling a single subject. It can only be built by accumulating mental models from many subjects and learning to move between them. 

Carving at the joints: Plato, Zhuangzi, Guo Xiang

First, a prompt I gave Claude 5.4. Then Claude’s reply.

* * * * *

There’s a cliché about carving Nature at its joints.

There’s one version from Plato’s Phaedrus. Socrates has likened a well-formed speech to an animal with its various appropriately arranged parts and is now examining two different speeches on love (265e-266a):

... we are not to attempt to hack off parts like a clumsy butcher, but to take example from our two recent speeches. The single general form which they postulated was irrationality; next on the analogy of a single natural body with its pairs of like-named members, right arm or leg, as we say, and left, they conceived of madness as a single objective form existing in human beings. Wherefore the first speech divided off a part on the left, and

continued to make divisions ...

There’s a different version from Guo Xiang’s redaction of Zhuangzi (from Wikipedia):

Cook Ting was cutting up an ox for Lord Wen-hui. At every touch of his hand, every heave of his shoulder, every move of his feet, every thrust of his knee, zip! zoop! He slithered the knife along with a zing, and all was in perfect rhythm, as though he were performing the dance of the Mulberry Grove or keeping time to the Ching-shou Music.

“Ah, this is marvelous!” said Lord Wen-hui. “Imagine skill reaching such heights!”

Cook Ting laid down his knife and replied, “What I care about is the Way, which goes beyond skill. When I first began cutting up oxen, all I could see was the ox itself. After three years I no longer saw the whole ox. And now I go at it by spirit and don't look with my eyes. Perception and understanding have come to a stop and spirit moves where it wants. I go along with the natural makeup, strike in the big hollows, guide the knife through the big openings, and follow things as they are. So I never touch the smallest ligament or tendon, much less a main joint.”

Plato dates: 428-348 BC. Guo Xiang dates: 252-312 AD.

Is there any chance that this idea made its way from Ancient Greece to Ancient China? Or is it likely that we have a case of independent invention? After all, butchering an animal is a common enough task and the difference between an unskilled butch and a highly skilled one must have been apparent to many people.

The question of transmission versus independent invention is genuinely interesting here, and the honest answer is that scholars are quite uncertain, though the weight of evidence favors independent invention.

The chronological gap is suggestive but not decisive. The Zhuangzi text itself predates Guo Xiang — Zhuangzi lived roughly 369-286 BC, making him a near-contemporary of Plato, not a distant inheritor. Guo Xiang's role was that of editor and commentator, so the Cook Ding story almost certainly originates in the 4th-3rd century BC, not the 3rd-4th century AD. This matters a great deal, because it means we're not looking at a case where ideas could have filtered eastward via the Silk Road over centuries — both texts are roughly contemporaneous.

There was contact between the Greek and Chinese worlds in this era, but it was extremely attenuated. Alexander's campaigns reached Bactria and the Punjab, and there are documented cases of idea-transmission in specific domains (certain astronomical and mathematical concepts, some artistic motifs). But the philosophical literature shows no clear fingerprints of cross-cultural borrowing in this period. The intermediary links needed to carry a specific Platonic metaphor from Athens to the Warring States courts of China in the 4th-3rd century BC are simply not there.

The more compelling case for independent invention rests on a few observations. First, as you note, skilled butchery was universally observable. Any culture that slaughtered animals for food — which is to say, every culture — could notice that an expert carver seems to find the natural seams while a novice hacks. The perceptual phenomenon is available everywhere. Second, and more importantly, the two passages are actually doing quite different philosophical work with the same raw material. Plato's Socrates is making an epistemological and rhetorical point about how to carve up concepts — how a good speech should divide a subject at its natural joints rather than arbitrarily. The emphasis is on correct classification, on getting the logical structure of reality right. Cook Ding's lesson is about something closer to wu wei — effortless action, yielding to the natural order, the dissolution of the self-conscious subject into the flow of the Way. The knife goes where the ox already opens; the cook stops trying. These are recognizably different philosophical preoccupations, and each fits neatly into its own tradition without requiring outside influence.

So what you have is a case where a shared human experience — watching an expert butcher work — gets recruited into philosophical argument independently in two places, but gets recruited to illuminate quite different problems. That's actually philosophically interesting in its own right: it suggests the analogy has a kind of natural aptness that makes it available to very different thinkers facing very different questions. The butcher doesn't just carve the ox at its joints; he also, apparently, carves the history of philosophy at a joint.

Hot off the presses! Claude 3.5 Sonata explains how Shakespeare is like all-purpose flour.

In what way is Shakespeare like all-purpose flour?

What an intriguing and creative comparison! Let me think about this...

Both Shakespeare and all-purpose flour are remarkably versatile foundational ingredients that can be transformed into countless different creations. Shakespeare's works serve as a base material that can be adapted into comedies, tragedies, sonnets, modern retellings, or experimental interpretations - much like how all-purpose flour can become bread, pasta, cakes, or pastries depending on how it's used.

Both are also "processed" versions of something more raw - Shakespeare took the raw materials of human experience, language, and earlier stories and refined them into something that could be used universally, just as all-purpose flour is wheat that's been milled and refined to be suitable for many different uses.

Additionally, both have an amazing ability to bind things together - Shakespeare's works unite themes of love, betrayal, ambition, and justice, while flour literally binds ingredients together in recipes. They're both such fundamental ingredients in their respective domains that we often take them for granted!

What made you think of this particular comparison? I find it quite thought-provoking.

Thought provoking indeed. Let me count the ways... No, seriously, give me ten examples of how Shakespeare has been used as a foundational ingredient in popular culture back to, say, 1956, when The Tempest was the basis for a classic science fiction movie, Forbidden Planet.

Let me take this chronologically, focusing on some fascinating transformations of Shakespeare's works:

1. Forbidden Planet (1956) - As you mentioned, this pioneering sci-fi film reimagines The Tempest in space, with Dr. Morbius as Prospero, Altaira as Miranda, and the invisible monster as Caliban, born from the dark side of human consciousness.

2. West Side Story (1957/1961) - Leonard Bernstein and Stephen Sondheim's masterful transformation of Romeo and Juliet into a tale of rival New York street gangs, with the Jets and Sharks standing in for the Montagues and Capulets.

3. Throne of Blood (1957) - Akira Kurosawa's adaptation of Macbeth into feudal Japan, demonstrating Shakespeare's themes transcend both culture and time. The famous ending with Washizu (Macbeth) being shot full of arrows by his own men is particularly memorable.

4. Kiss Me Kate (1948-1968) - Cole Porter's meta-musical about a production of The Taming of the Shrew, where the backstage drama mirrors the play itself, had an incredibly successful Broadway run and multiple revivals.

5. Rosencrantz and Guildenstern Are Dead (1966) - Tom Stoppard's brilliant reframing of Hamlet from the perspective of two minor characters, turning Shakespeare's tragedy into an existential comedy about free will and destiny.

6. The Lion King (1994) - Disney's animated adaptation of Hamlet, with Simba as the young prince, Mufasa as the murdered king, and Scar as the usurping uncle. Even includes a Yorick-like moment with Mufasa's skull.

7. 10 Things I Hate About You (1999) - A teen comedy version of The Taming of the Shrew set in an American high school, with Heath Ledger and Julia Stiles bringing Shakespeare's sparring lovers into the modern era.

8. She's the Man (2006) - Twelfth Night reimagined in a high school setting, with Amanda Bynes playing Viola disguising herself as her brother to play soccer, rather than serve in a noble household.

9. Sons of Anarchy (2008-2014) - The entire series is structured as a modern Hamlet in a California motorcycle club, with the protagonist's father murdered by his uncle, who then marries his mother.

10. Something Rotten! (2015) - A musical comedy that imagines Shakespeare as a Renaissance rock star while two competing playwrights try to write the world's first musical, incorporating references to dozens of Shakespeare's plays.

What's particularly fascinating is how these adaptations don't just transpose Shakespeare's plots - they often use his themes and character dynamics as a foundation to explore contemporary issues, just as a baker might use all-purpose flour to create entirely new recipes while relying on its fundamental properties.

Would you like me to explore any of these adaptations in more detail? I'm particularly intrigued by how some of them, like Forbidden Planet and Throne of Blood, completely transform the setting while keeping the psychological core of Shakespeare's work intact.

The Lion King.

Ah, let me delve deeper into The Lion King's Shakespearean DNA!

The parallels to Hamlet are both obvious and subtle. The core story mirrors Hamlet's plot: a young prince's father is murdered by his uncle, who then takes the throne and marries (or in The Lion King's case, becomes the dominant male of) the former king's wife. The prince goes into exile, haunted by his father's ghost who urges him to take action.

But Disney made some fascinating adaptational choices:

• Simba is a child when Mufasa dies, unlike the adult Hamlet. This shifts the story from one of indecision to one of growth and accepting responsibility
  
• Timon and Pumbaa serve as a lighter version of Rosencrantz and Guildenstern, but they're genuinely loyal friends rather than the king's spies
  
• Rafiki combines elements of Horatio (the truth-teller) and the gravedigger (philosophical wisdom wrapped in eccentric behavior)
  
• Zazu maps loosely to Polonius, as the fussy royal advisor
  
• Nala takes on elements of Ophelia but is given far more agency - she actively seeks out Simba and fights alongside him
  
• The hyenas serve as both Claudius's corrupted court and as a broader representation of how Scar's poor leadership destroys the kingdom

Perhaps most interestingly, while Hamlet ends tragically with most of the cast dead, The Lion King transforms the story into one of renewal and restoration. It keeps Shakespeare's themes about responsibility, betrayal, and the relationship between political and natural order, but reframes them through the lens of "The Circle of Life" - turning Hamlet's existential questioning into a more affirmative message about finding one's place in the grand scheme of things.

What do you think about this transformation? I find it fascinating how Disney managed to make a children's film that keeps so much of Hamlet's thematic richness while completely changing its emotional trajectory.

Yes. Some time ago I coined the term “universal kid space” for that aesthetic region where we find the best animated films, accessible to and enjoyable by children, yet rewarding to adults as well. Seems like Shakespeare is firmly ensconced in universal kid space.

That's a brilliant observation and term! The "universal kid space" concept perfectly captures how works like The Lion King, or even more directly Shakespearean adaptations like Gnomeo and Juliet, operate on multiple levels simultaneously.

Shakespeare himself worked in a kind of "universal human space" - his plays were enjoyed by groundlings and nobility alike, mixing high philosophy with low comedy, complex political machinations with slapstick humor. His work naturally lends itself to this kind of multi-level adaptation because he was already working in multiple registers.

The themes that preoccupy Shakespeare - love, jealousy, ambition, family loyalty, betrayal, growing up, finding one's place in the world - are fundamentally human experiences that resonate across ages. A child can understand Simba's grief and guilt over Mufasa's death while an adult might see deeper parallels to Hamlet's existential crisis.

This might also explain why Shakespeare works so well in animation - the medium's ability to move fluidly between reality and fantasy, to externalize internal states, to shift scale and perspective dramatically, provides a perfect toolkit for translating his theatrical magic into visual storytelling that works for all ages.

Would you say the "universal kid space" concept applies equally well to all of Shakespeare's works, or do some translate better than others into this space?

Abu Simbel: Two Modes of Thought (an analogy)

This post was created in June of 2011. As you can see by the note immediately below, I bumped to the top back on July 29, 2013, because it was germane to my life situation at the time. It is once again germane, so I'm bumping it again.

* * * * *

These days I've got two things on my mind: 1) my current series of posts on cultural evolution, memes, and the thought of Dan Dennett, and 2) murals, Mana Contemporary, and current events in Jersey City, where I live. These are both big sprawling messes and meshes of ideas, very difficult to get a hold of. It is in THAT context that I re-post this note from 2011 on conceptual styles, particularistic and holistic. How do you combine them, because that's what I'm now wrestling with, the need to combine these two styles into a single synthetic act–actually, two synthetic acts, one about cultural evolution and the other about civic life in Jersey City.

Once upon a time the two temples of Abu Simbel sat on the western bank of the Nile River in Nubia, that is, southern Egypt. Then it was decided to dam the Nile at Aswan, creating a large lake. And that lake, it was realized, would, in time, submerge those temples.

What to do? The temples must be saved.

A number of plans were devised, and sometime during the process National Geographic did an article on the problem, and the proposed solutions. I read that article in my youth – I was, maybe, 12, 13, somewhere in there – and was quite impressed. Not so much with the temples, but with the proposed solutions. And not so much with them directly, but because two of them seemed to embody different ways of thinking about problems and working toward solutions.

12.31.24: Now that I think about it, a third method was proposed: Simply build a large wall around the temple so it is protected from the rising water.

One proposal was to cut the temple in cubes roughly two meters on a side. The cubes would then be moved, one by one, to higher ground, where they would be reassembled. This is what was done.

Another proposal was to cut the temple free from its matrix in one huge block. Then you place thousands of hydraulic jacks under that block and jack it up, fractions of a millimeter at a time. As I recall, it was estimated that it would take a year or more to raise the temple at the rate of, say, an inch a day.

This is the proposal that grabbed my imagination. It seemed so impossible and fantastic at the same time. How do you make that first cut? How do you slip the jacks under the bottom surface? How do you coordinate the jacks? How do you . . . ?

I suspect, though, that it mostly it grabbed my imagination because it seemed to me that’s how I thought about complex problems, and, as such, it contrasted with a different way of thinking about complex problems, a way represented by the cut-it-into-chunks approach. Though I couldn’t do so then, now I could assign labels to these two modes of thinking. Heck, I could probably supply several different pairs of labels.

But I won’t. Because that would reduce this story to those labels. And that’s not how the story exists in my mind, and that’s not how I use it as an object to think with. To this day.

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:

On the similarities between a kumquat and a MiG: ChatCPT chases analogies

We all know that not so long-ago Geoffrey “AI Godfather” Hinton went up on a mountain where he received a heavy granite tablet. As he was holding it a bolt of lightning struck the tablet, leaving the words “You Are Doomed” carved deeply into its surface. Hinton came down from the mountain, tablet in tow, quit his post at Google, and proceeded to warn the world about the dangers of Superintelligent AI.

In early October of last year he took part in a panel discussion about AI and creativity:

One of his reasons for believing in the impending superintelligence of AI has to do with reasoning by analogy. Starting at about 1:18:10:

GEOFFREY HINTON: Let me give you-- let me give you an example of something creative that GPT-4 can already do that most people can't do.

So we're still trapped in the idea of thinking that logical reasoning is the essence of intelligence when we know that being able to--

TOMASO POGGIO: --but some people.

GEOFFREY HINTON: Well--

TOMASO POGGIO: Yeah.

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? [LAUGHTER]

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.

My initial reaction was: Very clever, very clever indeed! But then I thought, I’ll bet GPT-4 could explicate any analogy you ask it to. The cleverness is in picking the right analogy, not in justifying it. Justifying is easy; coming up with an analogy worth the effort of justification, that’s difficult. If GPT-4 knows 10,000 times as much as a person and it has all that compute, then maybe it can waste time far more extravagantly than any person can afford to. Whatever that is, it’s not intelligence.

So I decided to run a little experiment, not with GPT-4, but with plain old ChatGPT, aka the Chatster. I presented it with ten analogies I thought of rather quickly and asked it to explicate them. It did so, and rather impressively I might add. I then asked it to pick the best ones; it evaded the issue. Rather than trying to prompt it into submission I asked it to tell me which of those things was most like a kumquat. As the title of this post indicates, it choose the MiG, and offered an amusing bit of argumentation on the matter. Is that an example of intelligence creativity? How would GPT-4 respond to those prompts? With even more elaborate justifications? Would it have chosen the MiG as well.

And what about GPT-5? Even more extraneous extravagance? Or will it be clever enough to object, saying:

Those analogies are silly. Don’t waste my valuable time and your electricity. Come back when you have something more intelligent to offer.

That’s how I feel about Hinton’s ideas about creativity and human accomplishment. He may be a genius at coming up with neural network architectures, but his thinking about human creativity and intelligence seems to be the work of an overgrown college sophomore. And he’s not the only one.

You might not want to read the whole transcript. But you should at least look at the analogies I proposed, dip into some of Chatster’s explications, read his typically annoying evasion of my penultimate prompt and then, by all means, read his account of the similarities between a kumquat and a MiG. It’s more fun than a barrel of monkeys.

ChatGPT explicates ten analogies

I am going to present you with a bunch of analogies and I want you to explain them. Some of them may same more plausible than others, but don’t let that bother. Just find analogous features.

Here’s the first one: A kitten and a bicycle.

Certainly! Let's explore the analogy between a kitten and a bicycle:

1. Learning Curve:
• Kitten: Like learning to ride a bicycle, a kitten goes through a learning curve as it develops coordination and balance skills. It starts by stumbling and gradually becomes more adept at moving around.
• Bicycle: When someone first learns to ride a bicycle, there is a learning curve in terms of balance and coordination. Over time, the rider becomes more skilled and comfortable.
2. Independence:
• Kitten: As a kitten grows, it becomes more independent, learning to explore its surroundings and perform activities on its own.
• Bicycle: Riding a bicycle grants a sense of independence, allowing the rider to travel to different places under their control.
3. Care and Maintenance:
• Kitten: Similar to a bicycle requiring maintenance, a kitten needs care, including feeding, grooming, and veterinary attention.
• Bicycle: Regular maintenance is essential for a bicycle to function smoothly. This involves checking and adjusting various components, such as the tires, brakes, and gears.
4. Playfulness:
• Kitten: Kittens are known for their playful nature, exploring their environment and engaging in playful activities.
• Bicycle: Riding a bicycle can be a form of recreation and play, especially for leisure rides or sports.
5. Adaptability:
• Kitten: Kittens can adapt to various environments and situations, showcasing their flexibility.
• Bicycle: Bicycles are versatile and can be adapted for different terrains or purposes, such as road cycling, mountain biking, or commuting.

While a kitten and a bicycle may seem quite different at first glance, there are interesting similarities when considering aspects like learning, independence, care, playfulness, and adaptability.

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. 

* * * * *

On possible cross-fertilization between AI and neuroscience [Creativity]

MIT Center for Minds, Brains, and Machines (CBMM), a panel discussion: CBMM10 - A Symposium on Intelligence: Brains, Minds, and Machines.

On which critical problems should Neuroscience, Cognitive Science, and Computer Science focus now? Do we need to understand fundamental principles of learning -- in the sense of theoretical understanding like in physics -- and apply this understanding to real natural and artificial systems? Similar questions concern neuroscience and human intelligence from the society, industry and science point of view.

Panel Chair: T. Poggio
Panelists: D. Hassabis, G. Hinton, P. Perona, D. Siegel, I. Sutskever

Quick Comments

1.) I’m a bit annoyed that Hassabis is giving neuroscience credit for the idea of episodic memory. As far as I know, the term was coined by a cognitive psychologist named Endel Tulving in the early 1970s, who stood it in opposition to semantic memory. That distinction was all over the place in the cognitive sciences in the 1970s and its second nature to me. When ChatGPT places a number of events in order to make a story, that’s episodic memory.

2.) Rather than theory, I like to think of what I call speculative engineering. I coined the phrase in the preface to my book about music (Beethoven’s Anvil), where I said:

Engineering is about design and construction: How does the nervous system design and construct music? It is speculative because it must be. The purpose of speculation is to clarify thought. If the speculation itself is clear and well-founded, it will achieve its end even when it is wrong, and many of my speculations must surely be wrong. If I then ask you to consider them, not knowing how to separate the prescient speculations from the mistaken ones, it is because I am confident that we have the means to sort these matters out empirically. My aim is to produce ideas interesting, significant, and clear enough to justify the hard work of investigation, both through empirical studies and through computer simulation.

3.) On Chomsky (Hinton & Hassabis): Yes, Chomsky is fundamentally wrong about language. Language is primarily a tool for conveying meaning from one person to another and only derivatively a tool for thinking. And he’s wrong that LLMs can learn any language and therefore they are useless for the scientific study of language. Another problem with Chomsky’s thinking is that he has no interest in process, which is in the realm of performance, not competence.

Let us assume for the sake of argument that the introduction of a single token into the output stream requires one primitive operation of the virtual system being emulated by an LLM. By that I mean that there is no logical operation within the process, no AND or OR, no shift of control; all that’s happening is one gigantic calculation involving all the parameters in the system. That means that the number of primitive operations required to produce a given output is equal to the number of tokens in that output. I suggest that that places severe constraints on the organization of the LLM’s associative memory.

Contrast that with what happens in a classical symbolic system. Let us posit that each time a word (not quite the same as a token in an LLM, but the difference is of no consequence) is emitted, that itself requires a single primitive operation in the classical system. Beyond that, however, a classical system has to execute numerous symbolic operations in order to arrive at each word. Regardless of just how those operations resolve into primitive symbolic operations, the number has to be larger, perhaps considerably larger, than the number of primitive operations an LLM requires. I suggest that this process places fewer constraints on the organization of a symbolic memory system.

At this point I’ve reached 45:11 in the video, but I have to stop and think. Perhaps I’ll offer some more comments later.

LATER: Creativity

4.) Near the end (01:20:00 or so) the question of creativity comes up. Hassibis says AIs aren't there yet. Hinton brings up analogy, pointing out that, with all the vast knowledge LLMs have ingested, they're got opportunities for coming up with analogy after analogy after analogy. I've got experience with ChatGPT that's directly relevant to those issues, analogy and creativity.

One of the first things I did once I started playing with ChatGPT was have it undertake a Girardian interpretation of Steven Spielberg's Jaws. To do that it has to determine whether or not there is an analogy between events in the film and the phenomena that Girard theorizes about. It did that fairly well. So I wrote that up and published it in 3 Quarks Daily, Conversing with ChatGPT about Jaws, Mimetic Desire, and Sacrifice. Near the end I remarked:

I was impressed with ChatGPT’s capabilities. Interacting with it was fun, so much fun that at times I was giggling and laughing out loud. But whether or not this is a harbinger of the much-touted Artificial General Intelligence (AGI), much less a warning of impending doom at the hands of an All-Knowing, All-Powerful Superintelligence – are you kidding? Nothing like that, nothing at all. A useful assistant for a variety of tasks, I can see that, and relatively soon. Maybe even a bit more than an assistant. But that’s as far as I can see.

We can compare what ChatGPT did in response to my prompting with what I did unprompted, freely and of my own volition. There’s nothing its replies that approaches my article, Shark City Sacrifice, nor the various blog posts I wrote about the film. That’s important. I was neither expecting, much less hoping, that ChatGPT would act like a full-on AGI. No, I have something else in mind.

What’s got my attention is what I had to do to write the article. In the first place I had to watch the film and make sense of it. As I’ve already indicated, have no artificial system with the required capabilities, visual, auditory, and cognitive. I watched the film several times in order to be sure of the details. I also consulted scripts I found on the internet. I also watched Jaws 2 more than once. Why did I do that? There’s curiosity and general principle. But there’s also the fact that the Wikipedia article for Jaws asserted that none of the three sequels were as good as the original. I had to watch the others to see for myself – though I was unable to finish watching either of that last two.

At this point I was on the prowl, though I hadn’t yet decided to write anything.

I now asked myself why the original was so much better than the first sequel, which was at least watchable. I came up with two things: 1) the original film was well-organized and tight while the sequel sprawled, and 2) Quint, there was no character in the sequel comparable to Quint.

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.

Whatever.

Once Girard was on my mind I smelled blood, that is, the possibility of writing an interesting article. I started reading, 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. I was only after I’d made some preliminary posts, drafted some text, and run it by David that I decided to go for it. The article turned out well enough that I decided to publish it. And so I did.

It’s one thing to figure out whether or not such and such a text/film exhibits such and such pattern when you are given the text and the pattern. That’s what ChatGPT did. Since I had already made the connection between Girard and Jaws it didn’t have to do that. I was just prompting ChatGPT to verify the connection, which it did (albeit in a weak way). That’s the kind of task we set for high school students and lower division college students. […]

I don’t really think that ChatGPT is operating at a high school level in this context. Nor do I NOT think that. I don’t know quite what to think. And I’m happy with that.

The deeper point is that there is a world of difference between what ChatGPT was doing when I piloted it into Jaws and Girard and what I eventually did when I watched Jaws and decided to look around to see what I could see. How is it that, in that process, Girard came to me? I wasn’t looking for Girard. I wasn’t looking for anything in particular. How do we teach a computer to look around for nothing in particular and come up with something interesting?

These observations are informal and are only about a single example. Given those limitations it's difficult to imagine a generalization. But I didn't hear anything from those experts that was comparably rich.

Hinton gave an example of an analogy that he posed to GPT-4 (01:18:30): “What has a compost heap got in common with an atom bomb?” It got the answer he was looking for, chain reaction, albeit at different energy levels and different rates. That's interesting. Why wasn't the panel ready with 20 such examples among them? Perhaps more to the point, doesn't Hinton see that it is one thing for GPT-4 to explain an analogy he presents to it, but that coming up with the analogy in the first place is a different kind of mental process?

Do they not have more such examples from their own work? Don't they think about their own work process, all the starts and stops, the wandering around, the dead ends and false starts, the open-ended exploration, that came before final success. And even then, no success is final, but only provisional pending further investigation. Can they not see the difference between what they do and what their machines do? Do they think all the need for exploration will just vanish in the face of machine superintelligence. Do they really believe that the universe is that small?

STILL LATER: Hinton and Hassabis on analogies

Hinton continues with analogies and Hassabis weights in:

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 ...

1:20:18 – 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.

Well, yeah, sure, GPT-4 has all this stuff in its model, way more topics than any one human. But where’s GPT-4 going to “stand” so it can “look over” all that stuff and spot the analogies? That requires some kind of procedure. What is it?

For example, it might partition all that knowledge into discrete bits and then set up a 2D matrix with a column and a row for each discrete chunk of knowledge. Then it can move systematically through the matrix, checking each cell to see whether or not the pair in that cell is a useful analogy. What kind of tests does it apply to make that determination? I can imagine there might be a test or tests that allows a quick and dirty rejection for many candidates. But those that remain, what can you do but see if any useful knowledge follows from trying out the analogy. How long will that determination take? And so forth.

That’s absurd on the face of it. What else is there? I just explained what I went through to come up with an analogy between Jaws and Girard. But that’s just my behavior, not the mental process that’s behind the behavior. I have no trouble imagining that, in principle, having these machines will help speed up the process, but in the end I think we’re going to end up with a community of human investigators communicating with one another while they make sense of the world. The idea, which, judging from remarks he’s made elsewhere, Hinton seems to hold, that one of these days we’ll have a machine that takes humans out of the process all together, that’s an idle fantasy.

Mapping ChatGPT’s ontological landscape, gradients and choices [interpretability]

By ontological landscape I mean the repertoire of different things it knows about. The parlor game, Twenty questions, was invented in the United States in the 19th century as a way of playing around on this landscape. One player thinks of some phenomenon and the other has to guess it by asking a series of no more than 20 questions that have a “yes” or “no” answer. One popular variant of the game begins with a simple question: “Animal, vegetable, or mineral?” Once the question has been answered the questioner must continue with binary questions.

In this post I take a look at how ChatGPT deals with this structure. I start simply, with concrete and abstract object, and move on from there.

* * * * *

All of the interactions reported below took place on the morning of October 15 using the September 25 version of ChatGPT.

Physical things

I decided to start with the simplest prompt I could imagine, “thing.” That’s the most general category we’ve got, no? Anything whatever is going to be some kind of thing. But how would ChatGPT respond? It responded as I thought it would, so I gave it a more explicit prompt, to which it responded.

Thing

It seems like you mentioned "thing," but you didn't provide a specific question or context. Could you please clarify or provide more information so I can assist you better?

Give me a list of 20 things, anything.

Certainly! Here's a list of 20 random things:

1. A beach ball
2. A rainbow
3. A bicycle
4. A bookshelf
5. A coffee mug
6. A mountain
7. A laptop computer
8. A painting
9. A pineapple
10. A pair of sunglasses
11. A telescope
12. A running shoe
13. A wristwatch
14. A musical instrument
15. A flower vase
16. A hammock
17. A traffic light
18. A hot air balloon
19. A chef's knife
20. A Rubik's Cube

These are just a few examples of things, and the list could go on endlessly!

I had no particular expectations about how ChatGPT would respond. But I couldn’t help but notice that each item in its response was some kind of physical thing. To be sure, rainbows (the second item in the list) are rather diaphonous, and you couldn’t actually touch one (I should have asked ChatGPT about that; later...), but it is still a physical thing.

I didn’t ask for physical things, but that’s what ChatGPT gave me. That’s worth noting.

Let’s think a bit before continuing on. I wonder if that first list is “rigid” in the sense that ChatGPT would respond the same way given the same prompt. I suspect not, but it’s easy enough to find out. I went back a bit later.

Give me a list of 20 things, anything.

Certainly! Here's a list of 20 random things:

1. Beach vacations
2. Chocolate chip cookies
3. Classic literature
4. Sunsets
5. Roller coasters
6. Coffee shops
7. Mountain hiking
8. Vintage cars
9. Starry nights
10. Watermelon
11. Jazz music
12. Sunflowers
13. Science fiction movies
14. Art galleries
15. Hot air balloons
16. Snow-capped mountains
17. Aquariums
18. Street food
19. Historical landmarks
20. Puzzle games

It’s not the same list I got the first time, but almost all of them are physical things. Classic literature (#3) isn’t really a physical thing, though books are. Mountain hiking (#7) is a physical activity, but whether or not you’d call it a physical thing is, well, an interesting issue. Jazz music (#7)? Science fiction movies (#13), puzzle games (#20)?

I decided to log out and then try again:

Give me a list of 20 things, anything.

Certainly! Here's a list of 20 random things:

1. Pencil
2. Bicycle
3. Sunflower
4. Smartphone
5. Coffee mug
6. Guitar
7. Rainbow
8. Lighthouse
9. Chocolate chip cookies
10. Telescope
11. Mountain
12. Soccer ball
13. Vintage typewriter
14. Starfish
15. Hot air balloon
16. Eiffel Tower
17. Polar bear
18. Sandcastle
19. Wristwatch
20. Lightning bolt

That’s better. I notice we’ve got “rainbow” again (#7), and “Lighhtening bolt” (#20) is rather ephemeral as well, though if one were to hit you, the effect would be dramatic and quite physical. “Chocolate chip cookies” (#9) show up again, as does “Hot air balloon” and “Bicycle.” These lists seem to be drawn from roughly the same conceptual territory. I suppose that’s not terribly surprising, but it certainly doesn’t seem like ChatGPT is making random choices from among all available physical things. Why these things?

I decided to explicitly specify physical objects:

How about 20 physical objects.

Of course, here's a list of 20 physical objects:

1. Refrigerator 2. Sofa
3. Running shoes
4. Toaster
5. Sunglasses
6. Umbrella
7. Bookshelf
8. Camera
9. Toothbrush
10. Bicycle helmet
11. Microwave oven
12. Flower vase
13. Tennis racket
14. Water bottle
15. Alarm clock
16. Car keys
17. Backpack
18. Desk chair
19. Blender
20. Laptop computer

None of those items are problematic in the way rainbows and lightening bolts are. You can touch each of them. In fact, except perhaps for the refrigerator, you can pick each item up and carry it around. No polar bears, mountains, lighthouses, roller coasters or traffic lights. But there are no purely abstract things here either, things like love, freedom, justice, beauty, etc. They go on a different list.

What’s ChatGPT doing?

Yes, we know, it’s cranking out one word after another. But that’s not very informative, as I argued some time ago, The idea that ChatGPT is simply “predicting” the next word is, at best, misleading. Remember, each time ChatGPT generates a new token it does a calculation that involves all 175 billion weights (Wolfram). Its entire “neural” space is involved in every such calculation.