Terminology: Generative Machines, Epistemic Structure of the Cosmos, Intelligence-Complete

I’ve been spending a lot of time with my chatbots, ChatGPT and Claude, and some terminological issues have come. Noting particularly deep, just clarification.

Generative machines vs. equilibrium machines

While we talk of computers as machines, it’s obvious that they’re very different beasts. Electric drills, helicopters, sewing machines, hydraulic presses, they’re all (proper) machines. Interaction with and manipulaton of matter is central to their purpose. Computers, well, technically, yes, they push electrons around in intricate paths, and electrons are matter, subatomic particles, very small chunks of matter, the smallest possible chunks. What computers are really about, though, is manipulate bits, units of information. And they use “trillions of parts” (a phrase I have from Daniel Dennett) to do so. Thus computers, with their trillions of parts, are very different from machines, with only 10s, 100s, or 1000s of parts.

So, what names should we give to differentiate them. “Type 1” and “Type 2” machines would do the job, but it’s not very descriptive. ChatGPT and I settled on “equilibrium machines” for those machines centered on interaction with matter while “generative machines” seemed appropriate to bit-wranglers. “Generative” seems just right for computers, with its echoes on Chomsky’s generative grammar the generative pre-trained transformer (GPT) of machine learning. “Equilibrium machines” is perhaps a bit oblique for the other kind of machine, but it’s meant to evoke the equilibrium world of macroscopic devices as opposed to the far-from-equilibrium world of, well, generative machines.

Epistemic Structure of the Cosmos

Back in 2020 I wrote of the metaphysical structure of the cosmos. I said:

There is no a priori reason to believe that world has to be learnable. But if it were not, then we wouldn’t exist, nor would (most?) animals. The existing world, thus, is learnable. The human sensorium and motor system are necessarily adapted to that learnable structure, whatever it is.

I am, at least provisionally, calling that learnable structure the metaphysical structure of the world.

I’ve always been uneasy with “metaphysical” in that role. ChatGPT suggested that “epistemic” would serve better. The epistemic structure of the cosmos, I like that. As for “cosmos,” the dictionary tells me that the word implies order, which I like as well. 

I leave it as an exercise to the reader to demonstrate that the epistemic structure of the cosmos must necessarily be recursive. 

Intelligence-Complete

A generative machine is intelligence-complete if it possesses the full capacities of human intelligence, whatever human intelligence is. By that definition LLS are not intelligence complete. As for human intelligence, I like the account given in What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet.

Notes on the Metaphysical Structure of the Cosmos

I’ve been thinking about something I all “the metaphysical structure of the cosmos” now and then since August of 2020 when I introduced it in a post written in the wake of GPT-3. I wasn’t entirely serious about it. I’d only just then thought of the idea and hadn’t had time to think it through. It came back to me a few days ago when I was thinking about the “Xanadu meme” and other ideas. This time, in a conversation with Claude, I hazarded the idea that the metaphysical structure of the cosmos was recursive, though I didn’t use the word “cosmos.” Claude agreed.

It's about time I thought about the idea seriously. Is it one I want to use, in a technical sense, going forward? I don’t know. But I’ll offer some thoughts on the matter.

Just what does it mean, “metaphysical structure of the cosmos”?

Here’s what I said when I originally introduced the idea:

There is no a priori reason to believe that world has to be learnable. But if it were not, then we wouldn’t exist, nor would (most?) animals. The existing world, thus, is learnable. The human sensorium and motor system are necessarily adapted to that learnable structure, whatever it is.

I am, at least provisionally, calling that learnable structure the metaphysical structure of the world. Moreover, since humans did not arise de novo that metaphysical structure must necessarily extend through the animal kingdom and, who knows, plants as well.

“How”, you might ask, “does this metaphysical structure of the world differ from the world’s physical structure?” I will say, again provisionally, for I am just now making this up, that it is a matter of intension rather than extension. Extensionally the physical and the metaphysical are one and the same. But intensionally, they are different. We think about them in different terms. We ask different things of them. They have different conceptual affordances. The physical world is meaningless; it is simply there. It is in the metaphysical world that we seek meaning.

As I’ve already said, I introduced the idea in the wake of GPT-3, the first large language model (LLM) that had received much public exposure. Though only small number of people had direct access, enough of those wrote about it in fairly public ways that many of us knew about it, knew enough to be impressed.

When I introduced the idea I used a diagram something like this:

We have the LLM running down the middle, either that or the text on which it is trained. At this level of analysis it could be either one. The structure of the individual texts is a function of the human mind, which created the text, and the world, which the text is about, albeit often only indirectly (as in works of fiction). From this it follows, almost by definition, that the LLM derived from those texts reflects those two things as well, the mind and the world.

The significance of GPT-3, that is, its underlying LLM, and of subsequent LLMs is that that is the first time we’ve got the “whole thing” gathered together in a single, a single what? Model, text, whatever? It’s all there.

Yeah, I know. Not of it. All LLMs are biased in favor of the texts on which they’re built. Much of human thought, especially the thought of pre-literate peoples, is not represented in the training corpus of any LLM. So, we’re talking about an idealization. That’s OK. As long as we’re aware of what we’re doing, we can proceed.

Now, there’s lots of structure in any given text, and there’s lots of structure latent in any LLM. I’m not interested in all of that structure. I’m only interested in the ontological structure, by which I mean something close to the concept of ontology as it is ordinarily used in knowledge representation.

John Sowa’s use is typical. Here’s how he introduces the topic: “The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D.” I’m interested in structure of that catalog. I hypothesize that that structure is something which, for convenience, I call the Great Chain (a term long in use). Here’s a diagram:

That diagram needs some explaining; but this is not the time or place to do that. I say more in this old unpublished paper: Ontology in Knowledge Representation. My point is simply that there is a specific structure there. It’s that structure that interests me.

As an example, that structure tells us the difference between salt and sodium chloride (NaCl). Physically they are the same substance, but conceptually they are quite different. We recognize salt by its texture and appearance and, above all, by its taste. We can taste the presence of salt even where we cannot see it existing as a discrete substance. That is to say, conceptually, salt is adequately characterized by its sensorimotor properties. Sodium chloride is not. Sodium chloride is characterized in terms of a chemical theory that did not exist until the 19th century. That theory talks of atoms and bonds between them. We can’t see atoms or their bonds, rather we infer them on basis of a wide body of experimentation. Conceptually, then, they are very different.

Similarly, in one account of the world, based on one ontology, the Morning Star and the Evening Star are two different objects. But in account based on a heliocentric model of the solar system, they turn out to be the same object, the planet Venus. And so it is with the difference between animals and human beings. To the biologist they are the same kind of thing; human beings are just one kind, one species of animal. But in the common-sense construal of the world, they are very different; humans are not animals, though we have animal-like characteristics.

That, more or less, is what I’m talking about when I talk of the metaphysical structure of the cosmos (or world). That conceptual structure. It’s not explicit in any LLM, but it certainly exists implicitly, otherwise LLMs wouldn’t generate coherent texts. (Note that I have a working paper on ChatGPT and stories where it betrays ontological sensitivity: ChatGPT tells stories, and a note about reverse engineering.)

Intellectual creativity, humans-in-the-loop, and AI: Interlude, Claude on “xanadu” and the metaphysical structure of the world

After yesterday’s post about the Xanadu meme went up I started wondering just how an AI, a current AI, would even confront such a question. Why would an AI issue a simple one-word query to the web: “Xanadu”? And when it got 2,000,000 hits, what reason would it have to think that a large number? I decided to have a discussion with Claude Sonnet 4 about “xanadu.” That eventually led to an interesting discussion that ended with something I’ve been calling “the metaphysical structure of the world.” I introduced the term in a blog post from August of 2020, which I’d written in the wake of GPT-3 and then included it in my working paper, GPT-3: Waterloo or Rubicon? Here be Dragons. I’m beginning to think it might be useful. 

On the way there ChatGPT made the following observation

Current AI systems are remarkable at following up on investigations once they’re pointed in a direction. But that initial moment of recognition - “this might be interesting” - seems to require a different kind of agency. It’s not just pattern recognition; it’s pattern recognition combined with intrinsic motivation and aesthetic judgment about what’s worth pursuing.

Yes.

As always in these discussions, my prompts are in boldface.

* * * * *

“Xanadu”

What comes to mind when I say “Xanadu”?

Several things come to mind with “Xanadu”:

Literary: Most famously, it’s from Coleridge’s poem “Kubla Khan” - “In Xanadu did Kubla Khan / A stately pleasure-dome decree” - describing an exotic, dreamlike palace.

Historical: The actual Xanadu (Shangdu) was Kublai Khan’s summer capital in 13th-century Mongolia, which inspired Coleridge’s poem.

Pop culture: The 1980 musical film with Olivia Newton-John and Gene Kelly, along with its title song. There’s also Rush’s epic song “Xanadu” from their album A Farewell to Kings.

Cinema: In Citizen Kane, it’s the name of Charles Foster Kane’s sprawling, isolating estate.

The word has become synonymous with an idealized, fantastical place - somewhere luxurious but perhaps unattainable or illusory. It carries this sense of exotic grandeur mixed with a hint that such perfection might be more dream than reality.

What made you think of Xanadu? Were you thinking of one of these references in particular?

I was just curious. Back in January of 2006 I typed “Xanadu” into Google and got 2,000,000. That surprised me. “Xanadu” is not a common term. People don’t have much of an opportunity to use it. Yet there were 2 million web pages that mention it.

That’s a fascinating observation! You’ve hit on something really interesting about how language spreads in digital spaces versus everyday conversation.

“Xanadu” is indeed quite rare in daily speech - most people might go years without using it. But on the web, it has this remarkable reach because it’s such an evocative, culturally loaded word. Each of those cultural touchstones I mentioned creates its own cluster of web content: literary analysis sites discussing Coleridge, movie review sites covering the film, music sites with Rush lyrics, film studies pages about Citizen Kane.

Plus, “Xanadu” is exactly the kind of word that gets borrowed for business names, product names, gaming references, fantasy literature, and artistic projects. It carries this instant sense of mystery and grandeur that appeals to people creating content.

Your 2006 observation captures something about how the internet amplifies certain words that have cultural resonance far beyond their frequency in speech. Words that are “mythologically useful” - like Xanadu, Atlantis, Eden - probably punch way above their weight in search results because they’re so semantically rich.

It’s a perfect example of how digital archaeology can reveal hidden patterns in our collective cultural consciousness. Did you notice similar patterns with other evocative but uncommon words when you were exploring search results back then?

It didn’t take long to figure out that most of the hits did not link to pages mentioning Coleridge’s poem. After a bit I determined that a lot of the hits belonged in what I termed the “sybaritic” cluster, which I trace back to Citizen Kane. Lots of people saw that movie and it has a fairly substantial cultural presence. The movie itself quotes the first five lines of the poem in the newsreel sequence and, as you mentioned, there’s Kane’s mansion.

That’s a brilliant piece of cultural archaeology! Your “sybaritic cluster” concept really captures how Citizen Kane became the dominant cultural vector for “Xanadu” rather than the original Coleridge poem.

It makes perfect sense - while English literature students encounter the poem, Citizen Kane reached massive audiences and embedded “Xanadu” firmly in the American cultural lexicon as shorthand for extravagant, perhaps excessive luxury. The film’s Xanadu represents wealth turned grotesque and isolating, but the name itself got detached from that cautionary context.

So you probably found tons of luxury hotels, upscale restaurants, spa resorts, real estate developments, and high-end businesses that borrowed the name purely for its connotations of opulence. They’re not thinking about Mongolian summer palaces or Romantic poetry - they’re thinking about that sense of ultimate luxury that Kane’s estate represented.

This is such a neat example of how cultural transmission works. The literary source provides the raw material, but a hugely influential film recontextualizes it and creates the dominant semantic pathway. Most people using “Xanadu” for their luxury business probably have no idea they’re referencing Coleridge - they’re unconsciously channeling Welles.

Did you find that pattern held up as you dug deeper? Were there other cultural artifacts where you could trace similar semantic drift from high culture sources through popular media?

Ted Nelson’s Project Xanadu

I also identified a cluster I called the “cybernetic” cluster. For example, there’s a spot on Saturn’s moon, Titan, that I put in that cluster. Where do you think this cybernetic cluster comes from?

Ah, that has to trace back to Ted Nelson’s “Project Xanadu” - his pioneering hypertext system from the 1960s! Nelson chose the name explicitly referencing Coleridge’s poem, envisioning his system as a kind of “magic place of literary memory” where all human knowledge could be interconnected.

What economic growth and statistical semantics tell us about the structure of the world

Bumping this to the top of the queue on general principle, and because it takes a very abstract view of economic development, which is front and center in Tyler Cowen's current conversation with Stephen Jennings, who is a developer working Kenya.

New working paper. Title above. Download at:

• Academia: https://www.academia.edu/43938531/What_economic_growth_and_statistical_semantics_tell_us_about_the_structure_of_the_world
• SSRN: http://ssrn.com/abstract=3680119
• ResearchGate: https://www.researchgate.net/publication/343836653_What_economic_growth_and_statistical_semantics_tell_us_about_the_structure_of_the_world

Abstract, contents, and first section below.

---

Abstract: The metaphysical structure of the world, as opposed to its physical structure, resides in the relationship between our cognitive capacities and the world itself. Because the world itself is “lumpy”, rather than “smooth” (as developed herein, but akin to “simple” vs. complex”), it is learnable and hence livable. Machine learning AI engines, such as GPT-3, are able to approximate the semantic structure of language, to the extent that that structure can be modeled in a high-dimensional space. That structure ultimately depends on the fact that the world is lumpy. It is the lumpiness that is captured in the statistics. Similarly, I argue, the American economy has entered a period of stagnation because the world is lumpy. In such a world good “ideas” become more and more difficult to find. Stagnation then reflects the increasing costs the learning required to develop economically useful ideas.

Contents

Wending our way in a complex world 2
World, mind, and learnability: On the metaphysical structure of the cosmos 5
Stagnation, Redux: Like diamonds, good ideas are not evenly distributed 10
The complex universe: Further reading 18

Wending our way in a complex world

This paper is based on two very different posts that I’ve written in the last month. One of them takes the statistical semantics of AI engines like GPT-3 as its starting point: “World, mind, and learnability: On the metaphysical structure of the cosmos” (revised considerably for this paper). The other is about economic growth and stagnation: “Stagnation, Redux: Like diamonds, good ideas are not evenly distributed”.

These two very different papers nonetheless share both substance and method. Methodologically, both argue that the situation we observe is intelligible if we assume that the world is structured in a certain way. Their core substance is about that structure: the world must be “lumpy” – a notion I discuss on pages 5 ff. Because the world is lumpy we can learn about it, live in it, talk about it, and write about it. By contrast, a “smooth” world would be unintelligible and hence unlivable. The exhaustive statistical analysis of a large body of text is, in effect, able to recover that structural lumpiness as reflected in language and use it to produce new texts.

However, because the world is lumpy, we begin by learning and benefiting from things close to hand. When those resources have been exhausted we and must travel deeper into the world, expending more and more effort to extract economic benefit. Our current economic stagnation reflects the increasing cost of learning more about the world. A smooth world would no doubt be more convenient, for there would be economic benefit at every turn, either that or economic disaster. That is, if the world were smooth, we wouldn’t be here.

That, I know, this talk of smoothness and lumpiness is very abstract and “featureless”. But then could a resonance between such disparate phenomena as statistical semantics and economic stagnation but be abstract? I’ll provide more substance later in this paper, some diagrams, and some arguments. But first I want to suggest that what I’ve been calling lumpiness is what Ilya Prigogine and many others have called complexity.

Our complex world

Some years ago David Hays and I wondered why natural selection leads to complexity [1]. We argued that, over the long run, natural selection favors organisms with increased ability to process information, and that ability yields benefits in a complex universe. But what did we mean by that, a complex universe? Here is what we said:

It is easy enough to assert that the universe is essentially complex, but what does that assertion mean? Biology is certainly accustomed to complexity. Biomolecules consist of many atoms arranged in complex configurations; organisms consist of complex arrangements of cells and tissues; ecosystems have complex pathways of dependency between organisms. These things, and more, are the complexity with which biology must deal. And yet such general examples have the wrong “feel;” they don't focus one's attention on what is essential. To use a metaphor, the complexity we have in mind is a complexity in the very fabric of the universe. That garments of complex design can be made of that fabric is interesting, but one can also make complex garments from simple fabrics. It is complexity in the fabric which we find essential.

We take as our touchstone the work of Ilya Prigogine, who won the Nobel prize for demonstrating that order can arise by accident (Prigogine and Stengers 1984; Prigogine 1980; Nicolis and Prigogine 1977). He showed that when certain kinds of thermodynamic systems get far from equilibrium order can arise spontaneously. These systems include, but are not limited to, living systems. In general, so-called dissipative systems are such that small fluctuations can be amplified to the point where they change the behavior of the system. These systems have very large numbers of parts and the spontaneous order they exhibit arises on the macroscopic temporal and spatial scales of the whole system rather than on the microscopic temporal and spatial scales of its very many component parts. Further, since these processes are irreversible, it follows that time is not simply an empty vessel in which things just happen. The passage of time, rather, is intrinsic to physical process.

We live in a world in which “evolutionary processes leading to diversification and increasing complexity” are intrinsic to the inanimate as well as the animate world (Nicolis and Prigogine 1977: 1; see also Prigogine and Stengers 1984: 297-298). That this complexity is a complexity inherent in the fabric of the universe is indicated in a passage where Prigogine (1980: xv) asserts “that living systems are far-from-equilibrium objects separated by instabilities from the world of equilibrium and that living organisms are necessarily ‘large,’ macroscopic objects requiring a coherent state of matter in order to produce the complex biomolecules that make the perpetuation of life possible.” Here Prigogine asserts that organisms are macroscopic objects, implicitly contrasting them with microscopic objects.

World, mind, and learnability: A note on the metaphysical structure of the cosmos [& LLMs]

I originally posted this three years ago, on August 15.  I have added an important new section at the end, Paths of the mind (virtual reading), and so I am bumping this to the top of the queue.

There is no a priori reason to believe that world has to be learnable. But if it were not, then we wouldn’t exist, nor would (most?) animals. The existing world, thus, is learnable. The human sensorium and motor system are necessarily adapted to that learnable structure, whatever it is.

I am, at least provisionally, calling that learnable structure the metaphysical structure of the world. Moreover, since humans did not arise de novo that metaphysical structure must necessarily extend through the animal kingdom and, who knows, plants as well.

“How”, you might ask, “does this metaphysical structure of the world differ from the world’s physical structure?” I will say, again provisionally, for I am just now making this up, that it is a matter of intension rather than extension. Extensionally the physical and the metaphysical are one and the same. But intensionally, they are different. We think about them in different terms. We ask different things of them. They have different conceptual affordances. The physical world is meaningless; it is simply there. It is in the metaphysical world that we seek meaning. [See my post, There is a fold in the fabric of reality. (Traditional) literary criticism is written on one side of it. I went around the bend years ago.]

A little dialog

Does this make sense, philosophically? How would I know?

I get it, you’re just making this up.

Right.

Hmmmm… How does this relate to that object-oriented ontology stuff you were so interested in a couple of years ago?

Interesting question. Why don’t you think about it and get back to me.

I mean, that metaphysical structure you’re talking about, it seems almost like a complex multidimensional tissue binding the world together. It has a whiff of a Latourian actor-network about it.

Hmmm… Set that aside for awhile. I want to go somewhere else.

Still on GPT-3, eh?

You got it.[1]

 

A little diagram: World, Text, and Mind

Text reflects this learnable, this metaphysical, structure, albeit at some remove:

Learning engines are learning the structure inherent in the text. But that learnable structure is not explicit in the language model created by the learning engine.

There are two things in play: 1) the fact that the text is learnable, and 2) that it is learnable by a statistical process. How are these two related?

If we already had an explicit ‘old school’ propositional model in computable form, then we wouldn’t need statistical learning at all. We could just run the propositional model over the corpus and encode the result. But why do even that? If we can read the corpus with the propositional model, in a simulation of human reading, then there’s no need to encode it at all. Just read whatever aspect of the corpus is needed at the time.

So, statistical learning is a substitute for the lack of a usable propositional model. The statistical model does work, but at the expense of explicitness.

But why does the statistical model work at all? That’s the question.

It’s not enough to say, because the world itself is learnable. That’s true for the propositional model as well. Both work because the world is learnable.

Language model as associative memory

BUT: Humans don’t learn the world with a statistical model. We learn it through a propositional engine floating over an analogue or quasi-analogue engine with statistical properties. And it is the propositional engine that allows us to produce language. A corpus is a product of the action of propositional engine, not a statistical model, acting on the world.

Description is one basic such action; narration is another. Analysis and explanation are perhaps more sophisticated and depend on (logically) prior description and narration. Note that this process of rendering into language is inherently and necessarily a temporal one. The order in which signifiers are placed into the speech stream depends in some way, not necessarily obvious, on the relations among the correlative signifieds in semantic or cognitive space. Distances between signifiers in the speech stream reflect distances between correlative signifieds in semantic space. We thus have systematic relationships between positions and distances of signifiers in the speech stream, on the one hand, and positions and distances of signifieds in semantic space. It is those systematic relationships that allow statistical analysis of the speech stream to reconstruct semantic space.

Note that time is not extrinsic to this process. Time is intrinsic and constitutive of computation. Speaking involves computation, as does the statistical analysis of the speech stream.

The propositional engine learns the world via Gärdenfors’ dimensions [2], and whatever else, Powers’ stack for example [3]. Those dimensions are implicit in the resulting propositional model and so become projected onto the speech stream via syntax, pragmatics, and discourse structure. The language engine is then able to extract (a simulacrum of) those dimensions through statistical learning. Those dimensions are expressed in the parameter weights of the model. THAT’s what makes the knowledge so ‘frozen’. One has to cue it with actual speech.

The whole language model thus functions as associative memory [4]. You present it with an input cue, and it then associates from that cue with each emitted string ‘feeding back’ into the memory bank via associative memory.

 

Paths of the mind (virtual reading)

 

Now, imagine a word embedding model constructed over some suitable corpus of texts. Given that texts reflect the interaction of the mind and the world, the location of individual words in that model necessarily reflects that interaction. That structure is what I have been calling the metaphysical structure of the cosmos.

Consider some text. It consists of word after word after word. That sequence reflects the actions of the mind that wrote the text, and only the mind. For all practical purposes, the cosmos remains unchanging during the writing of that text and the mind has withdrawn from active interaction with the world, except insofar as the text is a set of symbols that it is placing on a piece of paper, moment after moment, word by word. Now let us trace the path some texts takes through a word embedding. Call this a virtual reading. The word embedding consists of tens of 1000s of dimensions, so that path will be a complicated one. That path must necessarily be a product of the mind (and only the mind?). That path is the mind in action.

Further, consider that the mind is what the brain does. The brain consists of 86 billion neurons, each having on the order of 10,000 connections with other neurons. The dimensionality of the space required to represent the brain is thus huge. At any given moment we can represent the state of the brain as a point in that space. From one moment to the next, the brain traces a path in that space, what a dynamicist (such as Walter Freeman) would call a trajectory. When someone is writing a text, that text reflects the operations of their brain. Therefore the path of a text through a word embedding necessarily mirrors the trajectory taken by the author's brain while writing the text. Notice, however, the reduction in dimensionality. The brain's state space is of vastly higher dimensionality than the word embedding space.

Finally, consider the operations of a transformer as it generates a text. Each time it generates a token it takes the entire model into account, all those 100s of billions of parameters (are we up to trillions yet?). Compare that to what a brain did when generating that same text. Is it reasonable to consider a single token as an image of, a reflection of, short trajectory segment in the state space of the brain that generated the text. Walter Freeman thought of the brain as moving through states of global coherence at the rate of 7-10 Hz, like frames of a film [5]. In what way is the movement of a transformer from one token to the next comparable to the movement of the brain from one frame to the next?[6]

---

References
 

[1] This post is an exploration of ideas raised in the course of thinking about GPT-3. See William Benzon, GPT-3: Waterloo or Rubicon? Here be Dragons, Working Paper, August 5, 2020, 32 pp., Academia: https://www.academia.edu/s/9c587aeb25; SSRN: https://ssrn.com/abstract=3667608
ResearchGate: https://www.researchgate.net/publication/343444766_GPT-3_Waterloo_or_Rubicon_Here_be_Dragons.

[2] Peter Gärdenfors, Conceptual Spaces: The Geometry of Thought, MIT Press, 2000; The Geometry of Meaning: Semantics Based on Conceptual Spaces, MIT Press, 2014.

[3] William Powers, Behavior: The Control of Perception (Aldine) 1973. A decade later David Hays integrated Powers’ model into his cognitive network model, David G. Hays, Cognitive Structures, HRAF Press, 1981.

[4] The idea that the brain implements associative memory in a holographic fashion was championed by Karl Pribram in the 1970s and 1980s. David Hays and I drew on that work in an article on metaphor, William Benzon and David Hays, Metaphor, Recognition, and Neural Process, The American Journal of Semiotics , Vol. 5, No. 1 (1987), 59-80, https://www.academia.edu/238608/Metaphor_Recognition_and_Neural_Process.

 

[5] Freeman, W. J. (1999a). Consciousness, Intentionality and Causality. Reclaiming Cognition. R. Núñez and W. J. Freeman. Thoverton, Imprint Academic, 143-172.

 

[6] I first argue this point in William Benzon, The idea that ChatGPT is simply “predicting” the next word is, at best, misleading, New Savanna, Feb. 19, 2023.