Framing my discussion of The God Test, Part 1: Rorschach, reason, and whaling – [GT-3]

I’ve got to bite the bullet: I’m just going to have to go through a bunch of (preliminary) stuff before I can really engage with The God Test. My current target is to be in a position to publish a proper review of the book in 3 Quarks Daily for the week of August 9.

Rorschach Recap

I want start by recapping the Rorschach metaphor I introduced in the previous post, More on how I’m approaching The God Test – Rorschach! [GT-2]. What I like about it is that has a shape, there’s something there, but it’s not clear what. So we have little choice but to project onto it in order to (begin to) make sense of it.

First: It is a new kind of thing, an artifact we can converse with in an open-ended and natural way. The steam engine was the same kind of thing. It was an inanimate object that moved over the surface of the earth under its own power. Previously only animals (& humans as animals) had that power. So it becomes an iron horse. Just what are AIs? What’s their nature? That’s one thing.

Second: How it works is opaque. We know how to create large language models (LLMs), but we don’t know how they work. That’s new. We may not have understood the deep physics of the steam engine, but we certainly knew how they worked.

Third: We don’t know what they portend for the future. To some extent this is a function of the first two: How can we, how should we, interact. But it is also a function of the future, which is undetermined. We just don’t know.

Rhetorical force over reason

This is an argument I made in the first working paper I published after the release of ChatGPT in November of 2022: ChatGPT intimates a tantalizing future; its core LLM is organized on multiple levels; and it has broken the idea of thinking (February 6, 2023).

What do I mean by that, has broken the idea of thinking? Prior to ChatGPT it was obvious that humans could think and computers could not. [Yeah, I know, there’s Deep Blue defeating Kasparov in chess. That just changes the dates, not the argument.] The difference in performance was so obvious that the fact that we don’t really know how humans think wasn’t much of an issue. Now it is. Sure, we can still say that we can think and the AI’s can’t, but that’s just a line and without good explanations on both sides of the line, it seems a bit arbitrary, if not desperate.

I made a particular argument about Searles’ (in)famous Chinese Room thought experiment. I read it when it was first published in Brain and Behavioral Science in 1980. I wasn’t impressed. Why not? He didn’t say anything about any of the techniques used in AI or computational linguistics (CL). How could anyone possibly take that seriously?

He talked about intention, that’s how. Meaning requires intention and only living things can have intention, a remark he made at the end of the article. Without intention the most you get is syntax, but no meaning. Searle could get away with that because, in the first place, the concept of intention has a long history within philosophy – it has a subtle meaning, but that can wait for a later post – and so philosophers, his main audience, were comfortable with it. That’s one thing.

But there’s something more important, something that we can see only in retrospect, and that’s the simple fact computers very obviously could not translate from Chinese into English or into any other language. That difference carried tremendous weight. We don’t have a subtle behavioral difference between computers and humans that requires a subtle and sophisticated argument. To a first approximation, almost any argument would do. As far as I was concerned, “intention” was just a fancy word for something we don’t understand. But that’s not an argument anyone needs to take seriously. The behavioral distance is quite sufficient to carry the argument for those who insist that computers can’t and will never be able to think like humans.

Now the behavioral evidence has changed. Sure, differences remain, but the evidence is shifting. The old arguments remain and those who believed them still do so, but it’s getting harder. The need for explicit arguments grounded in explicit accounts of computers, and also brains, is growing.

Whaling and expertise

What’s an expert in machine learning and LLMs actually expert in? For some time now I’ve been arguing that investing in AI is like investing in a whaling venture where the captain and crew of the ship know all there is to know about the ship and how to handle it but know little or nothing about whales and their behavior and about navigating around the Cape Horn and in the South Pacific, where the whales live. What are the chances of that voyage being successful? Not very good.

The people who have created the current AI technology are like that captain and crew. The know how to sail the ship. But they don’t know much about language or cognition. They don’t actually know much about the human mind. Here my point is not about the fact that the models are opaque, but that human language and cognition are highly structured and they don’t believe that one needs to know (much of) anything about that not only to build AI but to make confident prediction about the future of AI.

Gary Marcus, Subbarao Kambhampati, and others have been consistently arguing that, yes, the current technology is remarkable, but we are going to have to adopt classical symbolic techniques if we are to fully develop the technology so that we have accurate and safe systems. Marcus is arguing from his knowledge of human language and cognition. As far as I can tell, Wright doesn’t take that seriously. I know that he had Marcus on his NonZero podcast, and that he lists Marcus in his acknowledgements, but that he doesn’t discuss Marcus’s ideas. I conclude that he doesn’t take that line of argument seriously.

That’s a mistake, but this is not the place to make my own arguments on this issue. My point is simply that expertise in AI is no generally construed to encompass knowledge of, expertise in, human cognition and language. I can’t see how that is going to work out well in the future.

[Note: If you’re curious about my views, on this subject, read the article linked in the first paragraph of this section. My views all over the place here at New Savanna, particularly around the work of the mathematician Miriam Yevick. Also, check out the experimental work I’ve done with LLMs.]

GPT in the Classroom, Part 2: Escape to America

Time to return to the classroom. I want to return to the question of authorship. First, I present the interaction that resulted in a poem, “Escape to America.” Once we’ve been through that we can ask: Who’s the author? 

* * * * *

The poem, “Escape to America”

I want you to create a sonnet based on three short texts by Miriam Yevick. Use the first text for the first quatrain, the second text for the second quatrain, and the third text for the third quatrain. Create the final couplet as you will. Call the sonnet: Escape to America. Here are the three texts:

The tanks exuded power. The soldiers cast their vigor into the rows of women. The women’s hot breath floated back to them on the cheers. Girls showered flowers and kisses into the warrior’s path. This was the scent of war! The orderly formations loosened, females leaped from the sidewalk and ran into the mêlée. Soldiers broke rank and grabbed at random. The caravan driven on by the motorized units undulated forward amidst frenzied embraces.

Luckily the trains to Paris were still running normally. We were greatly surprised to encounter a city still believing in the protection offered by the Maginot Line (circumvented by the Nazis the previous week) and oblivious to what had happened and was happening some hundred miles to the North.

The next day we took a walk on Broadway. I saw a striking dress in a shop window: sky blue with bold purple stripes descending diagonally from both shoulders to meet along the centerline of the body. I went into the store and bought the dress, which accentuated my youthful shape, for $ 1. Ready-made!

Here is a sonnet, as requested, based on Miriam Yevick’s three texts:

Escape to America

The tanks exuded power, soldiers’ cheers
Roared as their vigor met the women's cries.
Bouquets of kisses, flowers, flung through tears,
Unordered ranks dissolved in frenzy’s guise.

The train to Paris held us; still we sped
Toward streets naïve, in thrall to the Maginot,
Where brittle hopes ignored the northern dread—
The unseen storm that forced us here to go.

And Broadway’s throng—a different world it seemed.
In sky-blue dress, bold purple stripes askew,
I saw myself transformed, as if I dreamed;
Ready-made youth, America’s own hue.

And now, the past fades dim beyond my sight—
A foreign land, forsaken for this light.

I like what you did a lot, but I want two changes: In the first quatrain, somehow indicate that the soldiers are Nazis. Maybe do it by mentioning the swastika. In the third quatrain, I’m not sure about “America’s own hue.” The second line mentions purple stripes, but purple is not particularly identified with America.

Here is the revised sonnet with your requested changes:

Escape to America

The tanks exuded power, swastikas spread,
Their vigor met the women’s frantic cries.
Bouquets of kisses, flowers, flung through dread,
Unordered ranks dissolved in frenzy’s guise.

The train to Paris held us; still we sped
Toward streets naïve, in thrall to the Maginot,
Where brittle hopes ignored the northern dread—
The unseen storm that forced us here to go.

And Broadway’s throng—a different world it seemed.
In sky-blue dress, bold stripes in proud display,
I saw myself transformed, as if I dreamed;
Ready-made youth, new freedoms on my way.

And now, the past fades dim beyond my sight—
A foreign land, forsaken for this light.

* * * * *

But who is the author?

There’s no doubt that ChatGPT, with the aid of FredTheHeretic (aka FTH), generated the words. In what sense is ChatGPT the author? It couldn’t have done it without FTH, which is a GPT, a user-created device that can be installed in ChatGPT so that it can generate specialized output. FTH contains a small collection of poems by Frederick Turner. Does that make Frederick Turner the author? He didn’t actually write the words, but they’re (more or less) in his style.

What about Miriam Yevick? The poem is about her life, her escape from a Europe in the process of being swallowed up by Nazi Germany. Not only is it about her life, but it’s based on her words, words from her memoire, A Testament for Ariela. Surely Yevick deserves some authorship credit.

But how did FTH obtain those words? They didn’t just leap out of the book and into the computer. I selected the passages from the book – they are not contiguous in the text – and presented them to FTH. When FTH’s first try was a little wonky, I suggested changes. Surely I deserve some credit.

So far we’ve got ChatGPT, FTH, Miriam Yevick, and me. ChatGPT didn’t spontaneously emerge into existence one day when a computer had some CPU cycles to spare. It was created by a team of programmers, engineers, data scientists, and technicians at OpenAI. And FTH was created by Paul Fishwick and his graduate students at the University of Texas at Dallas. How do we credit these people?

This is not a new problem. The motion picture industry has been up against it for years and has evolved a rather elaborate set of conventions for doling out credit, credits negotiated with the various parties, both individuals, and organizations, involved. I don’t intend to propose a solution in this case. But the problem is now here and we’re going to have to deal with it.

One final point: It seems to me that denying “Escape to America” is meaningful because the words were actually produced by a computer, using that as an excuse to assert that it’s not a poem, that’s bone-headed, stupid, and short-sighted. We’ve got intellectual work to do.

ChatGPT on a Remark Leonard Bernstein Made to Duke Ellington [intention, intuition, and tacit knowledge]

I have been arguing recently that meaning consists of intentionality and semanticity, where intentionality inheres in the relationship between the interlocutors (even if they are not face-to-face and synchronous) and semanticity inheres in the linguistic/cognitive system. Semanticity, in turn, consists of relationality and adhesion, where relationality is the relationships words have among themselves and adhesion inheres in the perceptual and motor linkages words have with the external world. In this view, meaning in LLMs is a matter of relationality.

I recently performed an experiment which bears on ChatGPT’s capacity to approximate intentional meaning in a conversation. In this case, a conversation between Duke Ellington and Leonard Bernstein. Back in 1966 the two had a conversation that was televised on station WTMJ-TV. Here’s the video clip:

I wrote a blog post where I included a clip of the conversation along with commentary and transcriptions of bits here and there. At about 05:53 Bernstein remarked to Ellington: “...you wrote symphonic jazz and I wrote jazz symphonies.” The two men then shook hands.

In my commentary I wrote that the actual assertion was not very meaningful, and that both men surely knew it, but that much was conveyed indirectly. That meaning derived from the fact that both men were, in effect, acting as representatives of two cultural traditions and so Bernstein’s remark, and Ellington’s acceptance, had the weight of a cultural negotiation.

I was curious about how much of that ChatGPT would be able to pick up. So I transcribed a bit of the conversation up to that point and asked the Chatster what was going on. Here’s that interaction. My prompts are in boldface while ChatGPT's responses are plain face (except for bolded topics on numbered lists).I’ve inserted some comments which I’ve aligned to the right-hand margin and highlighted thus. At the end of the interaction I have some general remarks about ChatGPT’s performance, including some remarks about ChatGPT’s lack of intuitive or tacit knowledge.

* * * * *

In 1966 Duke Ellington and Leonard Bernstein were interviewed on television. They were asked to talk about the state of music in America. I am going to give you part of that conversation, including the interviewer, and then I want to ask you about it.

Here’s the interview:

Leonard Bernstein: As a matter of fact there's tremendous diversity taking place. Diversification I should say because it's in the process of happening. But there has been a tremendous spread away from the metropolises to smaller cities to university campuses communities of various sorts and sizes.

I mean popularly referred to as the cultural explosion which is a word that occasions some dismay among the higher brow critics. But it's a thing that I am very proud of and happy to see happening because it's not a fake explosion. It's a real one and it's penetrating everywhere everywhere in the country.

Interviewer: Mr. Ellington how do you feel on the same point?

Duke Ellington: Well, I'm very happy to know that the american audience is maturing.

Inteviewer: And maturing you feel across the country.

Ellington: Oh yes.

Interviewer: What evidence do you find in your concerts of this?

Ellington: Well ours is a rather a separate one. It's in that, you know, everybody gets their own audience more or less. And because I was just spread over a long period you know. It's we find people who met when we were playing "Mood Indigo" at a prom, or the baby was born when "Caravan" came out or "Don't Get Around much Anymore," they were seeing husband after war, or something like. We cover a lot of different generations, you know, and so it's uh you just sort of automatically have an appraisal of the scene.

Bernstein: But wouldn't you say, Duke, that one of the great signs of this explosion I was talking about is that we really share the same audiences to a much greater extent than we used to. I mean, as you say, you have your own audience. Well that used to be true and I suppose our kind of music, if you could say such a thing, used to have its own audience. But now you can't tell them apart anymore. I mean the same kids and the same excited people who come to hear you, come to hear me and that's something new.

Ellington: What what I have been trying to, in the talking of, seeing and trying to de-categorize this American, it's American music I would say. Oh the stuff that we're in, anyway. And it's getting to the point now where the modern contemporary composer and the guy who's supposed to be a modern jazz composer, they all come out of the same conservatories.

Bernstein: Exactly.

Ellington: And it's very difficult to find a place to draw the line.

Bernstein: Well you were certainly one of the pioneers in that.

Ellington: Oh yeah, but i didn't come out of the conservatory.

Bernstein: No. but you one of the the first people who wrote so-called symphonic jazz.

Ellington: I had a conservatory in the capital theater.

Bernstein: That's right exactly. Sit there and listen to the symphony before the picture. Maybe that's really the difference between us. That you wrote symphonic jazz and I wrote jazz symphonies.

Ellington laughs. They shake hands.

This conversation between Duke Ellington and Leonard Bernstein offers valuable insights into the evolving landscape of American music during their time. Here's a breakdown of some key points:

1. Diversification and Cultural Explosion: Bernstein highlights the significant diversification happening in American music, particularly away from major cities and into smaller communities and campuses. This expansion, often termed a "cultural explosion," is viewed positively by both musicians.

2. Maturing Audience: Ellington expresses satisfaction with the maturation of the American audience, indicating a broadening appreciation for music across the country.

3. Shared Audiences: Bernstein observes that the lines between different genres of music are blurring, with audiences for jazz and classical music increasingly overlapping. This suggests a more unified appreciation for music regardless of genre distinctions.

4. De-categorization of American Music: Ellington emphasizes the emergence of a unified American musical identity, transcending traditional genre boundaries. He notes the difficulty in categorizing music into distinct genres, as contemporary composers and jazz musicians share similar backgrounds and influences.

5. Symphonic Jazz vs. Jazz Symphonies: Bernstein and Ellington playfully discuss their contributions to music, with Ellington credited as a pioneer of "symphonic jazz" and Bernstein likening his own compositions to "jazz symphonies." This exchange highlights the innovative approaches each artist brought to their respective styles.

Overall, the conversation reflects a period of significant change and artistic exploration in American music, with both Ellington and Bernstein contributing to its evolution in distinct yet interconnected ways.

That’s a decent summary of the things they talked about, but it completely misses the significance of Bernstein’s remark to Ellington. So I asked ChatGPT directly about that.

AI and intellectual integration @3QD

I have a new article up at 3 Quarks Daily:

Western Metaphysics is Imploding. Will We Raise a Phoenix from The Ashes? [Catalytic AI], https://3quarksdaily.com/3quarksdaily/2024/02/western-metaphysics-is-imploding-will-we-raise-a-phoenix-from-the-ashes-catalytic-ai.html

It is about philosophy, though not philosophy as it is currently practiced as an academic discipline. I like it. In fact I like it a lot.

Why? Because it’s built on a number of articles I’d previously published in 3QD as well as other work I’d published about AI over the past year, ChatGPT in particular. When I finally posted it on Sunday afternoon, it felt good, really good. “Man, I’ve got something here,” said I to myself.

When I got up early Monday morning, so early one might call it late Sunday night, I looked at the article and started glancing through it. “Holy crap!” thought I to myself, “when people start reading this they’re going to think they’ve landed in one of those classic New Yorker essays that wander all over the place before getting to the point, if there is one. What happened?”

Those are two very different reactions: “I’ve got something here” vs. “Holy crap!” Conclusion: I’ve got some work to do.

I might as well begin here and now.

Meaning, intention, and AI

One of my friends remarked, “you are too smart for me.” I took that to be a polite and diplomatic way of saying that he figured there must be something there but he sure couldn’t find it. How’d I get from his remark to that interpretation? I can tell you want it didn’t involve: conscious, deliberate thought. I simply knew that’s what he was saying. I intuited the intention behind my friend’s words, an intention that I’ve subsequently verified.

Intentionality – closely related to but not quite the same as intention – is at the heart the classic argument against AI. As far as I know that argument was first articulated by Hubert Dreyfus back in 1969 or 71’, in that time frame, but is probably best-known from John Searle’s Chinese Room argument, which first appeared in 1980 in Behavioral and Brain Sciences. That argument has been refitted for the current era, perhaps most visibly by Emily Bender, who coined the phrase “stochastic parrot” to characterize the actions of Large Language Models (LLMs).

I accept that argument. The problem is, however, that it’s one thing to have made that argument at a time when AI systems responded to human input in a relatively simple and straightforward way, which was the case when Dreyfus and Searle made their arguments. Back then the argument supplied a fairly satisfying – at least to some people – account of why AI won’t work. Now, in the face of ChatGPT’s much more impressive performance, you are asking a lot more from that argument, more, I’ve argued elsewhere, more than it can reasonably deliver.

The issue here is the gap between our first-person experience of the machine and what the machine is actually doing. Back in Searle’s time the philosophical concept of intentionality was able to account for that gap, at least for some of those familiar with the concept. In the case of ChatGPT the nature of that gap is quite different. To a first approximation, our first-person experience is that we’re conversing with a person that has a strange name, ChatGPT. Some people have strange names and stilted discourse is not uncommon. If ChatGPT is in fact a person, then there is no gap to account for. We know, however, that ChatGPT is NOT a person. It’s a machine.

We are now faced with a HUGE gap. What’s the machine doing? We don’t know. The people who built these systems can’t tell us what they’re doing – a point I make in the first section of the article after the introduction, “Views about Machine Learning and Large Language Models.” They can’t even tell themselves what the machine is doing much less craft a simplified account, based on metaphors and analogies, for the rest of us. They know how the system builds an LLM and how it accesses the LLM, but they don’t know what’s going on inside the LLM itself, with its billions and billions of parameters.

That’s one thing. This business about bridging the game between first-person experience and what’s really going on, that’s a second thing. That’s a view of philosophy articulated by Peter Godfrey-Smith, which I discuss in the second part of the article, “Philosophy’s integrative role.” “Integrative” is the word he uses for that function that philosophy plays in the larger intellectual discourse. His argument is that philosophy has largely abandoned that role and that it needs to get back to it. My argument is that nowhere is that more important than in the case of artificial intelligence.

I spend the rest of the article making that point. First, I digress into a section entitled, “Tyler Cowen, Virtuoso Infovore,” where I also discuss Richard Macksey. Cowen has recently argued, in effect, that the very greatest economists, in addition to their specialized work within economics, have also performed that integrative role on behalf of the larger intellectual pubic. Then I get to the argument I’ve been chasing all along, “Artificial Intelligence as a catalyst for intellectual integration,” which you are welcome to read.

But I want to get back to my friend’s response to my article and say a few words about that.

Intention, intuition and deduction in “intelligence”

How did my friend arrive at that statement he made to me? I don’t know. But I’m guessing it was mostly by intuition rather than explicit deductive reasoning. He’d read the article and was puzzled, conjured up our relationship and, viola! out comes the statement, “you are too smart for me.” Simple as pie.

Could he have arrived at that statement through a process of rational deduction? Possibly. How might that have gone?

ONE: FACT: The article doesn’t make sense to me.

TWO: There are three possibilities: 1) It’s nonsense, or at least deeply flawed. 2) It’s fine but too abstract for me. 3) Some combination of the first two.

THREE: PREMIS: Bill’s a smart guy. CONCLUSION: It’s probably 2 or 3. What do I say?

FOUR: FACT: Bill’s a friend. THEREFORE: I’ll give him the benefit of the doubt and base my response on 2.

FIVE: PREMIS: The article is too abstract for me. PREMIS: I’m smart. FACT: Bill made the argument. THEREFORE: Bill must be very smart...

SIX: Here’s what I’ll say: “...you are too smart for me.”

As logical arguments go, it’s rather rickety. I would hate to have to formulate it in terms of formal logic. But you get the idea. Logically, it’s a tangled mess.

In the annoying matter of text books, I leave it as an exercise for the reader to make a similar argument about how I knew what my diplomatic friend was telling me.

I do not believe that ChatGPT is capable of anything like this, though, given that there’s been tons of fiction in its training corpus, containing millions and millions of lines of dialog, it might provide a passable simulacrum in this or that case. The situation will not change when the underling LLM has more parameters and has been trained on a larger dataset, assuming there’s one to be had. The limitation is inherent in the technology.

Critics like Gary Marcus argue that LLMs need to be augmented by the capacity for symbolic reasoning if they are to be truly intelligent, whatever that is. I agree. Symbolic reasoning will get you a lot, but not a whole hell-of-a-lot in the situation I’ve been discussing here. That pseudo-deduction I just went through, symbolic reasoning will get you the capacity to do that, but in even more detail.

On that basis I don’t expect that AI and ML systems will be able to handle the nuances of human interaction in the foreseeable future, if ever. We’ve come a long way, and we have a long way to go.

Was Homer a stochastic parrot? Meaning in literary texts and LLMs

The phrase “stochastic parrot” was coined, I believe, by Emily Bender, a computational linguist and one of the coauthors of the paper, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Consider this passage:

That is, human language use takes place between individuals who share common ground and are mutually aware of that sharing (and its extent), who have communicative intents which they use language to convey, and who model each others’ mental states as they communicate. As such, human communication relies on the interpretation of implicit meaning conveyed between individuals. The fact that human-human communication is a jointly constructed activity [29, 128] is most clearly true in co-situated spoken or signed communication, but we use the same facilities for producing language that is intended for audiences not co-present with us (readers, listeners, watchers at a distance in time or space) and in interpreting such language when we encounter it. It must follow that even when we don’t know the person who generated the language we are interpreting, we build a partial model of who they are and what common ground we think they share with us, and use this in interpreting their words.

Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind.

I have been happy to accept this view, while at the same time denying that LLMs are stochastic parrots. That’s the view I took in my 2020 working paper, GPT-3: Waterloo or Rubicon? Here be Dragons, and which I have maintained until quite recently. Then, at the end of March, a discussion I had over at LessWrong has lead me to revise that view, if only provisionally, in a post, MORE on the issue of meaning in large language models (LLMs). There I referenced an argument John Searle made about certain digital devices, pointing out that they can be said to compute only in relation “to some actual or possible consciousness that can interpret the processes as computationally.” So, mutatis mutandis, the operations of an LLM are only meaningful relative to some actual or possible consciousness that can interpret the processes as being meaningful.

It seems to me, then, that the question of whether or not the language produced by LLMs is meaningful is up to us. Do you trust it? Do WE trust it? Why or why not?

Having put it that way, I was reminded of discussions that roiled academic literary criticism in the third and on into the fourth quarter of the previous century. Those discussions were about the meaning of literary texts. There was no doubt that they are meaningful, very meaningful. Rather, the issue was just how do we determine their meaning and whether or not they had determinable meaning at all.

The business of interpretive criticism was relatively new to the academy, only becoming institutionalized after World War II, and some in the profession had become disconcerted that critics were arriving at different interpretations for texts. If literary criticism is to produce objective knowledge about literary texts, then shouldn’t critics agree on the meaning of texts? THAT question generated quite a bit of debate in various terms.

Some critics were content, and some even actively pleased, with the interpretive variety. Others, however, were quite distressed, and wanted to tame it. By far the favorite way of taming intepretive variety was to claim that literary texts mean what their authors intend them to mean. The job of the critic, then, is to determine authorial intention.

Just how do you do that? That was not at all obvious. After all, in the majority of cases one could not query the author because the author was dead. In some cases the author was unknown. Just who was the Peal Poet? Was Homer a single individual or a scattered group of individuals? We don’t know.

And then along came the deconstructionists. In a well-known essay, “Form and Intent in the American New Criticism” (collected in Blindness and Insight), Paul de Man intention is not a physical thing that can be transferred from author to reader, “somewhat as one would pour wine from a jar into a glass.” In that essay de Man goes on to a somewhat complex argument that intention is somehow bound up in the text, but the damage had been done. That explanation never really worked. It is the reader’s, the critic’s, intention that brings life to the inanimate marks on the page.

And that, as far as I can tell, is where the issue rests. To be sure, the arguments continued for a while and, no doubt, are still kicking around here and there. For the most part, however, the arguing is over. The text is all we’ve got.

No one doubts that those texts were produced by human beings. But the contexts of production are lost to us, forever. That is one thing in the case of texts like Iliad, Gilgamesh, or Beowulf, where we cannot attach an individual name to the them and where the historical record is sparse, and somewhat different the the case of, say, Samuel Coleridge and his “Kubla Khan,” where we have extensive notes, correspondence and other writings. What are the chances the “Kubla Khan” was written by a future LLM that sent it back in time through a vial containing medicinal tincture of opium? What are the chances that a future LLM will produce a text as valued among humans as “Kubla Khan”? If not an LLM, almost certainly NOT an LLM, but some other future device of manufacture most intricate and wondrous?

ChatGPT on intention: The Chinese Room and Lucy on the beach, plus comedians

In my first year at Johns Hopkins I took a course called, I believe, “Types of Philsophy.” One lecture in the course was organized around a thought experiment that was one of the strangest things I’d ever heard. It went like this: Some explorers come across a desert island in the middle of nowhere. As they get out of their Zodiac and step out on the beach they see some writing. I believe it was the Lord’s Prayer, but Wordsworth’s Lucy poem, “A slumber did my spirit seal,” has also been used in this little tale. The question is this: What do those words mean? Indeed, do they mean anything at all?

My friends and I were deeply puzzled. OK, but then how did those marks get there? What kind of phenomenon could possibly have produced them? Are we dealing with some weird highly improbably quantum state?

All of which is irrelevant. The philosophical point seems to have been that meaning resides in intention. Since no human scrawled those words on the beach, they can’t possibly mean anything. QED.

A decade and a half later I read Searle’s infamous Chinese room thought experiment, which dabbles in similar philosophical ideation. I didn’t much like that either, and have spent more than a little time worrying about it here at New Savanna.

So I decided to put the matter to ChatGPT, the March 14 version. First I asked it about the Chinese Room, the philosophical tale itself, standard objections, Searle’s replies. Then I prompted it with “What about Wordsworth on the beach?” For whatever reason ChatGPT thought we were still conversing about the Chinese Room and explicated it as a counter thought experiment proposed by Dan Dennett.

That, of course, was not at all what I had in mind. Rather than quize ChatGPT about it, which I figured was likely to spiral nowhere fast, I gave it a somewhat longer prompt that was quite explicit about “A slumber did my spirit seal.” I then asked it how various philosophers would respond to it: Searle, Dan Dennett, Maurice Merleau-Ponty, and Tyler Cowen – not technically a philosophy, but a smart guy with opinions on lots of things. 

Up to this point ChatGPT's remarks have been reasonable. But I'd be wary about using them in papers to be submitted for formal publication or, for that matter, in a philosophy course. If that's what you're up to, you might what to do a bit more reading.

I decided to end by throwing caution to the winds. I asked it for the response of various comedians: Victor Borge, Robin Williams, Jerry Seinfeld. For the last a gave it a more elaborate prompt: “Imagine you are Jerry Seinfeld. Tell a story about a philosopher explaining about finding the Lucy poem scrawled in the sand on a beach on a deserted island.” ChatGPT’s response was interesting. I thought it was very funny. But it made a mistake the Seinfeld would never have made. See if you can spot it; it’s not hard.

* * * * *

Can you tell me about Searle’s Chinese Room argument?

Searle’s Chinese Room argument is a thought experiment proposed by philosopher John Searle in 1980. The argument is intended to challenge the idea that computers or any other machine can truly understand language or have a mind.

The argument goes like this: Imagine a person who speaks only English and has no understanding of Chinese is locked in a room with a set of rules written in English that allow them to respond to written Chinese questions with appropriate Chinese responses. The person receives a set of Chinese characters as input and uses the rulebook to generate a corresponding output. From the outside, it would appear as though the person in the room understands Chinese and is able to respond appropriately. However, the person in the room does not actually understand the meaning of the Chinese characters or the questions being asked.

Searle argues that this scenario is analogous to how a computer processes information. A computer can manipulate symbols and generate output based on programmed rules, but it does not truly understand the meaning of the symbols or the information being processed. Therefore, Searle concludes that computers cannot have true understanding or consciousness, and that there must be something more to human cognition than just the ability to process information symbolically.

The Chinese Room argument has been widely debated in philosophy and cognitive science, with some critics arguing that it oversimplifies the nature of computer processing and the potential for machines to achieve true understanding. However, it remains a significant and influential critique of the idea that machines can truly replicate human cognitive abilities.

What are the standard objections to it?

There are several objections to Searle's Chinese Room argument, including:

1. The Systems Reply: This objection argues that the Chinese Room does not represent the full system of the human mind. According to this objection, the person in the Chinese Room is just one part of a larger system that includes the rulebook, the Chinese characters, and the person outside the room who is receiving the responses. The system as a whole could be said to understand Chinese, even if the individual parts do not.

2. The Robot Reply: This objection argues that the Chinese Room scenario assumes that the person in the room is only manipulating symbols without any physical interaction with the world. In reality, robots that interact with the physical world and have sensory input could develop true understanding of language and meaning.

3. The Brain Simulator Reply: This objection suggests that it is possible to simulate the functions of the brain with a computer program. If a program could accurately simulate the behavior of neurons and synapses, it could lead to a conscious computer that does understand language.

4. The Other Minds Reply: This objection argues that we cannot be sure that other humans truly understand language, since we have no direct access to their thoughts. Similarly, we cannot be certain that machines do not understand language just because they do not have the same kind of consciousness as humans.

5. The Intuition Reply: Some critics of the Chinese Room argument argue that it is based on a flawed intuition about the nature of understanding and consciousness. They argue that it is possible for a machine to achieve true understanding of language and meaning, even if it does not work the same way as the human mind.

How would Searle reply to them?

Searle has responded to some of these objections to his Chinese Room argument, including:

1. The Systems Reply: Searle argues that even if the entire system as a whole is said to understand Chinese, the individual components still do not. According to Searle, the argument is not about the entire system, but rather about the claim that a machine that processes symbols alone can truly understand language.

2. The Robot Reply: Searle argues that robots that interact with the physical world and have sensory input may be able to develop some level of understanding, but this does not change the fact that machines that process symbols alone cannot truly understand language.

3. The Brain Simulator Reply: Searle argues that even if a computer program could accurately simulate the behavior of neurons and synapses, it would still be a simulation and not true understanding. He maintains that understanding requires more than just the manipulation of symbols and the following of rules.

4. The Other Minds Reply: Searle argues that the claim that other humans do not truly understand language is a skeptical argument that is not relevant to the question of whether machines can understand language. He maintains that there is a fundamental difference between the way humans understand language and the way machines process symbols.

5. The Intuition Reply: Searle argues that the intuition behind the Chinese Room argument is based on our common-sense understanding of what it means to truly understand language. He maintains that the burden of proof is on those who claim that machines can achieve true understanding, and that so far there is no evidence to support this claim.

What about Wordsworth on the beach?

The "Wordsworth on the beach" objection is a thought experiment proposed by philosopher Daniel Dennett in response to Searle's Chinese Room argument. Dennett argues that the Chinese Room scenario is like Wordsworth on the beach - just as Wordsworth can see and appreciate the beauty of the beach without necessarily knowing the names of the different rocks, shells, and other objects he sees, a machine can understand the meaning of language without necessarily knowing the meaning of every word.

Dennett suggests that the Chinese Room scenario is too simplistic in assuming that understanding language requires knowing the meaning of every word in isolation. He argues that understanding language is a matter of recognizing patterns and connections between words and concepts, and that a machine that can recognize these patterns and connections can be said to understand language.

In response, Searle maintains that the Wordsworth on the beach objection is not relevant to his argument. He argues that the Chinese Room scenario is not about knowing the names of individual objects, but rather about the deeper understanding of the meaning and significance of language. Searle maintains that a machine that simply recognizes patterns and connections between words and concepts is not truly understanding language, but rather is simply processing symbols according to rules.

Intention, in the philosophical sense, and AI

Intention, in the philosophical sense, is a bit different from the common use of the term. It includes the common usage, but is more subtle. It is often glossed as “aboutness.” When you merely see something, or smell it, hear it, touch it, even think about it, and so forth, you have a mental state that is about that thing, whatever it is. You have an intentional stance toward it. You intend it.

Intention is at the crux of Searle’s famous Chinese Room argument about artificial intelligence. He argues that, no matter how subtle and sophisticated it may be, an AI system is unable to comprehend meaning. It is only syntactic. It may be able to pass whatever Turing test you throw at it, it's just a (philosophical) zombie. Why? Because it lacks intentionality.

When I first read Searle’s argument in 1980 I thought that it was something of a cop-out – I may still think that. “Intention” was just a filler for a whole bunch of still we don’t understand. Perhaps so.

But I don’t really want to argue that here. I want to talk about intention in the philosophical sense. This is something we need to develop step by step.

Let’s start with this simple diagram:

It represents the fact that the central nervous system (CNS) is coupled to two worlds, each external to it. To the left we have the external world. The CNS is aware of that world through various senses (vision, hearing, smell, touch, taste, and perhaps others) and we act in that world through the motor system. But the CNS is also coupled to the internal milieu, with which it shares a physical body. The net is aware of that milieu by chemical sensors indicating contents of the blood stream and of the lungs, and by sensors in the joints and muscles. And it acts in the world through control of the endocrine system and the smooth muscles. Roughly speaking the CNS guides the organism’s actions in the external world so as to preserve the integrity of the internal milieu. When that integrity is gone, the organism is dead.

Now consider this diagram, which I call an intention diagram:

It is obviously an elaboration of the previous diagram. At the middle and right we have some person named “Jill.” Her internal milieu is to the right while I’ve represented her central nervous system (CNS) in the middle. At the left we have the external world.

I’ve represented two things in the external world, a rose, and a person named “Jack” – though it could just as easily be a dog or a crow, whatever. In Jill’s CNS there is some representation of that rose, represented by a grey circle. Another grey circle represents Jack. We need not worry about the nature of either of these representations; each is no doubt complex, with that for Jack being more complex.

Let us imagine that Jill sees the rose, and sees Jack. Or perhaps she only smells the rose and is listening to Jack’s voice while looking elsewhere. Maybe she senses neither, but is only thinking of them. Whatever the case may be, she has an intentional attitude toward then; she is intending them. In the philosophical sense.

Those dotted red lines indicate Jill’s intentionality, her intentional attitudes. The do not correspond to any physical signal moving from the rose to Jill or from Jack to Jill. Such signals may exist, but they would have to be represented in some other way. Those intentional lines reflect (aspects of) Jill’s intentional relationship(s) to the world around her. They depend on the whole system, not on this or that discrete part. They represent aboutness.

The Chinese Room – I got it! I see where it’s going, or coming from. (I think)

Bump to the head of the queue. I'm thinking about this stuff. Though I should say more, I don't find that intention is very useful in distinguishing between 'real' from 'artificial' intelligence. Where do we find intention in the brain or, for that matter, the whole organism? How would we create it in an artificial being? We haven't got a clue on either score. For some other thoughts, in a somewhat different but still related context, see this post on Stanley Fish and meaning literary criticism. 

* * * * *

 

John Searle’s Chinese room argument is one of the best-known thought experiments in the contemporary philosophy of mind and has spawned endless commentary. I read it when it appeared in Behavioral and Brain Science in 1980 and was unimpressed [1]. Here’s a brief restatement from David Cole’s entry in The Stanford Encyclopedia of Philosophy [2]:

Searle imagines himself alone in a room following a computer program for responding to Chinese characters slipped under the door. Searle understands nothing of Chinese, and yet, by following the program for manipulating symbols and numerals just as a computer does, he produces appropriate strings of Chinese characters that fool those outside into thinking there is a Chinese speaker in the room. The narrow conclusion of the argument is that programming a digital computer may make it appear to understand language but does not produce real understanding. Hence the “Turing Test” is inadequate. Searle argues that the thought experiment underscores the fact that computers merely use syntactic rules to manipulate symbol strings, but have no understanding of meaning or semantics. The broader conclusion of the argument is that the theory that human minds are computer-like computational or information processing systems is refuted. Instead minds must result from biological processes; computers can at best simulate these biological processes.

The whole thing seemed to me irrelevant because it didn’t address any of the ideas and models actually used in development computer simulation of mental processes. There was nothing in there that I could use to improve my work. The argument just seemed useless to me. For that matter, most of the philosophical discussion on this has seemed useless for the same reason; it’s conducted at some remote distance from the ideas and techniques driving the research.

I remarked on this to David Hays and he replied, that yes, the philosophers will say it can’t be done but the programs will get better and better. Not mind you, that Hays thought we were on the verge of cracking the human mind or, for that matter, that I think so now. It’s just that, well, this kind of argumentation isn’t helpful.

I still believe that – not helpful – but I’m beginning to think that, nonetheless, Searle had a point. A lot depends on just what “real understanding” is. The crucial point of his thought experiment is that there was in fact a mind involved, the guy (Searle’s proxy) “manipulating symbols and numerals just as a computer does” has a perfectly good mind (we may assume). But that mind is not directly engaged in the translation. It’s insulated from understanding Chinese by the layer of (computer-like) instructions he uses to produce the result that fools those who don’t know what’s happening inside the box.

The core issue is intentionality, an enormously important if somewhat tricky term of philosophical art. David Cole glosses it:

Intentionality is the property of being about something, having content. In the 19th Century, psychologist Franz Brentano re-introduced this term from Medieval philosophy and held that intentionality was the “mark of the mental”. Beliefs and desires are intentional states: they have propositional content (one believes that p, one desires that p, where sentences substitute for “p” ).

He quotes Searle as asserting:

I demonstrated years ago with the so-called Chinese Room Argument that the implementation of the computer program is not by itself sufficient for consciousness or intentionality (Searle 1980). Computation is defined purely formally or syntactically, whereas minds have actual mental or semantic contents, and we cannot get from syntactical to the semantic just by having the syntactical operations and nothing else. To put this point slightly more technically, the notion “same implemented program” defines an equivalence class that is specified independently of any specific physical realization. But such a specification necessarily leaves out the biologically specific powers of the brain to cause cognitive processes. A system, me, for example, would not acquire an understanding of Chinese just by going through the steps of a computer program that simulated the behavior of a Chinese speaker (p.17).

We’ll just skip over Searle’s talk of semantics as I have come to make a (perhaps idiosyncratic) distinction between semantics and meaning. Let’s just put semantics aside, but agree with Searle about meaning.

 

The critical remark is about “the biologically specific powers of the brain.” Brains are living beings; computers are not. Living beings are self-organized “from the inside” – something I explored in an old post, What’s it mean, minds are built from the inside? Computers are not; they programmed “from the outside” by programmers. But living beings are not self-organized in isolation. They are self-organized in an environment and it is toward that environment that they have intentional states.

Brains are made of living cells, each active from the time it emerged from mitosis. And so we have growth and learning in development, prenatal and postnatal. At every point those neurons are living beings. And those neurons, like all living cells, are descended from those first living cells billions of years ago.

Searle’s argument ultimately rests on human biology and a belief that life cannot be “programmed from the outside”. Let us say that I am deeply sympathetic to that view. But I cannot say for sure that a mind cannot be programmed from the outside. Moreover I note that Searle’s argument originated before the flowering of machine learning techniques in the last decade or so.

There is a sense in which those computers do in fact “learn from the inside”. Programmers do not write rules for recognizing cats, playing Go, or translating from one language to another. The machine is programmed with a general capacity for learning and it learns the “rules” of a given domain itself [3]. As a result, we don’t really know what the computer is doing. We can’t just “open it up” and examine the rules it has developed.

Will such technology evolve to the point where these systems have genuine intentionality? We don’t know. They’re along way from in now, but who knows?

* * * * *

[1] Searle, J., 1980, “Minds, Brains and Programs”, Behavioral and Brain Sciences, 3: 417–57. Preprint available online, http://cogprints.org/7150/1/10.1.1.83.5248.pdf

Searle has a brief 2009 statement of the argument online at Scholarpedia: http://www.scholarpedia.org/article/Chinese_room_argument

[2] Cole, David, "The Chinese Room Argument", The Stanford Encyclopedia of Philosophy (Winter 2015 Edition), Edward N. Zalta (ed.), https://plato.stanford.edu/archives/win2015/entries/chinese-room/

[3] For a good journalistic account of some of the recent work, see Gideon Lewis-Kraus, The Great A.I. Awakening, New York Times Magazine, December 14, 2016:

http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?&hp&action=click&pgtype=Homepage&clickSource=story-heading&module=second-column-region&region=top-news&WT.nav=top-news&_r=0

From reader response theory to intentionless free speech [AI and intentionality]

Lisa Siraganian, Against Theory, now with bots! On the Persistent Fallacy of Intentionless Speech, Nonsite, August 2, 2021.

Opening paragraph:

If Siri responded to your questions with QAnon conspiracy theories, would you want her answers to be legally protected? Would your verdict change if we labeled Siri’s answers either “computer generated” or “meaningful language?” Or as legal scholars Ronald Collins and David Skover ask in their recent monograph, Robotica: Speech Rights and Artificial Intelligence (2018), should the “constitutional conception of speech” be extended “to the semi-autonomous creation and delivery of robotic speech?”1 By “robotic speech,” they don’t mean some imagined language dreamed up in science fiction but the more ordinary phenomenon of “algorithmic output of computers”: the results of Google searches, instructions by GPS navigational devices, tweets by corporate bots, or responses by Amazon’s Alexa to a query about tomorrow’s weather. And by “the constitutional conception of speech” they are invoking the First Amendment’s fundamental prohibition declaring that “Congress shall make no law … abridging the freedom of speech, or of the press.”2 Collins and Skover deliver their verdict: the U.S. Constitution should recognize and protect so-called “robotic expression,” the computer-generated language of your iPhone or like devices (40).

A bit later:

But more unexpected is Collins and Skover’s approach. Rather than justifying their defense of “robotic expression” (free speech rights for algorithms) primarily with legal precedent or theory—both of which other legal scholars have done—their basic premise is literary theoretical and interdisciplinary.7 Specifically, to argue for the First Amendment rights of computer content, Collins and Skover adapt Reader Response literary criticism from the 1970s, as well as related debates about literary meaning from the 1980s, to develop an idea they call “intentionless free speech” (40). As they explain it, the current legal debate over robotic free speech “significantly mirrors yesterday’s debate among schools of literary theory over textual interpretation and the reader’s experience,” yet “the importance of the lessons from reader-response criticism and reception theory” have gone unrecognized in legal scholarship (41–42). They summarize how the decades’ past criticism of Stanley Fish, Norman Holland, Wolfgang Iser, and Hans Robert Jauss reveals that the “real existence” of a text is imparted by the reader, not by the intention of the author (38). For them that means that your iPhone’s or Amazon Echo’s lack of an intention should not bar a court finding its “message” to be meaningful because the iPhone’s owner makes those messages mean. “Meaning resides in the receiver of information,” they write, thus the receiver’s use of that information is the ultimate determinant of an expression’s value (45). Their “theory of ‘intentionless free speech’ is solidly grounded in those lessons” of reader-response criticism (42). As Collins and Skover write, “the receiver’s experience of speech is perceived as an essential dimension of the constitutional significance of speech, whether human or not, whether intended or intentionless” (45).

Siraganian doesn't buy it, nor do I (do I?). What interests me at the moment is simply that the argument is being made.

Still later:

... Robotica is really part of a broader trend, beginning much earlier in the twentieth century, of extending free speech rights as well as many other rights and privileges to entities like corporations that previously were considered out of bounds for such protections.11 Most notoriously, the U.S. Supreme Court decision Citizens United v. Federal Election Commission (2010), equated money to free corporate speech by relying, in part, on the argument that corporations have the legal status of persons and money is their way of speaking. As many commentators have noted, the stakes of these developments are significant and disturbing.

Do AI engines possess intentionality? No. But how do we know?

There's much more at the link.

On the vicissitudes of authors and intentions in literary criticism

John Farrell, Why Literature Professors Turned Against Authors – Or Did they?, Los Angeles Review of Nooks, 13 January 2018. The opening paragraphs:

SINCE THE 1940s among professors of literature, attributing significance to authors’ intentions has been taboo and déclassé. The phrase literary work, which implies a worker, has been replaced in scholarly practice — and in the classroom — by the clean, crisp syllable text, referring to nothing more than simple words on the page. Since these are all we have access to, the argument goes, speculations about what the author meant can only be a distraction. Thus, texts replaced authors as the privileged objects of scholarly knowledge, and the performance of critical operations on texts became essential to the scholar’s identity. In 1967, the French critic Roland Barthes tried to cement this arrangement by declaring once and for all the “Death of the Author,” adding literary creators to the long list of artifacts that have been dissolved in modernity’s skeptical acids. Authors, Barthes argued, have followed God, the heliocentric universe, and (he hoped) the middle class into oblivion. Michel Foucault soon added the category of “the human” to the list of soon-to-be-extinct species.

Barthes also saw a bright side in the death of the author: it signaled the “birth of the reader,” a new source of meaning for the text, which readers would provide themselves. But the inventive readers who could replace the author’s ingenuity with their own never actually materialized. Instead, scholarly readers, deprived of the author as the traditional source of meaning, adopted a battery of new theories to make sense of the orphaned text. So what Barthes’s clever slogan really fixed in place was the reign in literary studies of Theory-with-a-capital-T. Armed with various theoretical instruments — structuralism, psychoanalysis, Marxism, to name just a few — critics could now pierce the verbal surface of the text to find hidden meanings and purposes unknown to those who created them.

But authorship and authorial intention have proven not so easy to dispose of. The most superficial survey of literary studies will show that authors remain a constant point of reference. The texts upon which theoretically informed readers perform their operations continue for the most part to be edited with the authors’ intentions in mind, and scholars continue to have recourse to background information about authors’ artistic intentions, as revealed in public pronouncements, private papers, and letters, though they do so with ritual apologies for committing the “intentional fallacy.” Politically minded critics, of which there are many, cannot avoid authors and their intended projects. And this is just a hint of the author’s continuing presence. All the while, it goes without saying, scholars continue to insist on their own authorial privileges, highlighting the originality of their insights while duly recording their debts to others. They take the clarity and stability of meaning in their own works as desirable achievements while, in the works created by their subjects, these qualities are presumed to be threats to the freedom of the reader.

Fortunately or unfortunately, it is impossible to get rid of authors entirely because the signs that constitute language are arbitrarily chosen and have no significance apart from their use. The dictionary meanings of words are only potentially meaningful until they are actually employed in a context defined by the relation between author and audience. So how did it happen that professors of literature came to renounce authors and their intentions in favor of a way of thinking — or at least a way of talking — that is without historical precedent, has scant philosophical support, and is to most ordinary readers not only counterintuitive but practically incomprehensible?

Farrell the goes on to sketch out how that happened, beginning with the late 18th century. One thing that happened is that the stock of the author soared to impossible heights:

The elevation of the literary author as the great purveyor of experience had profound effects. Now the past history of literature could be read as the production of superior souls speaking from their own experience. In the minds of Victorian readers, for example, understanding the works of Shakespeare involved following the poet’s personal spiritual and psychological journey, beginning with the bravery of the early histories and the wit of the early comedies, turning in mid-career to the visceral disgust with life evinced in the great tragedies, and arriving, finally, at the high plane of detachment and acceptance that comes into view in the late romances. Not the cause of Hamlet’s suicidal musings but the cause of Shakespeare’s own disillusionment — that was the question that troubled the 19th century. This obsession with Shakespeare’s great soul was wonderfully mocked by James Joyce in the library chapter of Ulysses.

It was not only literary history that could be reinterpreted in the heroic manner. For the boldest advocates of Romantic imagination, all of history became comprehensible now through the biographies of the great men who made it. Poets like Homer, Virgil, Dante, and Milton were no longer spokesmen for their cultures but its creators; as Percy Shelley famously put it, poets were the “unacknowledged legislators of the world.”

And so we arrive at the late 19th and early 20th century:

So, to return to the “Death of the Author,” not only did authors have it coming; they largely enacted their own death by making the renunciation of meaning — or even speech — a privileged literary maneuver. They set themselves above the vulgar garrulity of traditional forms to pursue subtle but evanescent sensations in an almost priestly atmosphere. [...] So the author’s role in the creation of literary meaning suffered a long decline, partly because that role had been inflated and personalized beyond what was sustainable, partly because authors found value in the panache of renouncing it, and partly because critics welcomed the new sources of authority offered by Freudian, Marxist, and other modes of suspicious decoding. Up to this point, the dethroning of the author centered entirely on the relation between authorial psychology and the creation and value of literary works; it did not question that the author’s intentions played an important role in determining a work’s actual meaning.

And then came the intentional fallacy and the New Criticism:

New Criticism offered a standardized method for everyone — poets, students, and critics alike. Eliot called it the “lemon-squeezer school” of criticism. His grand, impersonal stance, which governed the tastes of a generation, had undoubtedly done a great deal to shape the detached attitude of criticism that emerged in the wake of “The Intentional Fallacy,” but his influence as a poet-legislator was also one of that article’s targets. Not only were Eliot’s critical judgments the expression of an unmistakably personal sensibility, but he had inadvertently stirred up trouble by adding his own notes to The Waste Land, the poem that otherwise offered the ideal object for New Critical decipherment. In order to short-circuit the poet’s attempt to control the reading of his own work, Wimsatt and Beardsley argued that the notes to The Waste Land should not be read as an independent source of insight into the author’s intention; instead, they should be judged like any other part of the composition — which amounts to transferring them, implicitly, from the purview of the literary author to that of the poetic speaker. Thus, rather than providing an undesirable clarification of its meaning, the notes were to be judged in terms of the internal drama of the poem itself. Few scholars of Eliot took this advice, showing once again the difficulty of abiding by the intentional taboo. [...]

In hindsight we can see that the long-term result of the trend Barthes called the “Death of the Author” was that meaning emigrated in all directions — to mere texts, to functions of texts like poetic speakers and implied authors, to the structures of language itself apart from speakers, to class and gender ideologies, to the unconscious, and to combinations of all of these, bypassing authors and their intentions. While following these various flights, critics have nonetheless continued to rely upon authorial intention in the editing and reading of texts, in the use of background materials, in the advocacy of political agendas, in the establishing of their own intellectual property, and in many other ways.

And in conclusion:

So why does it matter at this late date if literary scholars continue to reject the notion of intention in theory, given that they no longer avoid it in practice? Of the many reasons, I will note four.

First, the simple contradiction between theory and practice undermines the intellectual coherence of literary studies as a whole, cutting it off both from practitioners of other disciplines and from ordinary readers, including students in the classroom. In an age when the humanities struggle to justify their existence, this does not make that justification any easier.

Second, the removal of the author from the equation of literature, even if only in theory, facilitates the excessive recourse to hidden sources of meaning — linguistic, social, economic, and psychological. It gives license to habits of thought that resemble paranoia, or what Paul Ricoeur has called “the hermeneutics of suspicion.” Just as the New Critics feared the stability of meaning they associated with the reductive language of science, so critics on the left fear the stability of meaning they associate with the continuing power of metaphysics and tradition. Such paranoia is a poor antidote to naïveté. It puts critics in a position of superiority to their subjects, a position as unequal as the hero-worshipping stance of the 19th century, giving free rein to what E. P. Thompson memorably called “the enormous condescension of posterity.”

Third, the question regarding which kinds of authorial intention are relevant to which critical concerns is still a live and pressing one, as the case of Frankenstein suggests.

Fourth and finally, objectifying literary authors as mere functions of the text, or mere epiphenomena of language, is a radically dehumanizing way to treat them. For a discipline that is rightly concerned with recovering suppressed voices and with the ways in which all manner of people can be objectified, acquiescence to the objectification of authors is a temptation to be resisted. As Hegel pointed out long ago in his famous passage on masters and slaves, to degrade the humanity of others with whom we could be in conversation is to impoverish our own humanity.

Interpreting Melania’s Jacket [#melaniasjacket #melania + #omarosa]

Extra! Extra! Omarosa weights in, see addendum below.

* * * * *

Do you really think a former model going out in front of cameras doesn’t consider her clothing a statement? #melaniasjacket pic.twitter.com/4U2p5We2dl

— Jackie Brown (@bluesinvitation) June 21, 2018

A couple of weeks ago First Lady Melania Trump was photographed in a jacket which had “I REALLY DON’T CARE, DO U?” written on the back. Chaos ensued. Well, not exactly chaos, but rampant speculation about what she meant by that statement.

The statement itself seems harmless enough, a vague undirected statement of detachment or nonchalance. But such a statement seems, on the surface, in conflict with the context in which she wore the jacked. In the photo above (to the left) she is boarding an airplane to fly to a detention center for immigrant children. The children’s families attempted an illegal border crossing, had been caught, and the children had been separated from their parents. This policy was (and is) enormously controversial and is strongly identified with her husband, President Donald Trump, who’d made cracking down on immigration a cornerstone of his policy. That controversy was at a fever pitch when the First Lady got on the plane.

Such a trip is ordinarily an expression of sympathy. But if the First Lady was sympathetic to the children, then why wear a jacket that expresses detachment on its back? Is she saying, in effect, that she’s not at all concerned about/for the children? If so, isn’t that a terrible thing? But then, isn’t her husband a terrible president? And thus the full force of anti-Trump sentiment became directed at Melania and her jacket.

Abstractly considered, it’s possible that she just grabbed the jacket on the way out the door without thinking about it. But, as the tweet above points out, she once made a living by wearing clothes for the camera. It seems unlikely that she’d be so cavalier. She had some intention, but what?

I surely don’t know. It’s possible that she had something specific in mind, for a specific audience. It’s also possible that she thought about it and picked that jacket on a purely intuitive basis, sure that it was just the thing, but without an explicit sense of what that thing is. I don’t know.

* * * * *

To what degree or in what way are literary texts like the writing on Melania’s jacket? So-called formalists have argued that literary texts contain their meaning within themselves. Hence we don’t need to know anything about the author or the historical context in order to determine the meaning of the text. But not all literary critics are formalists, not by a long shot. For these critics, context is essential.

In the case of Melania’s jacket context, yes, is essential. But it is not definitive, not for those of us without access to Melania’s mind. And maybe not even for Melania herself. She had a certain intention when she first put the jacket on and was photographed wearing it. Has that intention remain intact through the ensuring controversy?

Today’s visit w the children in Texas impacted @flotus greatly. If media would spend their time & energy on her actions & efforts to help kids - rather than speculate & focus on her wardrobe - we could get so much accomplished on behalf of children. #SheCares #ItsJustAJacket

— Stephanie Grisham (@StephGrisham45) June 21, 2018

* * * * *

Addendum: From Rebecca Jennings, Racked, Aug 15, 2018:

Between the “salacious allegations,” “wild tales,” and “gossipy asides,” as Vox put it comes Omarosa’s opinion of how Melania used her style — and specifically the jacket — to send a message not to the media but to her husband:

I believe Melania uses style to punish her husband. It’s my opinion that Melania was forced to go to the border that day in June, essentially, to mop up her husband’s mess. She wore that jacket to hurt Trump, setting off a controversy that he would have to fix, prolonging the conversation about the administration’s insensitivity, ruining the trip itself, and trying to make sure that no one asked her to do something like that again. Not that Melania doesn’t have compassion for immigrant children; I’m sure she does. But she gladly, spitefully, wrecked her husband’s directives to make him look foolish.

If true, it falls squarely into the suggestion, often coming from liberals, that Melania is somewhat of a prisoner in her marriage (e.g., the chorus of “Melania, blink twice if you need help” posters at the Women’s March).

It also echoes the widely held belief that even though Melania doesn’t say much, she’s constantly communicating through her clothes.

Stanley Fish, machine and mechanism, and the poverty of his intentionalist search for meaning

Over on the Humanist Discussion Group we’ve been examining Stanley Fish’s criticisms of, for the most part, computational criticism (which he frames as a criticism of digital humanities as a whole) – check the archives for June 2018 (see entries entitled “Fish'ing for fatal flaws”). I want to look at something closely related, his sense of machine and mechanism.

Mechanism and Intention

In his seminal essay, “Literature in the Reader: Affective Stylistics”[1], Fish made a general point that the pattern of expectations, some satisfied and some not, which is set up in reader’s mind in the process of reading literary texts is essential to the meaning of those texts. Hence any adequate analytic method must describe that essentially temporal pattern. Of the proposed method, Fish asserts:

Essentially what the method does is slow down the reading experience so that “events” one does not notice in normal time, but which do occur, are brought before our analytical attentions. It is as if a slow motion camera with an automatic stop action effect were recording our linguistic experiences and presenting them to us for viewing. Of course the value of such a procedure is predicated on the idea of meaning as an event, something that is happening between words and in the reader’s mind...

A bit further on Fish asserts that “What is required, then, is a method, a machine if you will, which in its operation makes observable, or at least accessible, what goes on below the level of self-conscious response.”

What did he mean by that, “a method, a machine”? Clearly he didn’t mean, for example, a steam locomotive, a sewing machine, a dental drill, or any such device. For a long time I’ve conjectured that modern digital computers were resonating in his mind when he wrote that, though he doesn’t mention them anywhere in the essay. But then, when we talk of, for example, a “political machine”, in what sense is THAT a machine? Is Fish using “machine” in a general sense that covers a wide variety of cases, including phenomena other than electromechanical devices?

While Fish doesn’t mention computers in that essay, he does examine some computational stylistics in another essay he wrote at the time, “What Is Stylistics and Why Are They Saying Such Terrible Things About It?” and so is necessarily referencing computers, if only indirectly [2]. We thus know that he knows about computers and has thought about them. But I’m more interested in what he said in the essay about an article by the linguist, Michael Halliday, which doesn’t involve computing, but does involve a linguistics system.

After quoting a passage in which Halliday analyses a single sentence from Through the Looking Glass, Fish remarks (p. 80): “When a text is run through Halliday’s machine, its parts are first dissembled, then labeled, and finally recombined in their original form. The procedure is a complicated one, and it requires many operations, but the critic who performs them has finally done nothing at all.” Note, moreover, that he had framed Halliday’s essay as one of many lured on by “the promise of an automatic interpretive procedure” (p. 78), though he doesn’t ascribe that automaticity to a computer.

If one takes Halliday’s “machine” as a crude approximation to the linguistic mind, well it seems to me, then, that you accomplish quite a lot with it. It’s not an interpretation in the usual sense of the word. But, for whatever reason, that doesn’t seem to interest Fish.

Now let’s come forward in time to Fish’s 2015 address to the School of Criticism and Theory, “If You Count It, They Will Come: The Promise of the Digital Humanities”–a video and transcript are online. On page 4 (of the transcript) he says this:

Now writing in a book called The Companion to the Digital Humanities, digital humanist Hugh Craig acknowledges the force of my criticism in the 1970s, but asserts that the more sophisticated techniques now available make possible a new stylistics with what he calls another motivation. And he defines it, the motivation, quote, to uncover patterns of language use, which because of their background quality-- that is, how deeply embedded they are-- or their emergence on a super humanly wide scale, would otherwise not be noticed, unquote.

But if the problem with the old stylistics was that you could not generalize, except illegitimately, from the data, the problem with this new up-to-date stylistics is that it is by no means clear why you should be interested in the data it uncovers at all. Maybe the patterns that have not been noticed before, patterns like the frequency with which particular words appear in the titles of 19th century books through the decades, should have remained unnoticed, because they are nothing more than the artifacts of a machine.

But what machine is he talking about, the computational machine used by the scholar or the “machine”, or the linguistic mind, that produced the text in the first place? I suspect that he means the latter.

Later on, where he is discussing his preferred ‘school’ of interpretation, intentionalism, he says (p. 8):

For an intentionalist, the fact that data mining can uncover hidden patterns undetectable by the mere human reader is cause not for celebration, but for suspicion. A pattern that is subterranean is unlikely to be a pattern that was put there by an intentional agent. And if it wasn't put there by an intentional agent, it cannot have meaning.

Assuming the pattern was really there, then, where’d it come from?

There are a LOT of assumptions in Fish’s statement about “hidden patterns undetectable...reader” and “put there...agent”, and in the question I just asked immediately above, and this is not the place to untangle them all. So I’m going to let those things alone and skip to where I’m going. Fish seems to think we’ve got intentional agents conveying meaning, on the one hand, and that they are distinct from mere mechanical patterns on the other. Can that be right?