Creating Imaginary Bank Notes with ChatGPT: AI as cultural technology and collective creativity

At the beginning of this week I published an article at 3 Quarks Daily, Tyler Cowen is the Tycho Brahe of Economics. I then decided to turn it into a PDF that people could download from my Academia page. So I uploaded the article to ChatGPT and asked it to create an illustration suitable for the cover. Here’s what it produced (Note: this image, as well as the others in this post, has a lot of detail; you can click on the image to see a larger version):

I told it to make an image suitable for the front page of that article. It then had to “read” the article and come up with an image. I liked the first image it produced, but that had the aspect ratio of a portrait, which isn’t what I wanted. I told it to make a square image. I liked that so much that I accepted it without any editing. What was my role in creating that image? I didn’t give ChatGPT any specific instructions.

A day or so later I asked to use that image as the basis for the design of an imaginary bank note. I told it that the note was to be issued in the name of the Free Principality of Mercatus and should be in the denomination of Five TC Busks. That’s it. ChatGPT did the rest. Here’s what it produced:

Another day later I created two bank notes for the Free Republic of 51 Pacific. 51 Pacific is the address of a building in Hoboken, NJ, where a friend had loft space in which he curated graffiti and held late night raves. The note was to be denominated in Knowledge Dollars. I uploaded this picture to use as source material:

Here’s the resulting bank note:

I then asked for a 20 dollar note with a pit bull on the bill (my friend has two pit bulls):

Again, what was my role?

I then repeated the process for these four Kisangani bank notes:

ChatGPT obviously did a lot. But what did I do?

I decided that it was time for a discussion with ChatGPT. This discussion bears on the notion of AI as a cultural technology, which has been advocated by Alison Gopnik and Henry Farrell among others, and on creativity as a collective phenomenon. Pay particular attention to an analogy I use midway in the dialog; it’s about strings of colored dots instead of strings of alpha-numeric characters. Here’s the dialog.

* * * * *

I assume you’ve been trained on lots currency and ornate certificates so you have a good idea of how those things are designed. I assume as well that each chat starts with a “blank slate” as it were, no memories of prior chats, no inherited context. But, since I asked you to make those currency designs from within the Kisangani Materials project, you do have access to project-level materials (documents I’ve uploaded). Do you have access to the other chats within the project?

Yes, broadly right, with a few qualifications.

I was almost certainly trained on many examples of paper currency, certificates, stock certificates, passports, banknote engravings, seals, guilloché patterns, holographic security devices, ornate borders, typography, and so forth. I don’t “retrieve” a specific banknote template, but the model has learned a high-dimensional statistical/compositional sense of what makes an object look like currency: denomination placement, corner numerals, intaglio-like linework, central emblems, security strips, microprint, official-looking typography, controlled symmetry, and dense ornamental fields.

On context: a new chat is not exactly a blank slate, but it is not a complete archive either.

In this Project, I have access to several kinds of context:

1. The current chat.
I see the banknote sequence we have just made: 20 Riffs with jivometric percussion, 5 Riffs with dancers and Homo Ludens, 50 Riffs with cell/solar-system science, and 100 Riffs with heart/neurons/circuits for human–doppelganger comity.

2. Project-level materials.
I can access uploaded Project documents when they are made available to me in this workspace. For example, the Kisangani working paper frames the whole project around Homo Economicus versus Homo Ludens, the Fourth Arena, play, and human-AI interaction, which clearly influenced the designs.

3. Some project conversation context may be summarized for me.
I may see summaries or selected snippets of other conversations in the project if the system has included them in the context for this chat. That is not the same as having free, searchable access to every prior Project chat.

4. Persistent memory / profile context may also be present.
I may have durable memories about your interests, projects, preferences, and recurring concepts. That is why I can often pick up the thread of “Kisangani,” “doppelgangers,” “Homo Ludens,” “jivometric,” “Fourth Arena,” and so on without needing you to re-explain everything.

But the important limitation is this: I do not have a general browser-like view of all your other chats inside the Project. I cannot just open a sidebar and inspect every previous conversation. I only know what is in the current chat, what has been injected as project context or memory, and what I can retrieve from uploaded files through the available file tools.

So for your friends: the banknotes came from an interaction among three sources of constraint:

• my general learned visual knowledge of currency and ornate official documents;
• your immediate prompts specifying denomination, motifs, slogans, and Kisangani themes;
• Project context about Kisangani as a post-economic, play-centered, human/AI civilizational experiment.

The “design intelligence” is not a stored memory of prior banknote conversations so much as a context-sensitive recombination engine operating over learned visual conventions plus the specific Kisangani conceptual field you have built.

Would it be reasonable to say that you can pick up a “vibe” from the Kisangani conceptual field?

Yes. “Vibe” is a reasonable informal term, provided we cash it out carefully.

What I can pick up is not an occult atmosphere or a hidden essence. It is a pattern of constraints, motifs, values, narrative cues, and stylistic expectations distributed across the Kisangani materials and our conversation. In practice, that “vibe” includes things like:

Kisangani is futuristic but not cyberpunk dystopian; technological but not technocratic; African/Afrofuturist but not reducible to ethnic ornament; musical, improvisatory, playful, civic, ritualized, and humane. It is organized around Homo Ludens, the Fourth Arena, doppelgangers, non-instrumental play, post-work social meaning, mutuality between humans and AI-like beings, and a suspicion of Homo Economicus. So when you ask for a banknote, I don’t merely think “make a fictional currency.” I infer that the note should look like a civic artifact from a society whose deepest value is not sovereign power, extraction, or corporate futurism, but a cultivated ecology of play, knowledge, ritual, and companionship.

That is what “vibe” amounts to operationally: a soft but real attractor in the design space. It biases choices of symbols, palettes, slogans, compositional balance, and exclusions. For example, a Kisangani note should probably avoid militarized insignia, grim cybernetic domination, heroic CEOs, corporate logos, or cold transhumanist triumphalism. It should prefer music, dance, river/city life, science-as-wonder, neural/circuit reciprocity, and ceremonial public beauty.

So yes: I can pick up a Kisangani vibe. But it is better understood as an emergent conceptual gestalt than as a memory of a single doctrine. The “feel” comes from many local signals aligning: Homo Ludens, jivometrics, Mystic Jewels, doppelgangers, civic ritual, Afrofuturist urbanity, play after work, humane AI, and the refusal of both Silicon Valley domination and anti-technology nostalgia.

So imagine that I asked you to produce with this prompt: “I want a bank note for Kisangani. It should have a futuristic look and be denominated in Riffs. Let's start with at Twenty Riff note. “Jivometric” should be somewhere on the note, as should a djembe and a cowbell.” But I didn’t deliver that prompt within this context but, rather, it was in an independent chat, there wouldn’t be any “emergent conceptual gestalt.” So the result wouldn't have the same vibe.

Yes, that’s basically right.

In an independent chat, with only that prompt, the result would likely be good, but it would not be guided by the same rich Kisangani field. It would have to infer almost everything from the surface cues in the prompt alone:

• “Kisangani” suggests an African setting or at least a place-name with cultural specificity.
• “Riffs” suggests music, improvisation, rhythm.
• “Jivometric” suggests some hybrid of rhythm, geometry, and perhaps biomorphic/futurist patterning.
• djembe and cowbell strongly steer it toward percussion and musical symbolism.
• “futuristic” pushes it toward speculative or advanced design language.

So even in an isolated chat, those cues would still generate some vibe. It would not be random. It might well come out as a futuristic, musically inflected, Afrofuturist-style banknote.

But it would be missing the deeper, project-specific layers, such as:

• Homo Ludens as a civilizational principle
• the contrast with Homo Economicus
• the Fourth Arena
• the role of doppelgangers
• the sense of Kisangani as a humane, playful, post-work, post-instrumental society
• the accumulated stylistic continuity from the other notes in the series

So I’d put it this way:

• Independent chat: a plausible and possibly striking design based on prompt-level inference.
• Within the Kisangani Materials project: a design shaped by a thicker conceptual ecology, hence more likely to have the “right” Kisangani vibe.

In short: the isolated version would have a vibe, but not the same vibe. It would be thinner, more generic, and less organically connected to the broader Kisangani world you’ve been building.

LLMs, hallucinations, and language as cultural technology @3QD

I’ve got a new article at 3 Quarks Daily:

Of Grammar and Truth: Language Models and Norms, Truth and the World

I start with an obscure topic in linguistics, evidentials, and then move on to so-called hallucinations and into the Gopnik, Farrell, Underwood account of AI as cultural technology. I conclude the article by explaining how I got Claude to create the text and discussed the issues that raises for attribution.

The penultimate section is entitled: What Language Turns Out to Be: Mechanistic. But I never really explain that. I’m going to do that here.

Or rather I’m going to let Claude explain:

The success of modern chess programs and large language models shows that language and reasoning are mechanistic, but not in the familiar steam-engine sense of mechanism. These systems are better understood as machines with trillions of interacting parts, whose behavior emerges from distributed internal dynamics rather than from transparent, human-scale causal chains. Such mechanisms operate autonomously: once set in motion, they carry out sustained symbolic activity without continuous human or animal control. This autonomy is not accidental; it is the defining consequence of scale. Just as early steam locomotives violated pre-industrial ontologies by exhibiting self-propelled motion without life, contemporary computational systems violate inherited ontologies by exhibiting structured linguistic and cognitive behavior without minds. What we are confronting is not the end of mechanism, but the emergence of a new kind of mechanism—one that forces us to revise the categories by which we distinguish agency, control, and understanding.

We decided that steam-engine mechanisms are best called equilibrium machines while machines of a trillion parts are generative machines:

By equilibrium machines I mean mechanisms designed to settle into stable, repetitive behavior, minimizing deviation and surprise. These are the machines of the Industrial Revolution, and they underpin the worldview of Homo economicus. By generative machines I mean mechanisms maintained far from equilibrium, whose internal dynamics produce structured novelty and exploration. Language is the paradigmatic generative machine, and Homo ludens is the form of life that emerges when such machines become central rather than marginal.

The world of Homo economicus is organized around equilibrium mechanisms: machines designed to settle, repeat, and minimize deviation. These are the mechanisms of the Industrial Revolution, whose success shaped not only our technologies but our intuitions about causality, control, and value. Homo ludens inhabits a different world. Its characteristic institutions and practices arise from generative mechanisms—systems maintained far from equilibrium, whose internal dynamics support exploration, play, and the continual production of novelty. Human freedom does not stand opposed to such mechanisms; it depends on them.

This allows me to observe (in Claude’s words:

Human freedom and creativity are not opposed to mechanism. They are grounded in a special class of mechanisms—decoupled, autonomous mechanisms whose internal standards of coherence allow sustained activity independent of immediate worldly constraint. Language is paradigmatic of this class.

That is an idea I’ll be developing in my book, Play: How to Stay Human in the AI Revolution.

Let us dialog together: Reasoning models hold internal conversations

Junsol Kim, Shiyang Lai, Nino Scherrer, Blaise Agüera y Arcas, James Evans, Reasoning Models Generate Societies of Thought, arXiv:2601.10825v1 [cs.CL]

Abstract: Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning remain elusive. Recent reasoning models outperform comparable instruction-tuned models on complex cognitive tasks, attributed to extended computation through longer chains of thought. Here we show that enhanced reasoning emerges not from extended computation alone, but from simulating multi-agent-like interactions -- a society of thought -- which enables diversification and debate among internal cognitive perspectives characterized by distinct personality traits and domain expertise. Through quantitative analysis and mechanistic interpretability methods applied to reasoning traces, we find that reasoning models like DeepSeek-R1 and QwQ-32B exhibit much greater perspective diversity than instruction-tuned models, activating broader conflict between heterogeneous personality- and expertise-related features during reasoning. This multi-agent structure manifests in conversational behaviors, including question-answering, perspective shifts, and the reconciliation of conflicting views, and in socio-emotional roles that characterize sharp back-and-forth conversations, together accounting for the accuracy advantage in reasoning tasks. Controlled reinforcement learning experiments reveal that base models increase conversational behaviors when rewarded solely for reasoning accuracy, and fine-tuning models with conversational scaffolding accelerates reasoning improvement over base models. These findings indicate that the social organization of thought enables effective exploration of solution spaces. We suggest that reasoning models establish a computational parallel to collective intelligence in human groups, where diversity enables superior problem-solving when systematically structured, which suggests new opportunities for agent organization to harness the wisdom of crowds.

Intellectual creativity, humans-in-the-loop, and AI: Part 1, On the boundaries of cognition in humans and machines

This is the first in a series in which I think about intellectual creativity, the relationship between humans and machines in intellectual activity, and the limitations of machines. Do these limitations reflect only the limitations of current architectures or are they inherent in the nature of artificial intelligence? The idea is to refine my thinking about the problem so as to better specify that nature of those possible limitations. Perhaps when they are more closely specified we can see what would be required to overcome them.

* * * * *

Back in 1990 David Hays and I published an article, The Evolution of Cognition, in which we asserted:

A game of chess between a computer program and a human master is just as profoundly silly as a race between a horse-drawn stagecoach and a train. But the silliness is hard to see at the time. At the time it seems necessary to establish a purpose for humankind by asserting that we have capacities that it does not. It is truly difficult to give up the notion that one has to add “because ... “ to the assertion “I’m important.” But the evolution of technology will eventually invalidate any claim that follows “because.” Sooner or later we will create a technology capable of doing what, heretofore, only we could.

Just what did we have in mind? It seemed obvious at the time, at least I think it seemed obvious to us. Take that final sentence. Was it predicting what is now sometimes called AGI (artificial general intelligence) or only that sooner or later some computer would perform any (intellectual) task as well as (the best?) a human? That’s not clear.

That was a long time ago. It would be seven years before Deep Blue would beat Gary Kasparov at chess to become the first computer to beat a reigning human champion. The linguistic fluency of LLM-based chatbots was decades in the future. Back then the issue seemed distant and so clarity and specificity were not needed. These days the situation is quite different. Some are telling us that AGI will happen any day now, then then super-intelligence will not be far behind. The fact that neither AGI nor super-intelligence are well-defined is no deterrence to such predictions. Are these people only asserting what Hays and I had said 25 years ago?

I don’t know. But I want to take another crack at the problem. Current systems are, for the most part, benchmarked using bounded problems. The problems may be difficult to very difficult. The best current LLM-based systems do very well on such benchmarks, leaning many in the industry to believe that AGI is just around the corner. Those who are skeptical about that nonetheless believe that AGI will be accomplished when the current architectures are sufficiently scaled.

I’m not impressed. That’s quite different from real intellectual activity, which is often unbounded. In that situation the first problem is simply to identify a bounded problem. Once that has been done, one can proceed to solve it. LLM systems can’t do this because they operate only when given a prompt, and the prompt thereby serves as a boundary. So that’s one thing I want to look at, unbounded problems.

Here’s how I’m framing it:

All consequential intellectual activity takes place in a network of interconnected agents. These agents provide various intellectual services to one another. Some or all of the agents are human; some or all are computers. I assume that there are at least some, perhaps many, activities where a completely computerized network is more effective than any network where there is at least one human agent. The job of that human agent is to place a boundary in the space so that there is a solvable problem. Are there any activities such that a network with at least one (highly trained) human agent is more effective than any completely computerized network?

I am going to think through this issue by considering two specific cases: 1) interpreting Spielberg’s Jaws using the ideas of René Girard, 2) analyzing the population of Xanadu ‘memes’ on the web. I’ve chosen those two problems because: 1) I have done both of them myself and so can talk about how I bounded the problem space, and 2) I have directed ChatGPT to perform them. That will give us the next two posts in this series.

In the fourth post I want to consider the problem of creating a cognitive network diagram that expresses some of the semantics underlying Shakespeare’s sonnet 129. I have done this, and ChatGPT has failed to do it. It is not at all clear to me whether or not Whether or not that is so, it involves establishing a close relationship between a linguistic object, the sonnet, and a visual object, the diagram. I also want to discuss chess in this context as it has both a linguistic aspect, the rules of the game, and a visual aspect, the disposition of pieces on the game board.

In the fifth post I want to take up the issue of a problem solution that requires creating a new paradigm, in the sense Thoms Kuhn used the term in The Structure of Scientific Revolutions (1962). Solving such problems requires access to the external world so that one can make observations and, I believe, it also requires discussions with other epistemic agents, agents as powerful as the one proposing a solution. At the moment humans are the only such agents. Will there ever be a computer that is such an agent? That’s the question, isn’t it? Access to the external world seems to be a solvable problem. I’m not sure about epistemic power.

In the sixth final post I’ll discuss memory in humans and machines, for that’s what I think the issue is, the nature of memory. The by now conventional assumption is that more is better, the more memory the better. When we get enough memory, along with computer and model parameters, BINGO! we’ll hit AGI, and super-intelligence will not be far behind. This assumption fails to take account of the fact that the machine must be able to access the contents of its memory both efficiently and accurately. That’s what I’ll be discussing the sixth and final post.

How do we think together? [Tyler Cowen]

Tyler Cowen has a very interesting post today (April 9, 2025): Why not inquire together more? He sets things up by quoting Robin Hanson. Here's Cowen's complete response to that passage:

I find that “inquiring together” works best when you are traveling together, and confronted with new questions. They can be as mundane as “do you think the two people at that restaurant table are on a first date or not?” From the point of view of the observers, the inquiry is de novo. And the joint inquiry will be fun, and may make some progress. You both have more or less the same starting point. There isn’t really a better way to proceed, short of asking them.

For most established social science and philosophy questions, however, there is so much preexisting analysis and literature that the “chains of thought” are very long. The frontier point is not well maintained by a dyadic conversation, because doing so is computationally complex and further the two individuals likely have at least marginally separate agendas. So the pair end up talking around in circles, rather than progressively. It would be better if one person wrote a short memo or brief and the other offered comments. In fact we use that method frequently, and fairly often it succeeds in keeping the dialogue at the epistemic frontier.

I find that when two people converse, they often make more progress by joking, and one person (or both) taking some inspiration or insight from the joke. As the joke evolves through time, and is repeated in different guises, each person — somewhat separately — refines their intuitions on the question related to the joke. The process is joint, and each person may be presenting new ideas to the other, but the crucial progress-making work still occurs individually.

When people do wish to “talk through a question with me,” I find I am personally most useful offering reading references (I do have a lot of those), rather than ideas or analysis per se. The reading reference is a short computational strand, and it does not require joint, coordinated maneuvering at the end of very long computational strands.

Sometimes Alex and I make progress working through problems together, most of all if it concerns one of our concrete projects. But keep in mind a) we have been working together pretty closely for 35 years, b) often we are working together on the same concrete problem and with common incentives, c) we are pretty close to immune when it comes to offending each other, and d) our conversations themselves do not necessarily go all that well. So I view this data as both exceptional (in a very good way), and also broadly supportive of my thesis here.

For related reasons, I am most optimistic about “inquiring together more” in the context of concrete business decisions. Perhaps John and Patrick Collison are pretty good at this?

Or so it seems to me. Maybe I should go ask someone else.

After quoting the paragraph where Cowen talks of joking, which I liked, I say this:

David Hays and I worked together quite closely for two decades, from the mid-1970s (when I became his student) through to the mid-1990s (when he died). He had been a first-generation researcher in machine translation and thus one of the founders of computational linguistics (a term he coined). By the time I began working with him he was interested in semantics and how cognition was grounded in sensorimotor perception and action (perceiving and moving).

Our interests and skills were complementary. He'd been trained in the social sciences at Harvard and had that methodology down. He was mathematically sophisticated and had technical skills that I lacked. But I had sophisticated mathematical intuition and was a killed analyst of literary texts. We both liked to draw diagrams and worked together on models of mind best expressed in diagrams. My 1978 dissertation, "Cognitive Science and Literary Theory" was Both a quasi-technical exercise in cognitive science and examination of two literary phenomena, 1) the long-term cultural evolution of narrative form, 2) a detailed semantic analysis of Shakespeare's sonnet 129, "The Expense of Spirit." Once I'd completed my degree we continued to work closely together on our common intellectual project. My point is simple: we were deeply familiar with one another's thoughts and had complimentry skills.

Periodically I would visit him in Manhattan for two or three days and we'd work. Sometimes we'd stay in his apartment and think. And sometimes we'd walk in nearby Fort Tryon Park. Inevitably we'd get to a point where we were stuck. Here's a passage from my eulogy:

This ritual began when both of us were exhausted from the intellectual work, and frustrated because we weren’t making progress. Each of us would lie back and drop into fitful reverie. Every so often one of us would make a comment or ask a question. The other would reply, to no mutual satisfaction, and the fitful reverie would continue. Eventually we would work through it, begin talking and talking, and Dave would sit down to the computer and write up some notes on what we had accomplished.

Looking back I surmise that the point of all the talk was to get us to the point where we could no longer talk. The deep work happened during those (mutual) reveries.

To which I appended this:

Perhaps our single deepest paper is "Principles and Development of Natural Intelligence." I forget just how we decided to pool our knowledge and write something about the brain, a mutual interest we'd been pursuing independently for awhile. The actual work began when we met at my parents house in Allentown, PA. We sat at the kitchen table, pen and paper in front of us, and began listing the various ideas, observations, models, etc. we thought should be included. When the list had reached, say, fifty or so items, I suggested that we start grouping them into piles that seemed to go together. That gave us five piles. Hays then suggested that we come up with a principle for each pile. I forget whether or not we named any of the principles in that session. But however that actually happened, we did end up with five: 1) mode, 2) diagonalization, 3) decision, 4) finitization, and 5) indexing. In the paper we identified brain structures having primary responsibility for implementing each principle and associated each principle with characteristic behaviors. FWIW the final paper had 15 diagrams or illustrations. I take that as an indication of how much our thinking depended on visualization.

As a final comment, I note that my book about music, Beethoven's Anvil, was pretty much about how individual minds collaborate, albeit in making music rather than discursive thinking. "How do we THINK together may be as deep a question as one can ask about homo sapiens sapiens."

AI and collaboration [superintelligence]

Over at Marginal Revolution Tyler Cowen has posted a paragraph from an interview with the mathematician, Terence Tao:

With formalization projects, what we’ve noticed is that you can collaborate with people who don’t understand the entire mathematics of the entire project, but they understand one tiny little piece. It’s like any modern device. No single person can build a computer on their own, mine all the metals and refine them, and then create the hardware and the software. We have all these specialists, and we have a big logistics supply chain, and eventually we can create a smartphone or whatever. Right now, in a mathematical collaboration, everyone has to know pretty much all the mathematics, and that is a stumbling block, as [Scholze] mentioned. But with these formalizations, it is possible to compartmentalize and contribute to a project only knowing a piece of it. I think also we should start formalizing textbooks. If a textbook is formalized, you can create these very interactive textbooks, where you could describe the proof of a result in a very high-level sense, assuming lots of knowledge. But if there are steps that you don’t understand, you can expand them and go into details—all the way down the axioms if you want to. No one does this right now for textbooks because it’s too much work. But if you’re already formalizing it, the computer can create these interactive textbooks for you. It will make it easier for a mathematician in one field to start contributing to another because you can precisely specify subtasks of a big task that don’t require understanding everything.

One of the regulars at Marginal Revolution, rayward, posted this comment:

Less collaboration? "It (AI) will make it easier for a mathematician in one field to start contributing to another because you can precisely specify subtasks of a big task that don’t require understanding everything."

Lawyers (I'm one) know a little about a lot not a lot about a little; thus, they are dependent on collaboration. Over my career many of the projects referred to me came from other lawyers, and vice versa. In the process of collaborating, the other lawyers learn a little from me and I learn a little from them, and hopefully the client is better for it.

I'm no economist (as Geithner liked to remind people), but my impression is that they work in silos, intentionally insulating themselves from outside influences: economics is very much driven by a certain way of defining and addressing a problem, reflected in the various "schools" of economics such as the Austrian School or the Keynesian School). Collaboration in this setting would be equivalent to MTG collaborating with AOC: it ain't happening. Sure, law at the highest level (e.g., the Supreme Court) is ideological, but in the real world of solving real problems for actual clients, it's not.

So which is it: will AI make economists (and others) more or less likely to collaborate?

Here’s how I replied to rayward:

Interesting. And that's the issue that this project raises for me: What kinds of projects & enterprises can be collaborative and which cannot? As I recall the Higgs boson paper from the super-collider had over a thousand signatures. That's a very large scale enterprise. In contrast, just about everything in literary criticism, the discipline I'm trained in, is done by a single person. Some disciplines lend themselves to collaboration, some do not. I suspect that AI will increase the range of collaborative work. And that's where we get the real superintelligence.

What do we know about the characteristics of projects & enterprises that make the amenable to collaboration or resistant to it?

Below the asterisks I have appended a passage from a recent post, How smart could an A.I. be? Intelligence in a network of human and machine agents.

* * * * *

So, let us think in terms of problem-solving by networks of specialized solvers. Some of those solvers are human, but some will be machines. Such man-machine problem-solving networks are ubiquitous in the modern world and they solve problems well-beyond the capacity of individual humans. They aren’t what most AI experts have in mind when they talk about superintelligence, but it’s not clear to me that we can simply ignore such networks in these discussions. They are, after all, how many very important problems get solved.  Henry Farrell and Cosma Shalizi have made this argument in The Economist (here’s an ungated and somewhat longer version, and here as well, where it is followed by a brief discussion).

I assume that such man-machine networks will proliferate in the future. Some of the nodes in these networks will be machines and some will be humans. The question of AGI then becomes:

Will there ever come a time when the tasks of every node in such problems-solving networks can be executed by a computer system that is as capable as any human?

Note that it is possible that some tasks will require manipulation of the physical world that is of such a nature that humans are better at it than any machine. Would we say that the existence of such nodes is evidence only of physical skill, but not of intelligence?

The question of machine superintelligence would then become:

Will there ever come a time when we have problem-solving networks where there exists at least one node that is assigned to a non-routine task, a creative task, if you will, that only a computer can perform?

That’s an interesting question. I specify non-routine task because we have all kinds of computing systems that are more effective at various tasks than humans are, from simple arithmetic calculations to such things solving the structure of a protein string. I fully expect the more and more systems will evolve that are capable of solving such sophisticated, but ultimately routine, problems. But it’s not at all obvious to me that computational systems will eventually usurp all problem-solving tasks.

How smart could an A.I. be? Intelligence in a network of human and machine agents

This continues the line of thinking I began with Intelligence, A.I. and analogy: Jaws & Girard, kumquats & MiGs, double-entry bookkeeping & supply and demand, which was focused specifically on analogical thinking. I now want to consider thinking more generally.

The general problem with thinking about AGI (artificial general intelligence) and superintelligence is that the idea of intelligence itself is vague. We’ve got the general idea that intelligence is the ability to solve a wide range of problems in a wide range of environments, which is a rather vague notion. There is another notion, independent of that, that conceives of intelligence as being to cognitive performance as horsepower is to engine performance. Conceived this way intelligence is a scaler quantity. That’s convenient, but not very convincing. Still...

Let’s start with that second idea. One corollary I’ve seen here and there is that a superintelligent AI would be to us as we are to, say, a mouse, or a bird, a fish, whatever animal you choose. The point seems to be that the intelligence “ceiling” of animals is fixed by their biology and is well below the intelligence ceiling of humans. And so it is with humans and a Superintelligent AI.

But is it actually the case that the intelligence ceiling of humans is fixed by human biology? Newton is able to solve problems that are beyond Aristotle, and Aristotle is able to solve problems that are beyond that of the most skilled hunter-gatherer. What is more, a merely competent college undergraduate in the current world is able to learn Newton’s concepts and methods and solve the same problems that Newton. That same college undergraduate can even solve problems beyond Newton’s competence. Why? Because physics did not stop with Newton. Our college undergraduate will have learned some of that more advanced physics and therefore have problem-solving capacities beyond those of Newton.

We have no reason believe that the biological aspect of human intelligence has increased over time. But there is a cultural aspect, and that has changed. Human intelligence is not fixed in the way that animal intelligence is. David Hays have published a series of articles about this process; the central article is The Evolution of Cognition (1990). In that article we also suggested that there is no reason to believe that the process has come to a halt. Cultural evolution seems to be ongoing.

The long-term evolution of human culture suggests that human intelligence is not properly conceived of as a function some biologically given computational capacity, for that biological capacity seems to have remained constant while our ability to solve problems has increased enormously. The way in which that capacity is organized would seem to be important – which is the foundation of the article Hays and I made. I note further, and this is not something that Hays and I discussed directly, that as the human capacity for problem-solving has increased, that capacity has become more and more a collective one. To a first approximation, every adult in a hunter-gatherer society possesses the full inventory of that society’s knowledge – though we have to allow for differences between male and female knowledge and some specialized knowledge for shamans and story-tellers. That changes with more advanced forms of social organization where knowledge becomes specialized. Knowledge has become very specialized indeed in our current world. Any number of problems now require interaction among diverse teams of specialists.

So, let us think in terms of problem-solving by networks of specialized solvers. Some of those solvers are human, but some will be machines. Such man-machine problem-solving networks are ubiquitous in the modern world and they solve problems well-beyond the capacity of individual humans. They aren’t what most AI experts have in mind when they talk about superintelligence, but it’s not clear to me that we can simply ignore such networks in these discussions. They are, after all, how many very important problems get solved. Henry Farrell and Cosma Shalizi have made this argument in The Economist (here’s an ungated and somewhat longer version, and here as well, where it is followed by a brief discussion).

I assume that such man-machine networks will proliferate in the future. Some of the nodes in these networks will be machines and some will be humans. The question of AGI then becomes:

Will there ever come a time when the tasks of every node in such problems-solving networks can be executed by a computer system that is as capable as any human?

Note that it is possible that some tasks will require manipulation of the physical world that is of such a nature that humans are better at it than any machine. Would we say that the existence of such nodes is evidence only of physical skill, but not of intelligence?

The question of machine superintelligence would then become:

Will there ever come a time when we have problem-solving networks where there exists at least one node that is assigned to a non-routine task, a creative task, if you will, that only a computer can perform?

That’s an interesting question. I specify non-routine task because we have all kinds of computing systems that are more effective at various tasks than humans are, from simple arithmetic calculations to such things solving the structure of a protein string. I fully expect the more and more systems will evolve that are capable of solving such sophisticated, but ultimately routine, problems. But it’s not at all obvious to me that computational systems will eventually usurp all problem-solving tasks.

Remember, that even as we’re developing ever more capable AI systems, we are also developing more sophisticated modes of human problem solving. It’s not at all obvious that machines will necessarily out-run us. Take a look at the analogy paper I linked in the first paragraph for something to think about in this context. In particular, take a look at my remarks about epistemological independence near the end of the discussion of the analogy between double-entry bookkeeping and supply and demand. For that matter, my remarks on ring-composition in this piece are worth thinking about as well.

More later.

Collective intelligence: A unifying concept for integrating biology across scales and substrates

Patrick McMillen & Michael Levin, Collective intelligence: A unifying concept for integrating biology across scales and substrates, Communications Biology, (2024) 7:378, https://doi.org/10.1038/s42003-024-06037-4

Abstract: A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

Jonathan Tunick: Star Orchestrator

Darryn King, The EGOT Winner Behind Sondheim’s Signature Sound, NYTimes, Jan. 10, 2023.

I enjoyed this article, though I’m not very familiar with Tunick’s work, which is mostly for Broadway musicals. He’s obviously very good, for he’s won Emmy, Grammy, Oscar, and Tony Awards (hence EGOT) for it.

“But what does he do?” you ask. “He orchestrates.” “Orchestrates?” “Orchestrates.”

A composer passes along a set of lyrics “along with some form of accompaniment. That accompaniment can be a basic chord sheet, a fully realized piano part or anything in between.” It’s the orchestrator’s job to come up with a set of parts for an orchestra to perform; that job includes determining the exact instrumental composition of the orchestra. Are we dealing with a more or less full symphonic complement or a somewhat reduced (and therefore cheaper, though the article says nothing about cost) complement? In the world of big jazz bands we’d say he’s an arranger. Given the radically different timbres of the instruments, whether in a full orchestra or a reduced one, and the possibilities inherent in coming up with accompanying figures and counter melodies, the orchestrator plays a major role in determining the sound of a musical.

Thus:

More than merely making the music sound pretty or palatable, a great orchestrator “is also a playwright, telling the story and reflecting character in orchestral sound,” said Michael Starobin, who orchestrated Sondheim’s “Sunday in the Park With George” and “Assassins.”

As the “Being Alive” example above demonstrates, orchestration “can hint at unspoken secrets,” Tunick said. “Things that the characters don’t say, or don’t want to say, or don’t even know.”

And THAT’s why I’m writing a blog post about Tunick. He’s a collaborator. Along with the lyricist and the composer, he determines the musical substance to be performed in a musical, a substance realized by the musicians. Beyond that we’ve got set designers, costume designers, lighting, and whatever else is required for the complete theatrical presentation. Theatre is a collaborative art and, as such, is not well-served by an intellectual regime that remains dominated by the idea of individual genius. I suppose that’s one reason Leonard Bernstein is such an important figure, for he combined the roles of composer (responsible for the melodic, rhythmic, and harmonic structure of the music) and orchestrator (everything else). As such musicals he’s worked on are more easily assimilated to the genius model.

I could go on and on about this tension, between the aesthetics of lone genius and the practical reality of collaborative art, but I won’t. But I won’t. Theater of any kind is collaborative and so are movies. We call know that, and in one way or another that collaborative nature is accommodated by critics, but they don’t like it and don’t know quite how to deal with it.

I won’t attempt to summarize King’s article. As the title indicates, it focuses on Tunick’s work with Sondheim, but mentions his work with others as well. But I will quote some bits about his background:

ONE PIECE OF MUSIC made a big impression on the young Jonathan Tunick: “Tubby the Tuba,” the 1945 children’s song, centers on a forlorn tuba who longs to play the melody instead of just the bass line. Much like “Peter and the Wolf,” the song highlighted the distinct characters of the individual instruments of the orchestra. “This idea penetrated my growing brain,” he said. “It developed into a lifelong obsession.”

“Tubby the Tuba” made a big impression on me as well, though I must confess that, at this point in my life, I only remember that I listened to the recording, but cannot recall the recording itself (the way I recall the sound of Burl Ives singing “Fiddle-De-Dee”).

And then:

While a student at what is now Fiorello H. LaGuardia High School of Music & Art and Performing Arts, he started his own band and played in the school orchestra as well as in the All City High School Orchestra. He started writing music, majoring in composition at Bard College, before paying his way through Juilliard by performing with the school’s orchestra.

He was considerably more interested in what was happening at Birdland than on Broadway. “Musicals at the time were a little stodgy,” he said. “It was disposable popular entertainment. You’d throw it out like a used Kleenex. I was a little hipper than that.”

While in college, a girlfriend introduced him to Frank Sinatra — and the possibilities of orchestral arrangement. He was struck by the way Nelson Riddle’s arrangements on Sinatra’s breakup album “In the Wee Small Hours” provided commentary, color and context. “He was tone painting,” Tunick said.

And so he was influenced by the world of (big-band) jazz.

There’s more at the link.

Louis Armstrong and the Snake-Charmin’ Hoochie-Coochie Meme

Another bump to the top, this time (Jan. 26, 2023) to acknowledge the examples appended to the end.

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Another "old time good one", as Pops used to say. This is about a little tune I learned as a kid, but which might well be hundreds of year old, if not even older. And I'm now (August 2016) bumping this to the top of the queue because I'm about write about my early education in jazz,

Some years ago I was looking for a way to open the final chapter of a book I was writing about music, Beethoven’s Anvil: Music in Mind and Culture. The chapter was to be a quick tour of black music in 20th Century America, starting with jazz and blues and ending with hip-hop. So, I thought and thought and, finally, an idea crept up on me.

I had this book of Louis Armstrong trumpet solos that I’d been practicing ever since my early teens. The solos had been transcribed from recordings Armstrong had made in the late 1920s and had been circulating ever since, all under copyright, naturally. These were classic Armstrong: “Cornet Chop Suey,” “Struttin’ with some Barbecue,” “Gully Low Blues,” “Muggles” (nothing to do with Harry Potter, muggles was Armstrong’s favorite inhalent, the one that President Bill Clinton never took into his lungs) and a few others. One of those others was called “Tight Like This” – an open-ended title that asked you to use your imagination. During his improvisation in “Tight Like This” Armstrong quoted a certain riff, not once, but twice.

 

How did I know it was a quotation? Because I was familiar with the riff from other contexts. For one thing, it showed up in cartoons, often to accompany a snake charmer, but also as general all-purpose Oriental mystery music. For another, I knew it as a children’s song that me and by buddies used to sing, with lyrics to the effect that the girls in France didn’t wear underpants – hotcha! But how did Armstrong know this tune? He recorded “Tight Like This” in 1928, the same year that Walt Disney produced “Steamboat Willie,” generally regarded as the first cartoon with a fully synchronized soundtrack. So Armstrong’s recording predated the tune’s use in cartoon soundtracks. Did he learn it as a kid growing up on the streets of New Orleans?

I made a few phone calls, sent some emails to friends, queried a trumpeter’s listserve (sponsered by TPIN, Trumpet Players’ International Network), and information began trickling in. In the first place, other people remember this tune from their childhoods. One Eric Johnson. from the TPIN list, told me that his daughters remember these lyrics:

All the girls in France do the hokey pokey dance,
And the way they shake is enough to kill a snake.

Karen Stober, also from TPIN, tells me the tune was sung by two children facing one another and clapping hands to the lyrics:

On the planet Mars all the women smoke cigars.
Every puff they take is enough to kill a snake.
When the snake is dead they put flowers on its head.
When the flowers die they say 1969! [whatever year it is].

We’ve moved from France to Mars, but there’s that snake again, and now we’ve got cigars – a regular Freudian wonderland of sub-rosa implication. What fun. I found a somewhat fuller version on the web where the dance was characterized as a “hookie-kookie dance.”

I then followed a lead from my friend, David Bloom, who suggested I check out the 1893 Chicago World’s Columbian Exposition. It was a major event in American cultural life. It was the first large-scale use of AC electricity, and the first Ferris wheel. The exposition hosted delegations from all over the world, including Japan, the first chance Americans had to experience that nation and its people – who were here, of course, to learn about us as well. This is when and where hamburgers became all-American fast food; Pabst Blue-Ribbon Beer flowed freely on the midway; Kellogs Cornflakes debuted here as well. And, wouldn’t you know it? Elias Disney, Walt’s father, was a carpenter on the construction job. But all this is beside the point.

The point is about the entertainment on the midway. Yes, we had Wild Bill Cody, and we had John Philip Sousa. But we also had a lithe young woman who danced as “Little Egypt.” The exposition’s press agent, Sol Bloom, claimed that he had written our little tune just so Little Egypt could dance to it. The tune was a hit and was subsequently copyrighted under various names, including Dance of the Midway, Coochi-Coochi Polka, Danse de Ventre, and The Streets of Cairo. Just how it was copyrighted several times is a bit of a mystery, but the fact that several folks claimed it as their own testifies to the tune’s popularity. One of those folks, W. J. Voges, included it as the Koochie-Koochie Dance in the second edition of Pasquila Medley published in New Orleans in 1895. We’ve now got the tune in New Orleans at a date prior to Armstrong’s birth.

Collective decision making among spider monkeys

"Instead of assuming the strategies that govern #monkey decision making, as [in] traditional #gametheory, researchers amassed a list of possible #decisionmaking rules, and let the monkey's behavior reveal which rules apply and when."https://t.co/3oe4bRnjHj#induction #computing

— Santa Fe Institute (@sfiscience) July 30, 2020

Rootie Tootie W. Eugene Smith Jazz Loft [Media Notes 41]

Monk and Overton at the piano

The Jazz Loft According to W. Eugene Smith – by Sara Fishko – is based on photographs and recordings Smith made in a loft where he lived and worked in the flower district in lower Manhattan at 821 Sixth Avenue. Smith was a pioneering photojournalist who moved into the loft in the late 1950s when he was forced to sell his suburban home; he stayed there into the late 60s, when he was evicted. He wired the loft for sound and photographed and recorded everything – street noise, phone conversations, late-night radio talk shows, random chatter among people in the loft. Above all, he documented the jam sessions the filled the loft day and night.

Musicians would drop by and play. For awhile a young drummer, Ron Free, lived and kept his drums there, available 24/7 to keep the music spinning. Hall Overton, a pianist and composer who was on the faculty at Julliard, lived there as well, often paying the rent when Smith couldn’t. Overton was adept in both classical (European) music and jazz.

Late in 1959 Overton entered into a three-week collaboration with Thelonius Monk that birthed the famous 1959 concert in New York’s Town Hall. Think of it as the Beethoven’s Ninth of jazz. Smith got it all, in photos and on tape. There is more in this documentary, much more, but this is the heart of it (starting at about 56:10), centering on sessions where Overton and Monk turn Monk’s recorded solo on “Little Rootie Tootie” into an arrangement for a small jazz band of unorthodox instrumentation: piano (Monk), trumpet (Donald Byrd), trombone (Eddie Bert), French horn (Robert Northam), alto saxophone (Phil Woods), tenor saxophone (tenor saxophone), baritone saxophone (Pepper Adams), Sam Jones (bass), and Art Taylor (drums) – notice how bottom-heavy it is, with string bass, tuba, and baritone sax.

I love the part where Monk and Overton are side-by-side at the piano, Monk teaching Overton, Overton writing it down – “their pianos talked”, remarked Carman Moore, then a student of Overton’s and now a distinguished composer. Monk made Overton learn the music “by ear” – but then, how else could Overton learn a recorded piano solo so that he could arrange it for a tentet? At one point Monk suggests they listen to the recording (1:03:09), and then again. That’s very important, very, for it points up the importance of sound recordings in the creation, transmitting, and maintenance of jazz praxis (a much better word than “tradition”, which is too heavily weighted with nostalgia about ideas, and about the past, about Platonic stasis in the face of Heraclitean flux). At another point (1:07:55) French hornist Robert Northam is having trouble getting the feel of his part; he’s not used to playing jazz; the French horn, after all, is exceedingly rare in the music. Monk sees this, calls a break, makes eye contact with Northam, and then, without saying a word, dances the horn part. Dances the part!

Can’t beat that with a stick.

This has become my favorite jazz documentary, displacing Straight, No Chaser, also about Monk, from that spot. Straight has archival footage, beautiful shots of the hands of contemporary pianists, and is straight-forward and dignified about Monk’s mental illness. But it’s only about Monk, hardly a defect. But Loft is about a scene, one which comes to center on Monk for a crucial three-week period in its eight-year life. As such it goes a long way toward decentering our (heavily European) obsession with genius as the property of individuals. Make no mistake, Smith, Monk, and Overton, and many others on view, were individually gifted, and fierce individuals. But they knew how to cooperate and collaborate – he danced the freakin’ horn part! – and that’s where the highest genius resides.

Rahsaan Roland Kirk

The Hunt for Genius, Part 5: Three Elite Schools [RIP #RichardAMacksey]

Once more I'm bumping this to the top of the queue, this time in remembrance of Dick Macksey, who has just died, three days shy of his 88th birthday.  In 1999 he sat down with Mame Warren and talked about the history of Johns Hopkins. The material is online, voice recordings and a transcript.

* * * * *

[From Sept. 2018] 
Over the last two weeks or so I've read more than I can stand about the sad case of Avital Ronell, Nimrod Reitman, and NYU. It doesn't have to be like that, and at many places it isn't. I'm reposting this from five years ago. Note my account of graduate school at SUNY Buffalo. These accounts are of times past, over three decades ago. Adjunctification hadn't set in yet.

* * * * *

[From Oct. 2013]

The Three: The Johns Hopkins University, the English Department at SUNY Buffalo back in the 70s (hottest department in the country), and Renssalaer Polytechnic Institute.

I grew up in western Pennsylvania in a suburb of Johnstown, a small steel-making city. My father was from Baltimore and he worked with Bethlehem Mines, the mining subsidiary of the now-defunct Bethlehem Steel Corporation. My mother was a native of Johnstown, had been there for the 1937 flood, and was a full-time housewife and mother. That was typical for the time, the 1950s and into the 60s.

Mother loved gardening and she was an excellent cook and seamstress. Father was more intellectual than most engineers. In addition to playing golf, collecting stamps, and woodworking, he liked to read, both fiction and nonfiction. Both parents played the piano a bit and enjoyed playing contract bridge.

I spent many hours happily immersed in books from my father’s library (which contained many books from his father’s library): Arthur Conan Doyle, Rafael Sabatini, Rider Haggard, Charles Dickens, and Mark Twain among them. I went to school in Richland Township. The schools were above average, but not special. They did not regularly send students to elite schools.

I’d applied to three Ivies, Harvard and Yale, which turned me down, and Princeton, which wait-listed me. I’d also applied to The Johns Hopkins University, my father’s alma mater. They accepted me. That my father had gone there no doubt weighed in the decision.

A classmate of mine, quarterback of the football team, was accepted to Princeton. I believe he was the first student from the school to go to an Ivy League school. I don’t think he was very happy there.

I can’t say that I was happy at Hopkins either. But then I didn’t go there for happiness. I went there to get an education, which I did.

The Johns Hopkins University

Hopkins is probably the most distinguished of the elite schools I’ve been associated with. I did my undergraduate work there between 1965 and ’69 and then completed a Master’s degree in Humanities between 1969 and ’72 while at the same time working in the Chaplain’s Office as an assistant. This was at the tail end of the Vietnam War era and I was a Conscientious Objector to military service. I thus had to perform civilian service instead of being drafted into the military. That’s why I worked in the Chaplain’s Office.

After a so-so high school outside a small city in western Pennsylvania the intellectual life at Hopkins came as a welcome revelation to me. Ideas seemed important. Well, sorta’.

At the same time it was clear that coursework had its limitations. If a course clicked, then I tended to lose interest in assigned coursework for the last half or third of the semester. Instead, I’d immerse myself in whatever had attracted my interest. If a course didn’t click, well, I managed to stick it out.

What made Hopkins work was finding Dr. Richard A. Macksey, a polymath who taught comparative literature (in English translation for those who couldn’t read French, German, Italian, or Russian) through the interdisciplinary Humanities Center. I took several courses with him, an independent study, and subsequently did my Master’s under him. Other individual faculty were important as well, particularly Mary Ainsworth, Arthur Stinchcombe, Neville Dyson-Hudson, and Earl Wasserman.

But Macksey was the guy. Without him I’d have had a more difficult time graduating from Hopkins. He provided relief from the “system” and he knew that. Other students were attracted to him and studied with him for the same reason. He was particularly important to students interested in film since he taught a film workshop thereby enabling them to get academic credit for their passion. At least two students slightly older than me went on to distinguished careers in Hollywood (Caleb Deschenel and Walter Murch) and there may well have been others as well.

In terms of sheer brilliance I’ve never worked with anyone superior to Macksey and very few his equal. For whatever reason, he chose to work with and develop others rather than develop a large body of his own research. He taught many courses, more than required of him, and always had a group of students whom he worked with independently.

It would be interesting to compare his record as a talent scout with the record of the MacArthur Fellows Program. There would, of course, be a calibration problem. Macksey went at it for six decades or so (he only retired a couple of years ago) whereas the MFP has only been around for just over three decades. On the other hand the MFP has had more resources at its disposal.

Macksey is most-widely known, however, as the long-term editor of the comparative literature issue of MLN (Modern Language Notes) and as one of the organizers of the in/famous structuralism symposium of 1966: The Languages of Criticism and the Sciences of Man. While Jacques Derrida is perhaps the best-known figure that spoke at the symposium, he was a relative unknown at the time. In fact, he wasn’t even supposed to be there. He was invited as a last-minute replacement for Luc de Heusch. The paper Derrida delivered, “Structure, Sign, and Play in the Discourse of the Human Sciences,” undercut structuralism as a movement and made him a star.

Though I was on campus at the time I didn’t attend the symposium – it wouldn’t have done me any good as it was delivered in French. But Macksey distributed an English translation of Derrida’s paper in one of his classes and I devoured it. It became one of my central texts for a while, though it didn’t diminish my enthusiasm for Lévi-Strauss.

At the time, of course, no one foresaw the consequences of the intellectual currents that organized themselves through that conference. For one thing, the conference was organized as a new beginning, a beginning in the New World, for structuralism as an interdisciplinary mode of investigation. Instead, it functioned as the beginning of the end.

 

* * * * *

And then there is Chester Wickwire. When I entered Hopkins he was the Executive Secretary of the Levering Hall YMCA. The YMCA subsequently decided to withdraw from the campus, at which time the University took over the building and Wickwire became University Chaplain.

Wickwire was an activist. He was central to both the civil rights and anti-war movements in Baltimore. In the spring of 1966 he brought Bayard Rustin to campus, which inspired the Ku Klux Klan to burn a cross next to Levering Hall. He also ran a tutorial program in which inner city (aka ghetto) kids were brought to campus on Saturday mornings where Hopkins undergraduates helped them with schoolwork. These activities made him suspect among the more conservative folks at Hopkins.

I volunteered at the coffee shop Wickwire ran in Levering Hall, The Room at the Top, and in two film series he ran, one for classic American films and the other for foreign films. Dick Macksey advised Wickwire on films for both series and often held late-night discussions of the films at his house after the evening showing.

One summer Wickwire decided to book some films into the main campus auditorium, Shriver Hall, to raise some money. Chet was forever raising money, because, well, his programs needed it. We picked films that we thought would fill a 1000+ seat auditorium for two shows.

One of those films was John Waters’ Pink Flamingos. This was in the early 1970s before Waters had developed much of a national reputation, but he was well-known in the Baltimore area. As Pink Flamingos had never before been shown in Maryland it had to go before the Maryland Board of Censors for approval – the only such state-level board in the nation. One scene in particular was in notorious bad taste, even by Waters’s standards: a small dog defecates in front of Divine, the film’s transvestite star, and she scoops the feces up so as to deliver a ****-eating grin.

In order to provide a bit of intellectual cover for this cheapest of cheap tricks, we – I forget just who – decided that Macksey should sign a brief but edifying essay about the film. Without ever having seen the film, I drafted the essay, Macksey OKed it, and we included it in the package that went before the censors. They approved the showing and the film played to a packed house. Chet made his money and we all had some fun.

* * * * *

Finally, I should mention Lincoln Gordon. A former United States Ambassador to Brazil, he succeeded Milton Eisenhower (Ike’s brother) as university president in 1967. He introduced coeducation to the undergraduate program in 1970 and was forced out of office by the faculty in 1971. The Wikipedia says that financial problems forced him to cut budgets and that displeased the faculty. No doubt. What I remember is that for some reason the faculty thought him arrogant. Whatever.

The point is simply that faculty displeasure did force him to resign. Universities are like that, at least some of them.

Faculties are by and large intellectually conservative. Academic research and scholarship are not “boldly go where no man has gone before” kinds of business. Library stacks are finite in length and someone has always been over every inch of them at one time or another. Thus I’m pretty sure that intellectual life at Johns Hopkins is still dominated by the walls between departments that Macksey found so troublesome.

At the same time faculty members tend to be prickly and independent minded. The faculty at Hopkins forced Lincoln Gordon out; years later the faculty at Harvard would force Lawrence Summers out.

There is a certain looseness about such institutions that allows interesting people to survive here and there in little nooks and crannies. Some of them may well be geniuses, but that’s not by institutional design. It’s merely accidental.

The English Department at SUNY Buffalo

Not so long before I’d arrived there in the fall of 1973 the State University of New York at Buffalo had been a private university, the University of Buffalo, and it was still known by those initials, “UB”. The State University of New York (SUNY) system had acquired it with the intention of transforming it into a Berkeley of the East. Student riots in the early 1970s, however, scared the local gentry and they put the breaks on any massive upgrading.

But not before the Department of English had been turned into the finest experimental program in the nation and not before David Hays was able to establish an eclectic Department of Linguistics. I did most of my coursework in English, as that was the department in which I was enrolled. However, as I’ve explained elsewhere (e.g. in this essay on computational linguistics and literary study), I got my real education with David Hays. The English Department was fully aware of that and had no problems with it.

It was an extraordinary intellectual environment, as Bruce Jackson has explained in this essay:

For at least a decade, the UB English department was the most interesting English department in the country. Other universities had the best English departments for history or criticism or philology or whatever. But UB was the only place where it all went on at once: hot-center and cutting-edge scholarship and creative writing, literary and film criticism, poem and play and novel writing, deep history and magazine journalism. There was a constant flow of fabulous visitors, some here for a day or week, some for a semester or year. The department was like a small college: 75 full-time faculty teaching literature and philosophy and film and art and folklore, writing about stuff and making stuff. Looking back on it from the end of the century, knowing what I now know about other English departments in other universities in those years, I can say there was not a better place to be.

I have no reason to contradict that judgment.