Bill Benzon

The Story of My Intellectual Life

In the early 1970s I discovered that “Kubla Khan” had a rich, marvelous, and fantastically symmetrical structure. I'd found myself intellectually. I knew what I was doing. I had a specific intellectual mission: to find the mechanisms behind “Kubla Khan.” As defined, that mission failed, and still has not been achieved some 40 odd years later.

It's like this: If you set out to hitch rides from New York City to, say, Los Angeles, and don't make it, well then your hitch-hike adventure is a failure. But if you end up on Mars instead, just what kind of failure is that? Yeah, you’re lost. Really really lost. But you’re lost on Mars! How cool is that!

Of course, it might not actually be Mars. It might just be an abandoned set on a studio back lot.


That's a bit metaphorical. Let's just say I've read and thought about a lot of things having to do with the brain, mind, and culture, and published about them as well. I've written a bunch of academic articles and two general trade books, Visualization: The Second Computer Revolution (Harry Abrams1989), co-authored with Richard Friedhoff, and Beethoven's Anvil: Music in Mind and Culture (Basic Books 2001). Here's what I say about myself at my blog, New Savanna. I've got a conventional CV at I've also written a lot of stuff that I've not published in a conventional venue. I think of them as working papers. I've got them all at Some of my best – certainly my most recent – stuff is there.


Exploring the Digital Wildnerness

Wiki Contributions


Is accessing the visual cartesian theater physically different from accessing the visual cortex? Granted, there's a lot of visual cortex, and different regions seem to have different functions. Is the visual cartesian theater some specific region of visual cortex?

I'm not sure what your question about ordering in sensory areas is about.

As for backprop, that gets the distribution done, but that's only part of the problem. In LLMs, for example, it seems that syntactic information is handled in the first few layers of the model. Given the way texts are structured, it makes sense that sentence-level information should be segregated from information about collections of sentences. That's the kind of structure I'm talking about. Sure, backprop is responsible for those layers, but it's responsible for all the other layers as well. Why do we seem to have different kinds of information in different layers at all? That's what interests me.

Actually, it just makes sense to me that that is the case. Given that it is, what is located where? As for why things are segregated by location, that does need an answer, doesn't it. Is that what you were asking?

Finally, here's an idea I've been playing around with for a long time: Neural Recognizers: Some [old] notes based on a TV tube metaphor [perceptual contact with the world].

I like certainly the idea of induction heads. Why? Because I've done things with ChatGPT that certainly require a pattern-matcher or a pattern-completion, which seem like things that induction heads, as described, could be doing. In this paper I had ChatGPT interpret Steven Spielberg's Jaws using ideas from Rene Girard. That requires that it match events in Spielberg's movie with patterns of events that Girard describes. I've done that with other things as well.

In this set of experiments I gave ChatGPT a prompt that begins something like this: "I'm going to tell you a story about Princess Aurora. I want you to use that as the basis for a new story where Prince Harry the Eloquent replaces Princess Aurora." I then include the story in the prompt. That seems like a pattern-matching or pattern-completion task. ChatGPT had no trouble. Things got really interesting when I asked the Princess Aurora be replaced with a giant chocolate milkshake. Just about everything thing in the story got changed, but the new story nonetheless preserved the overall pattern of events in the old story. In these cases it's easy to compare the source story and the new story word-for-word, sentence-for-sentence, and paragraph-for-paragraph to see what ChatGPT did.

Now, of course I couldn't look under the hood, as it were, to verify that induction heads were doing those things. But it seems to me that would be something to work toward, finding a. way to examine what's going on when an LLM performs such tasks.

The thing is, if you ask ChatGPT to tell a story, it will do that. But what does the fact that it can tell a story tell you about what it's doing. Yeah, it's telling a story, so what? But the story task I've given it has a lot of constraints, and those constraints give us clues about the nature of the underlying mechanisms. The interpretation task is like that as well. It's pretty easy to judge whether or not ChatGPT's interpretation makes sense, to see whether or not the events in the film really do match the patterns specified in the interpretive lens, if you will. If the interpretation makes sense, it's got to be doing pattern-matching. And pattern-matching is a much-investigated process.

Finally, I'm SURE that LLMs are full of structure, rich and complex structure. They couldn't perform as they do without a lot of structure. The fact that it's hard to understand that structure in terms of structures we do understand doesn't mean there's nothing there. It just means we've got a lot to learn. LLMs are not stochastic parrots talking shit to a bunch of drunken monkeys banging away on old Underwood manual typewriters.

Oh, BTW, I've set up a sequence, Exploring the Digital Wilderness, where I list posts which are about some of my experiments.

In a paper I wrote awhile back I cite the late Walter Freeman as arguing that "consciousness arises as discontinuous whole-hemisphere states succeeding one another at a "frame rate" of 6 Hz to 10 Hz" (p. 2). I'm willing to speculate that that's your 'one-shot' refresh rate. BTW, Freeman didn't believe in a Cartesian theater and neither do it; the imagery of the stage 'up there' and the seating area 'back here' is not at all helpful. We're not talking about some specific location or space in the brain; we're talking about a process.

Well, of course, "the distributed way." But what is that? Prompt engineering is about maneuvering your way through the LLM; you're attempting to manipulate the structure inherent in those weights to produce a specific result you want.

That 1978 comment of Yevick's that I quote in that blog post I mentioned somewhere up there, was in response to an article by John Haugeland evaluating cognitivism. He wondered whether or not there was an alternative and suggested holography as a possibility. He didn't make a very plausible case and few of the commentators took is as a serious alternative.

People were looking for alternatives. But it took awhile for connectionism to build up a record of interesting results, on the one hand, for cognitivism to begin seeming stale on the other hand. It's the combination of the two that brought about significant intellectual change. Or that's my speculation.

Oh, I didn't mean to say imply that using GPUs was sequential, not at all. What I meant was that the connectionist alternative didn't really take off until GPUs were used, making massive parallelism possible. 

Going back to Yevick, in her 1975 paper she often refers to holographic logic as 'one-shot' logic, meaning that the whole identification process takes place in one operation, the illumination of the hologram (i.e. the holographic memory store) by the reference beam. The whole memory 'surface' is searched in one unitary operation.

In an LLM, I'm thinking of the generation of a single token as such a unitary or primitive process. That is to say, I think of the LLM as a "virtual machine" (I first saw the phrase in a blog post by Chris Olah) that is running an associative memory machine. Physically, yes, we've got a massive computation involving every parameter and (I'm assuming) there's a combination of massive parallel and sequential operations taking place in the GPUs. Complete physical parallelism isn't possible (yet). But there are no logical operations taking place in this virtual operation, no transfer of control. It's one operation.

Obviously, though, considered as an associative memory device, an LLM is capable of much more than passive storage and retrieval. It performs analytic and synthetic operations over the memory based on the prompt, which is just a probe ('reference beam' in holographic terms) into an associative memory. We've got to understand how the memory is structured so that that is possible.

More later.

Miriam Lipshutz Yevick was born in 1924 and died in 2018, so we can't ask her these questions. She fled Europe with her family inn 1940 for the same reason many Jews fled Europe and ended up in Hoboken, NJ. Seven years later she got a PhD in math from MIT; she was only the 5th woman to get that degree from MIT. But, as both a woman and a Jew, she had almost no chance of an academic post in 1947. She eventually got an academic gig, but it was at a college oriented toward adult education. Still, she managed to do some remarkable mathematical work.

The two papers I mention in that blog post were written in the mid-1970s. That was the height of classic symbolic AI and the cognitive science movement more generally. Newell and Simon got their Turing Award in 1975, the year Yevick wrote that remarkable 1975 paper on holographic logic, which deserves to be more widely known. She wrote as a mathematician interested in holography (an interest she developed while corresponding with physicist David Bohm in the 1950s), not as a cognitive scientist. Of course, in arguing for holography as a model for (one kind of) thought, she was working against the tide. Very few were thinking in such terms at that time. Rosenblatt's work was in the past, and had been squashed by Minsky and Pappert, as you've noted. The West Coast connectionist work didn't jump off until the mid-1980s.

So there really wasn't anyone in the cognitive science community at the time to investigate the line of thinking she initiated. While she wasn't thinking about real computation, you know, something you actually do on computers, she thought abstractly in computational terms, such as Turing and others did (though Turing also worked with actual computers). It seems to me that her contribution was to examine the relationship between a computational regime and the objects over which he was asked to compute. She's quite explicit about that. If the object tends toward geometrical simplicity – she was using identification of visual objects as her domain – then a conventional, sequential, computational regime was most effective. What's what cognitive science was all about at the time. If the object tends toward geometrical complexity then a different regime was called for, what she called holographic or Fourier logic. I don't know about sparse tensors, but convolution, yes.

Later on, in the 1980s, as you may know, Hans Moravic would talk about a paradox (which became named after him). In the early days of AI, researchers worked on abstract domains, like chess and theorem proving, domains that take a high level cognitive ability. Things went pretty well, though the extravagant predictions had yet to pan out. When they turned toward vision and language in the late 1960s and into the 70s and 80s, things fell apart. Those were things that young kids could do. The paradox, then, was that AI was most effective at cognitively difficult things, and least effective with cognitively simple things.

The issue was in fact becoming visible in the 1970s. I read about it in David Marr, and he died in 1980. Had it been explicitly theorized when Yevick wrote? I don't know. But she had an answer to the paradox. The computational regime favored by AI and the cognitive sciences at the time simply was not well-suited to complex visual objects, though they presented to problems to 2-year-olds, or to language, with all those vaguely defined terms anchored in physically complex phenomena. They needed a different computational regime, and eventually we got one, though not really until GPUs were exploited.

More later, perhaps.

I'll get back to you tomorrow. I don't think it's a matter of going back to the old ways. ANNs are marvelous; they're here to stay. The issue is one of integrating some symbolic ideas. It's not at all clear how that's to be done. If you wish, take a look at this blog post: Miriam Yevick on why both symbols and networks are necessary for artificial minds.

LOL! Plus he's clearly lost in a vast system he can't comprehend. How do you comprehend a complex network of billions upon billions of weights? Is there any way you can get on top of the system to observe its operations, to map them out?

I did a little checking. It's complicated. In 2017 Hassibis published an article entitled "Neuroscience-Inspired Artificial Intelligence" in which he attributes the concept of episodic memory to a review article that Endel Tulving published in 2002, "
EPISODIC MEMORY: From Mind to Brain." That article has quite a bit to say about the brain. In the 2002 article Tulving dates the concept to an article he published in 1972. That article is entitled "Episodic and Semantic Memory." As far as I know, while there are precedents – everything can be fobbed off on Plato if you've a mind to do it, that's where the notion of episodic memory enters in to modern discussions.

Why do I care about this kind of detail? First, I'm a scholar and it's my business to care about these things. Second, a lot of people in contemporary AI and ML are dismissive of symbolic AI from the 1950s through the 1980s and beyond. While Tulving was not an AI researcher, he was very much in the cognitive science movement, which included philosophy, psychology, linguistics, and AI (later on, neuroscientists would join in). I have no idea whether or not Hassibis is himself dismissive of that work, but many are. It's hypocritical to write off the body of work while using some of the ideas. These problems are too deep and difficult to write off whole bodies of research in part because they happened before you were born – FWIW Hassibis was born in 1976.

Scott Alexander has started a discussion of the monosemanticity paper over at Astral Codex Ten. In a response to a comment by Hollis Robbins I offered these remarks:

Though it is true, Hollis, that the more sophisticated neuroscientists have long ago given up any idea of a one-to-one relationship between neurons and percepts and concepts (the so-called "grandmother cell") I think that Scott is right that "polysemanticity at the level of words and polysemanticity at the level of neurons are two totally different concepts/ideas."  I think the idea of distinctive features in phonology is a much better idea.

Thus, for example, English has 24 consonant phonemes and between 14 and 25 vowel phonemes depending on the variety of English (American, Received Pronunciation, and Australian), for a total between 38 and 49 phonemes. But there are only 14 distinctive features in the account given by Roman Jakobson and Morris Halle in 1971. So, how is it the we can account for 38-49 phonemes with only 14 features?

Each phoneme is characterized by more than one feature. As you know, each phoneme is characterized by the presence (+) of absence (-) of a feature. The relationship between phonemes and features can thus be represented by matrix having 38-49 columns, one for each phoneme, and 14 rows, one for each row. Each cell is then marked +/- depending on whether or not the feature is present for that phoneme. Lévi-Strauss adopted a similar system in his treatment of myths in his 1955 paper, "The Structural Study of Myth." I used such a system in one of my first publications, "Sir Gawain and the Green Knight and the Semiotics of Ontology," where I was analyzing the exchanges in the third section of the poem.

Now, in the paper under consideration, we're dealing with many more features, but I suspect the principle is the same. Thus, from the paper: "Just 512 neurons can represent tens of thousands of features." The set of neurons representing a feature will be unique, but it will also be the case that features share neurons. Features are represented by populations, not individual neurons, and individual neurons can participate in many different populations. In the case of animal brains, Karl Pribram argued that over 50 years ago and he wasn't the first.

Pribram argued that perception and memory were holographic in nature. The idea was given considerable discussion back in the 1970s and into the 1980s. In 1982 John Hopfield published a very influential paper on a similar theme, "Neural networks and physical systems with emergent collective computational abilities." I'm all but convinced that LLMs are organized along these lines and have been saying so in recent posts and papers. 

Load More