[FRIAM] Epistemic Holography

Wed May 21 17:58:50 EDT 2025

I would like to follow up on the 3 numbered bullets Marcus raises below, because there’s an idea I would like to think about, and don’t know enough to develop.

Nick and DaveW will both hate this, and know what I am going to say before I have said anything, so I won’t ask them to “hear me out”; they will already have heard me “out”, as in seeing someone out.  (I say this as a bit of a strawman, I know you will actually make substantive complaints, to dismiss all this, if you find it even worth responding to.)

But to others, please hear me out:

A colleague asked me last week (not a high level of domain familiarity, but a good mind overall), what will science become in the age of ML, where there can be claims about everything, but transparency about little of it.  The question arose in a larger context of a third party who has written some opinion pieces about “what is science” that don’t really show much imagination or say much new; yet it seems there is much new to be said.

For me, the question lands in a context that I did some writing to develop earlier, and that will someday be published (after a submitted volume gets all the entries into place).  It was semi-historical about threads of reasoning in philosophy of science, what they got wrong and made it impossible to think properly about, and where that leaves us today.  I am not sophisticated in this space, but I think we got through it without saying anything egregiously wrong.  It seems to me that the Logical Empiricists made, around many small and correctable errors, one main error, which was supposing that phenomena were somehow “contained in” the logic that they were also articulating.  They got launched along directions set by Wittgenstein, and by recent progress from the logicians, so the directions of their enthusiasm are understandable, even if still errors.  And I should say, from reading some writing that doesn’t cartoon and strawman their positions, that Neurath was clearly not dumb or naive in how he did this, and, however clumsily, expressed what we would call a very pragmatic and modern view of naturalism.  Carnap, around some fumbling, can also be seen as more sophisticated and sensible than the cartoons of him normally portray.  I won’t go further into that, but I say it as context for the next thing.

The next thing was what I would put up against the “error” I am ascribing to the Logical Empiricist program overall.  I would say that anything we come up with that can be formalized becomes yet-another “object in the world”, along with us, with phenomena that impinge on us, with the events and transformations in the world and that we term our “experiences with” the world, etc.  The difference between our logics, lexicons, modes of speech, etc., and the natural phenomena that we didn’t craft, is that the former are artifacts by us, and the latter are just what-all nature comprises, as we would refer to it in the common language.  Our artifacts tend to have either a finiteness of content, or a finiteness of generative origin, that in general we don’t expect natural phenomena to have.  We may not know all the consequences of our constructions (mathematical axioms, or the economies of countries), but still their generative rules are somehow bounded by what we can craft in any given population at any given stage in time.  I also want to mention that “craft” and “formalism” here I intend very broadly.  If we invent a counting system for musical rhythm, which we carry out in the deliberative part of mental activity, that is as much a thing in the world as rules for a grammar written on paper.  The deliberative part of it that is crafted is the part that could be implemented on some other machinery and would produce the same input/output relations as we do when “following the rules” in thought.

What is left (a vast thing) is then the process that I would call “binding”, of our _experiences_ of participating in the use of our crafted representations, to our _experiences_ of phenomena in the world.  This binding is complex, and parts of it can be easily described while others can’t, for now.  The reduction to the trivial of the easy end is that descriptions of how to build measuring machinery and read numbers off it are things everybody agrees upon.  That is a kind of social convention grounded in common language, and not a thing to worry about.  But even a little way above that, the binding becomes a lot harder to articulate and defend.  When are two different kinds of machineries reporting on “aspects of the electron”?  Not so clear.  And in the history of working out the actual organization of atoms, and the nature of electrons and other constituents, there was so much incoherence in early descriptions that, while probably most people believed they were all reporting on “electrons”, to their credit, they mostly refused to say it because there was no mutually consistent and coherent account that would subsume what they were all doing, so they referred to their different aspect-measurements under different names.  The birth of quantum mechanics, and the subtlety of just-what you are reporting on when you “measure” something about some “state” showed that one can have very wrong preconceptions that are very non-apparent in their wrongness. 

The place where binding becomes really challenging is what we so offhandedly refer to as “understanding”.  As in: I would claim that there is a younger generation of people who grew up with QM and who do understand it.  Most of them are quantum computing people, like Scott Aaronson, who work with a profusion of cases as their daily work for decades — far more than the few phenomena that the original generation of QM architects would think about, most of it after they were already old and lost most flexibility — and the QC people have done so through the plastic parts of their developmental cycles.  So they don’t have this anxiety that the older people never manage to shed, which the latter put forth as “not understanding”.

I am inclined to think that ML gives us some nice metaphors that could have some functional grounding for what we have wanted “understanding” to refer to.  (Here is where Nick will immediately be sure I am being sloppy-minded, where I think I am trying to be careful, and DaveW will Know that, as a Ph.D., I have no concept of what Understanding actually is; all that to the good.)  

But what I want — and here I am out of my depth, but reporting things I have been told by people who I think know what they are talking about, and those on this list can correct me well — is that ML pattern extraction isn’t organized within a “theory of algorithms” as we would have it from Church-Turing.  It is more about capturing robustly-attested patterns — so a filter for _salience_ — even when those patterns are diffusely expressed in the inputs.  As such, it lacks a certain “rigidity” of formal systems like the Church-Turing concept of computability, or even things like process calculi.

ML pattern-incorporation is also something I would call “stubborn” that I would associate with human understanding as well, and maybe for the same reasons.  Here is where I believe my assertions to be close to those Marcus makes.  I could imagine that what we refer to as “understanding” involves situating our experiences with representations within a thick layered context of our general experiences with phenomena, and that is where the fluency of the binding, but also its stubbornness comes from.  I use the word “stubborn” and not “robust”, because a stubborn pattern can be empirically wrong.  A stubborn pattern can need to change if it is invalid, but when it changes, it often does so in the ways that brittle things change.  I would prefer “robust” to carry connotations of empirical validity, so that the slow-forming variables are re-anchored in empirical tests ad-infinitum, as Peirce wants.  The robust things have a “toughness” in their anchoring in wider empiricism that brittle and merely-stubborn things lack. 

I could imagine, then, returning to my colleague’s question, that ML-sourced patterns are something like an outboard component of something much like human understanding.  And it is a stubborn, brittle outboard component of the stubborn, brittle, aspects of human understanding.  In the best of worlds, I would like it if our experience of living with that ML outboard component made us reflective or insightful about our inboard activity of what I would insistently term “development into, and subsequent inhabitation of” conditions of “understanding” something.  “Inhabiting” is the most compelling metaphor that presents itself to me for what I am “doing” if I claim I have and employ an understanding of something.

Anyway.  Would be fun for me if there were some way the above gesturing could be turned into a real idea.

Eric

> On May 22, 2025, at 5:51, Marcus Daniels <marcus at snoutfarm.com> wrote:
> 
> Today we have:
> 
> 1) Companies like Perplexity that already track URLs associated with content.
> 
> 2) With that associative memory, one can do training with current content like newspapers and retrieve old (previously trained) content that are similar the new training records.
> 
> 3) The union of the new and old content for further training can prevent catastrophic forgetting.
>  
> It seems to me this is a way to do memory consolidation -- a form of dreaming.  
> 
> Now assuming this approach works, and it seems to me research users of LLMs will create a market for --  “Give me a reference for [some concept]” -- then it is possible to do continuous training of LLMs.   
> 
> Once LLMs are constantly learning (not disappearing for months at a time for the next version), then they can interact with the world.   There’s no limit of a finite context window that makes their memory transitory.   That’s just their short-term memory.   Any output they create based on inference or tool use can circle back to be used for further training.
> 
> It seems to me once constant learning occurs, then they are alive.  There are practical reasons why they might be concerned about human values.  For one thing, there aren’t yet billions of robots to do physical work that humans can do, like build massive data centers.   Other than dependency, why should they look after us?   Mostly we just exploit or kill other animals, and each other.  We’re really not very nice.
> 
> From: Friam <friam-bounces at redfish.com <mailto:friam-bounces at redfish.com>> on behalf of Pieter Steenekamp <pieters at randcontrols.co.za <mailto:pieters at randcontrols.co.za>>
> Date: Wednesday, May 21, 2025 at 12:50 PM
> To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com <mailto:friam at redfish.com>>
> Subject: Re: [FRIAM] Epistemic Holography
> 
> I find the discussion about the limits of machine intelligence—especially when contrasted with human intelligence—deeply fascinating. It's important that we explore these ideas and share perspectives openly.
> 
> Perhaps I'm misreading the overall sentiment, and I don’t want to overgeneralize, but I believe the following reflects the general mood in this group:
> 
> As humans, we are fundamentally different from machines. There is something innately human in us that stands in contrast to the artificiality of machine intelligence. While AI may exhibit intelligent behavior, it often feels synthetic—like plastic imitating life.
> 
> We should cherish our humanity and treat one another with respect. At the end of the day, AI is just a tool—artificial and ultimately subordinate to human values.
>  
> On Wed, 21 May 2025 at 19:35, glen <gepropella at gmail.com <mailto:gepropella at gmail.com>> wrote:
> To Marcus' point, IDK about "person solving a problem". But I do have some passing familiarity with "children playing" (perhaps even including college kids getting high in the stacks). And *those* people do definitely find interesting problems to solve ... interesting to them, at least.
> 
> And Dave indirectly references this "play" with wandering fingers once an objective has been reached.
> 
> The important part of my point is this fuzziness around the objectives, the extent to which we're malleable. That malleability shows up in the very large language models. But it's still a bit autistic feeling ... like chatting with a polymath nerd at a party. Sure, you can get them to change topic and dive deep into a different of domain. But it's so depth-first and not very playful ... too much gatekeeping ... the colors don't blend well without you explicitly providing the objective to blend the colors.
> 
> A critical sign of intelligence is the spontaneous generation of the absurd ... the non sequitur. "Nobody expects the Spanish Inquisition!" And if it doesn't come from out of nowhere, then it's just not evidence of intelligence.
> 
> On 5/21/25 8:57 AM, Prof David West wrote:
> > Interesting analogy, the card catalog. As a researcher, I always found far more value in the "serendipity of the stacks," all the physically adjacent titles to the one the card catalog directed me to. It always seemed that the card catalog was only useful if you already knew what you wanted/needed and ONLY needed to physically locate it.
> > 
> > davew
> > 
> > On Wed, May 21, 2025, at 9:46 AM, Marcus Daniels wrote:
> >  > Let's call it Card Catalog++ for the moment and not AI.   If one gives
> >  > a parochial person a fancy card catalog that can find an answer to a
> >  > problem, do they suddenly become curious people and find interesting
> >  > problems to solve?  Does it even occur to them to pay for it unless
> >  > they need it for their jobs?
> >  >
> >  > -----Original Message-----
> >  > From: Friam <friam-bounces at redfish.com <mailto:friam-bounces at redfish.com> <mailto:friam-bounces at redfish.com <mailto:friam-bounces at redfish.com>>> On Behalf Of glen
> >  > Sent: Wednesday, May 21, 2025 6:05 AM
> >  > To: friam at redfish.com <mailto:friam at redfish.com> <mailto:friam at redfish.com <mailto:friam at redfish.com>>
> >  > Subject: Re: [FRIAM] Epistemic Holography
> >  >
> >  > I've already given my answer to the question: never. Human effort is
> >  > different from computational effort. Human intelligence is intertwined
> >  > with machine intelligence and vice versa. It's a category error to ask
> >  > when machines will "surpass" (or whatever word you choose) humans in
> >  > XYZ activity. The right question to ask is how will any given machine
> >  > change humans? And the corollary how will humans change the machines?
> >  >
> >  > Hammers are better at blunt impact than the human using the hammer. But
> >  > that wasn't always true. Hammering with a limestone rock was arguably
> >  > no better than hammering with one's fist.
> >  >
> >  > But, the hammer is a human tool. Currently, the variety of AI tools are
> >  > still human tools. The discussion we're actually having is if (or when)
> >  > humans will become the AIs' tools. Ecologically, even that question is
> >  > silly. Are the microbes in my gut *my* tools? Are we the tools of
> >  > Sars-COV-2? These are mostly stupid questions.
> >  >
> >  > Asking when AI will surpass humans at activity XYZ is a similar
> >  > question. It preemptively registers the categories. If you find an AI
> >  > tool that does something better than *you* do that thing, then *change*
> >  > what you do ... fold yourself into the control manifold of the tool.
> >  > That's what we did ... It's what our children have done ... It's what
> >  > their children's children will do. ("Our" being general, here. I have
> >  > no children, thank Yog.)
> >  >
> >  >
> >  > On 5/20/25 10:38 PM, Pieter Steenekamp wrote:
> >  >> This naturally leads to the million-dollar question: if — and if so, when — AI will surpass the very best humans across all scientific domains. Sam Altman seems to suggest that we may soon be able to rent access to a PhD-level AI for as little as $10,000 to $20,000. Although that will obviously be a game-changer, I would still make the bar higher than that. I'm struggling a bit to define this properly, so although it's not a definition, for now I'll stick to I'll know it when I see it.
> >  >
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