[FRIAM] ML ⇔ understanding (was Epistemic Holography)

[Prof David West]

Unification. None implied. I agree that such would be unlikely.

To the extent that some people confidently express that "this is how we use language," this is how scientists think," they are pretty much wrong to some significant extent. Reconciliation/unification would always be elusive or non-existent. You cite logicians, but my favorite example would be Whitehead's Process Philosophy contrasted with his earlier work with Russel.

Even if possible, I don't immediately see the value in "unification."

Cynical aside to Pieter: I think Altman's only vision is a personal net worth greater than his old partner Elon.

davew


On Thu, May 22, 2025, at 8:38 AM, glen wrote:
> I'm arrogant enough to think I understand your gist (resurrecting your 
> prior context that Marcus elided). But I'm having trouble coming up 
> with any kind of Unified scheme that ML uses. Similarly, I'm having 
> trouble committing to the implication/assumption that humans (even 
> scientists) think in some way that can be unified.
>
> In both cases, it could easily be, and most likely *is*, my ignorance 
> that prevents me from seeing these unifications. But given the 
> resistance of, for example, classical logicians to non-classical 
> logics, I might be free enough to assert that any consensus on how 
> humans think might also simply be "stubborn". And that makes me a bit 
> sympathetic to Dave's accusations of Scientism.
>
> Were we to fix particular sub-domains and restrict our discussion to 
> *those*, then it would be easier to see a unified frame. The current 
> discussions are about LLMs. So the ML we talk about could be limited to 
> that. And, rather than science writ large (incl. social science, 
> evolution, etc.), we could limit the discussion to gravity and the 
> standard model. Then we could map things like understanding between 
> those sub-domains.
>
> But without such specificity, your colleague's (and your) binding for 
> "science" casts doubt on the enterprise.
>
> On 5/21/25 5:50 PM, Santafe wrote:
>> Not higher standard.  (Or, probably so by many — I recognize the pattern you call out — but I think not by me.)  I agree fully with what you say below.  Just as true for my own mind as someone else's, though I have access to a higher bandwidth of (some) report outputs about it.
>> 
>> The nature of understanding, its characteristics and weirdnesses, is something that I imagine could be interesting though.  Science happens to be a useful somewhat-orderly window through which to ask about it, to the extent that we can segment science off as a domain within the culture that takes a particular, distinctively pragmatic (by which I mean recursive in descriptive levels) approach to trying to recognize errors that are cryptic and hard to catch and characterize.
>> — Intersubjectivity to catch the mistakes of subjectivity and revelatory truth;
>> — empirical grounding to catch the mistakes of both subjective and intersubjective (mass-delusional) renderings;
>> — a premise that refutation is generally stronger than confirmation, and a certain “open society” model (connection made for the reasons Popper did) to support that asymmetry;
>> — formal language as a kind of debugging framework for informal reasoning;
>> — social protocols to try to figure out what it means to “empirically ground” anything;
>> — and hopefully more than those.
>> All that effort at error discovery and correction, however, is ultimately grounded at least in part in people's sense that they “understand” one or another thing.  Probably the generation of ideas also depends considerably on what that “understanding” constitutes.
>> 
>> Far from wanting to claim it is unconnected to the broad schema that ML uses, I am interested in what that schema might enable us to see about ourselves, because now it can be ramified to quite complex patterns, not just the simple ones that could be investigated in the first generations after Hopfield.
>> 
>> Obviously I am overdue on grant-reporting tasks that I am procrastinating further.
>> 
>> Eric
>> 
>> 
>>> On May 22, 2025, at 8:06, Marcus Daniels <marcus at snoutfarm.com> wrote:
>>>
>>> Eric writes:
>>>
>>> “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.”
>>>
>>> What transparency does one have about a new hire’s mind?   An employer may have a credential from a university, or a confirmed work history, or green wall of github commits from a credible open-source project, or a highly cited Google Scholar profile.  This doesn’t tell us anything about the mechanism of their reasoning; it remains a black box.   And yet LLM-based AI is held to a higher standard by some.  Why?
>>>
>
>>> On 5/21/25 2:58 PM, Santafe wrote:
>>>> 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.
>> 
>
>
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