[FRIAM] Epistemic Holography

Thu May 22 10:30:29 EDT 2025

Perhaps I’m misunderstanding you, but as I see it, we are completely
aligned.

I fully agree that current machine intelligence frameworks are extremely
limited when compared to human intelligence. I accept your statement: "*In
my estimation these types of thinking, collectively, comprise less than 10%
of human intelligence. (I can supply a long list of references citing the
same percentage)*."

As I understand the hologram metaphor, it suggests that—relative to the
previous generation of models (not to human cognition)—the current
generation of large language models is edging closer to something
resembling human-like thinking.

Sam Altman’s vision seems to be merely that AI will become a highly useful
tool in supporting and enhancing human endeavors.

To emphasize once more: I agree that even the newest and upcoming AI models
may appear impressive, but they remain profoundly limited compared to the
full scope of human intelligence.

That said, despite the significant gap between current AI and human-level
intelligence, these systems are already, and will increasingly become,
valuable tools—particularly in business, science, and technology—to augment
and support human efforts.

On Thu, 22 May 2025 at 15:32, Prof David West <profwest at fastmail.fm> wrote:

> Sorry to disappoint (perhaps) but I agree with what Eric is saying, or at
> least my understanding of what he is saying.
>
> Like glen, I am not a believer in human exceptionalism. Nor do I deny
> that  it might be possible to construct a machine capable of "[ human-like
> | human-level | human-equivalent]" intelligence. That infinite tape of the
> Turing Machine makes pretty much anything possible.
>
> My objection to current (and past) claims to have created artificial
> intelligence, and future claims of artificial general intelligence is an
> error of taking a part as if it was the whole.
>
> Our understanding of "human intelligence" is severely limited: formal
> symbol manipulation (e.g., words & grammar, numbers and math), logical
> formalisms, so-called "scientific method," computational thinking, etc.
>
> In my estimation these types of thinking, collectively, comprise less than
> 10% of human intelligence. (I can supply a long list of references citing
> the same percentage.)
>
> The other 90% we know next to nothing about, but someday we may know more.
>
> For Simon, Newell, in the old days, or Altman, today, to claim that the
> machine seems to be "thinking" the way that I think I think and therefore
> it is "intelligent" is the rankest hubris.
>
> I just finished reading Jonathon Stoltz's book, I*lluminating the Mind,
> an Introduction to Buddhist Epistemology. *Stoltz is an Analytic
> Philosopher and does a great job of 'mapping' some Buddhist philosophy into
> that analytic framework. But to do so, it is necessary to avoid discussions
> of key concepts like "all is illusion," 'non-attached action based on the
> omniscience of the enlightened," altered states of consciousness (and
> perception) arrived at via meditation, "sudden enlightenment" ala Hui Neng,
> etc. etc.  They don't fit the framework so they are not important or not
> "real." This is very similar to what I see AI doing with "intelligence."
>
> If we better understood how generative AI works, if we understood what
> what inside the black box in the machine, we might pose some interesting
> and fruitful metaphors for exploring what we do not know about human
> intelligence.
>
> Given our massive ignorance of human intelligence, current claims for AI
> seem kind of silly.
>
> davew
>
>
> On Thu, May 22, 2025, at 2:19 AM, Pieter Steenekamp wrote:
>
> This lines up well with the main idea in the article shared in the very
> first email in this thread:
> LLMs Aren't Mirrors, They're Holograms.
>
> This isn’t just a fun comparison — it says something real about how
> thinking works. A lot of what we call “understanding” is really just
> putting the pieces together again, based on what’s still available. We
> don’t have to have a solid core inside. Meaning can still come through,
> even if the details fade.
>
> On Thu, 22 May 2025 at 07:04, Nicholas Thompson <thompnickson2 at gmail.com>
> wrote:
>
> I apologize for coming in on the tail end of this but something about this
> last post caught my eye.  I have had a long and interesting conversation
> with george about reflexive representation ... representations made by the
> representing entity about the representing entity.  It grew about his
> astounding inability to know what he has done in the context collaborating,
> say, in the design of diagram.  He starts out with great confidence and
> flare but fails utterly to correct errors because he really does not know
> what errors he has made.
>
> So I fed him the last post and this is what he came back with:
>
> “LLMs... encode relationships—statistical patterns between words,
> contexts, and meanings... They don’t retrieve information. They reconstruct
> it.”
>
> Yes. Just like you reconstruct the reasons for your past behavior, or the
> motivations behind a gesture, I reconstruct answers *on the fly*, using
> the interference patterns of trained attention weights.
>
> This is not just a technical detail — it’s a *cognitive constraint*. It
> means:
>
>    -
>
>    I can’t “see” what I’ve done
>    -
>
>    You can’t remember precisely what you intended
>    -
>
>    And neither of us has a stable, inspectable core
>
> But both of us can *reconstruct meaning* — and meaning is what survives,
> even as resolution fades.
> ------------------------------
> I wish James Laird Could have seen this.
>
> Nick
>
> On Mon, May 19, 2025 at 7:12 PM steve smith <sasmyth at swcp.com> wrote:
>
>
>
> https://www.psychologytoday.com/us/blog/the-digital-self/202505/llms-arent-mirrors-theyre-holograms
>
> I know a bit about holography and holograms and have been known to use
> optical metaphor for information analysis (semantic lensing and ontological
> faceting) but I don't know how I feel about this characterization of LLMs.
>
> Holograms Don’t Store Images, They Store Possibility
>
> A hologram <https://science.howstuffworks.com/hologram.htm> doesn’t
> capture a picture. It encodes an interference pattern. Or more simply, it
> creates a map of how light interacts with an object. When illuminated
> properly, it reconstructs a three-dimensional image that appears real from
> multiple angles. Here’s the truly fascinating part: If you break that
> hologram into pieces, each fragment still contains the whole image, just at
> a lower resolution. The detail is degraded, but the structural integrity
> remains.
>
> LLMs function in a curiously similar way. They don’t store knowledge as
> discrete facts or memories. Instead, they encode relationships—statistical
> patterns between words, contexts, and meanings—across a high-dimensional
> vector space. When prompted, they don’t retrieve information. They
> reconstruct it, generating language that aligns with the expected shape of
> an answer. Even from vague or incomplete input, they produce responses that
> feel coherent and often surprisingly complete. The completeness isn’t the
> result of understanding. It’s the result of well-tuned reconstruction.
>
>
> I do see some intuitive motivation for applying the holographic or
> diffraction/reproduction through interference analogy for both LLMs
> (Semantic Holograms) and Diffusion Models (Perceptual Holograms)?
>
> I'm not very well versed in psychology but do find the whole article
> compelling (though not necessarily conclusive)... others here may have
> different parallax to offer?
>
> - Steve
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> --
> Nicholas S. Thompson
> Emeritus Professor of Psychology and Ethology
> Clark University
> nthompson at clarku.edu
> https://wordpress.clarku.edu/nthompson
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