Re: K3D TPAC Presentation - Vocabulary Slide for Demo A

Daniel
I am suggesting that in order to contribute you need to be familiar with
the space, the mechanism, the field
From the 10 terms in your slide, I understand that you do not have such
familiarity *not from the planet where I come from

neither with the spatial domain, nnor with knowledge representation.
You say that you do things *like a multivibe coding something
and I think it is fair that you show it today

It is not fair that you say you're going that you have a methodology and
then not being able to
show how it works

So please, whatever you'd like to contribute, make it relevant and fit in
the picture
No, we are not asking for a spatial domain ontology here *because they
already exist and we have them
What I am asking is to explain what you are talking about and how does it
fit here
taking into account that there are decades of literature, projects,
technologies already
So, as a simple example, when I make a new type of pizza
I need to be able to explain how is this different from all the other
pizzas  that exist

To start from the state of the art review, is simply to remind everyone a)
what are talking about and b) that you
are familiar with the topic you are trying to make a contribution to

But as I am trying to prepare my own work and do exactly what I am
suggesting others do *I do eat
my own dog food - for my overview today, I do not have time to repeat
myself.

I am sure it will all come to you, as we all gone through that  orientation
stage
I need to finish my work now, urgently

See you online

PDM


On Fri, Nov 14, 2025 at 1:35 PM Mike Bergman <mike@mkbergman.com> wrote:

> Hi All,
>
> Daniel, there is something in your approach that caught my eye. My cursory
> reaction is that I would like to see your ideas framed in more widespread
> appropriate terms (Peirce, Wheeler, Prigogone), and I'd like to see a more
> 'marketing' approach to your presentation, given the audience at TPAC. Not
> knowing what to say, I captured things in your own words across this thread
> and asked one of our LLM friends to summarize:
>
> *The K3D spatial vocabulary brings a fresh and practical contribution to
> the AI–Knowledge-Representation community by standardizing how we describe
> 3D knowledge environments—Houses, Rooms, Doors, Nodes, and Galaxies—that
> both humans and AI agents use to navigate, cluster, and reason over complex
> information. Positioned squarely within the “Domain Ontologies / ODD” layer
> of the AI-KR landscape, K3D provides a coherent and interoperable
> vocabulary for representing spatial organization of knowledge, with clean
> links to KR learning via embedded vectors and to reliability engineering
> through boundary and access constraints. By offering a shared language for
> modeling spatial knowledge structures, K3D helps unify disparate AI-KR
> practices and makes it easier to exchange, annotate, and validate knowledge
> resources across tools, research efforts, and organizations.*
>
> *For the wider community, K3D delivers immediate value by offering
> concrete, reproducible use cases that demonstrate how spatial ontologies
> improve clarity, collaboration, and machine interpretability. Whether
> representing an “AI-KR House” of vocabularies as interconnected Rooms and
> Nodes, visualizing how embeddings cluster in a Galaxy, or defining typed
> Doors that link related subdomains, K3D shows how 3D conceptual spaces can
> make complex KR artifacts more navigable and actionable. Its methodology
> supports transparent vocabulary development, repeatable annotation
> workflows, and a clear pathway for integrating symbolic and learned
> representations. In short, K3D helps professionals move beyond flat,
> fragmented documentation toward structured, extensible, and
> standards-aligned environments that advance the state of AI-KR practice*.
>
> Obviously, I would tone this down, recognize competing approaches and the
> history, shorten it, and remove those 'breathy' aspects common to LLMs.
> Nonetheless, it helped me to better understand and contextualize what you
> are doing.
>
> Good luck with the presentation.
>
> Best, Mike
> On 11/13/2025 11:11 PM, Paola Di Maio wrote:
>
> Daniel
> thanks for your background
>
> it is important to understand where members come from, but ultimately,
> where we come from does not matter much
> *and we may not have time to listen toe everybody's story,
>
> the meeting has a section for open floor, which means you are free to talk
> about your interest and goals
>
> But it sounds that you have not yet cleared in your mind as to what you
> want to contribute and how,
> probably because you are not yet familiar with what we are doing here KR
> and how *which we confess is not always clear to me either but at least we
> have spent time working it out
>
> The topic of interest here is not 3KD, but the KR for spatial domain, that
> it aims to represent
> To be able to make contributions to your field you need to be familiar
> with:
> a) what is a useful contribution, for me one or two concepts and terms
> well defined and with use cases could be useful contributions
> b) how to make a contribution *how to conduct a state of the art review
> for example, and how to communicate your results meaningfully
> c) how to pitch your proposed contribution to the work being done
>
> So you need to take into account all of these things.  The reason why I am
> engaging with you here on this
> is because I myself, have benefited immensely from being mentored by
> others
> John Sowa is the best example for me, but everyone who has taken time to
> point me in the right direction
> throughout my journey has been, and still is, my mentor
>
> Now we use LLM to point us, but they can be misleading
>
> The demo i would be interested in is the orchestration that you mentioned
> multiple times
> a demonstration of how multiple LLMs can be queried meaningfully without
> API keys
>
> if you can do it, please show it
> if not, show it to us in the future when you can do it
>
> P
>
>
>
> On Fri, Nov 14, 2025 at 12:43 PM Daniel Ramos <capitain_jack@yahoo.com>
> wrote:
>
>> Paola,
>>
>> Thank you for your apology and for offering the 10‑minute slot.
>>
>> I appreciate you taking responsibility for the earlier tone, and I’m glad
>> we can try to bring this back to a constructive technical discussion.
>>
>> On the AI‑generated materials:
>> I do use AI assistants to help me write in professional English and to
>> structure long, complex thoughts.
>> English is not my first language, and I do not treat AI the way I treat a
>> compiler, an IDE, or a spell‑checker: as a tool, AI is a partner that I
>> direct.
>>
>> The architecture, the vocabulary, and the standards proposals come from
>> my own work over many months;
>>
>> AI helps me express that work more clearly, it does not decide what I
>> think.
>>
>> A bit more on where I am coming from technically:
>>
>> I’m self‑employed, working from Cidade Estrutural, and I paid for my GPU
>> and AI usage out of my own pocket.
>> My inspirations are very concrete: Apollo 11 engineering and code,
>> computers history, the game industry, and especially the demoscene – for
>> example, the famous early‑2000s FPS/demos that fit into tens of kilobytes
>> by storing procedures instead of raw assets.
>> That led directly to K3D’s procedural compression: store “how to
>> reconstruct” knowledge on GPU, rather than huge raw embedding arrays (not
>> Milton work, that was a happy coincidence).
>>
>> I also research a lot about learning methodology.
>>
>> I designed the training pipeline like teaching a child: we are currently
>> ingesting atomic knowledge (characters, punctuation, math symbols) so we
>> can later build up to words, phrases and texts inside a coherent spatial
>> memory.
>>
>> From that perspective, PTX is not a buzzword for me, it’s a deliberate
>> engineering choice.
>>
>> Hand‑written PTX kernels are rare because they are the GPU equivalent of
>> assembly: most people rely on cuBLAS/cuDNN and high‑level frameworks.
>>
>> The reason DeepSeek’s work on a single PTX kernel attracted so much
>> attention is exactly that – very few teams are willing or able to go down
>> to that level to squeeze out performance and control.
>>
>> K3D intentionally pushes reasoning and memory operations into PTX so the
>> logic is fast, transparent and reproducible on consumer hardware, not
>> hidden in black‑box libraries.
>>
>> I also see AI as more than “text prediction”.
>>
>> The line of work on world models and video‑based predictive models shows
>> that modern systems are learning internal models of environments, not just
>> token sequences, and they already noticed that the AI future is at least
>> virtually embodied.
>>
>> K3D is my attempt to give those systems – and humans – a shared spatial
>> KR substrate: Houses, Rooms, Nodes, Doors, Galaxy, a Tablet.
>>
>> In your diagram, that vocabulary lives in the Domain Ontologies / ODD
>> space (with links to KR learning and reliability), and that is the part I
>> have been trying to contribute to this CG.
>>
>> Regarding today’s session and the demo:
>> Given where the implementation is right now, I do not have a polished,
>> self‑contained demo that meets the expectations you outlined (state of the
>> art, open vocabularies list, validated use cases and a live system) ready
>> for TPAC.
>>
>> We are still in the phase of training atomic knowledge and integrating
>> procedural compression; the viewer and spatial structures exist, but I
>> would rather not present an improvised demo that doesn’t meet your
>> standards or mine.
>>
>> So, to use our time respectfully, I propose the following for the
>> 10‑minute slot you offered:
>>
>> I use the time to explain “where I’m coming from”:
>> my background, the historical inspirations (Apollo, demoscene,
>> engineering, teaching), and how that led to a spatial KR architecture that
>> *overlaps* but *does not depend on this CG*.
>>
>> I summarize, very concretely, how the K3D spatial vocabulary maps into
>> your diagram: it is a domain ontology for spatial knowledge environments
>> (Houses, Rooms, Nodes, Doors, Galaxy, Tablet) in the ODD space, with clear
>> edges to KR learning and reliability.
>>
>> If you prefer to keep the slot only for a brief Q&A instead of a full
>> 10‑minute overview, I will of course respect that.
>>
>> My goal is simply to explain my position clearly once, within your scope,
>> and then let the group decide whether this spatial KR perspective is useful
>> for AI‑KR going forward.
>>
>> Thank you again for the apology and for the opportunity to clarify these
>> points.
>>
>> Best regards,
>> Daniel
>> On 11/14/25 1:21 AM, Paola Di Maio wrote:
>>
>> Daniel
>> I completely apologise for my tone and for suggesting enrolment in my
>> courses
>>
>> This was intended ironically and resulting of my own frustration in
>> reading your AI generated materials and responses
>>
>> I rephrase:  it seems that you are not familiar with the spatial
>> knowledge representation domain
>> and I suggest you familiarise yourself with the learning resources
>> available
>> *it is true tho that if you need specific guidance from me you need to
>> enrol in one of my courses
>>
>>
>> I am glad to receive an email from you that sounds written by a human,
>> expressing human concerns
>> what about if in your 10 minutes slot today we discuss where you are
>> coming from *that is what is colloquially referred to as coming from
>> another planet, and you get the chance to air exactly all the points that
>> you state in your email below
>> PLUS give a demo
>> and we can take things from there?
>>
>> I apologise sincerely for causing offense and once again thank you for
>> stepping up and enabling this exchange
>>
>> P
>>
>> --
> __________________________________________
>
> Michael K. Bergman
> 319.621.5225http://mkbergman.comhttp://www.linkedin.com/in/mkbergman
> __________________________________________
>
>