gibsonf1

The problem is that the relational database of tables has no relationship to how humans store information, we humans use graph structures to relate information. This is why RDBMS are a dead end in getting closer to understanding.

Hmm, could the use of genAI have anything to do with this failure and the inability to quickly fix it?

This discovery fully validates the organic system side of Peter Putnam's theory on induction, the Neuro Conditioned Reflex Principle, that requires changing state at synapses over time to select the nested Relative Dominance feedback winner in a given Conditioned Reflex feedback loop. Its quite incredible that the functionality of induction proposed by Putnam in 1963 has been consistently validated since then. [1]

[1] https://www.peterputnam.org/outline-of-a-functional-model-of...

The Graphmetrix patent on Conceptual Awareness interfacing with a Linked Data Hypergraph finally came through.

This new SystemTwin™ information structure allows us to capture the full space-time data and interlinked documents about a part of the world in the form of a TwinPod™ or any number of linked TwinPod™s to model systems and collaborate at any scale.

The newish SBCL parallel gc is fantastic and uses no additional memory during gc

Its interesting that they don't even mention the key to human intelligence, concepts, in this list.

Would it work with the parallel GC feature?

Incredible! Is this ready for at scale production use?

Systems Twin Intelligence, where a Pod represents the full space-time information for part of the world, using Solid Protocol: https://graphmetrix.com/trinpod-server

The W3C Linked Web Storage (LWS) working group is transforming Solid into a web standard: https://www.w3.org/groups/wg/lws/

A key error is there literally are no where close to billions of concepts. Its a misunderstanding of what a concept is as used by us humans. There are an unlimited number of instances and entities, but the concepts we use to think about them is very limited by comparison.

The 18,000 people who lost their jobs may disagree.

Our[1] solution to that is to use a hierarchical semantic systems approach such that you can give access to a subsystem or entire biological systems.

[1] https://graphmetrix.com/trinpod-server

Actually, here is an example enterprise internet scale solid solid server: https://graphmetrix.com/trinpod-server

Thats a terrible ontology (the relations) - needs to be much lower level to understand anything important.

Seems a bit scary that the "source" text from the pdfs could actually be hallucinated.

Does it hallucinate with the LLM being used?

Probably without too much difficulty. If you have a sample to confirm, that would be great. frederick @ graphmetrix . com

We[1] Create "Units of Thought" from PDF's and then work with those for further discovery where a "Unit of Thought" is any paragraph, title, note heading - something that stands on its own semantically. We then create a hierarchy of objects from that pdf in the database for search and conceptual search - all at scale.

[1] https://graphmetrix.com/trinpod-server https://trinapp.com

Yep, they completely missed the boat. They tried to use concepts without actually modeling concepts, making a huge mess of contradicting statements which actually didn't model the world. Using a word in a statement does not a concept make!

There is no type of math that can model conceptual reasoning. You can use conceptual reasoning, however, to do math.

Exactly, we disagree and you are not alone in thinking this. You can use reason to do math, but your can't model reason with math.

My point is no type of math will work to model reason. Math is one of the many tools of reason, it is not the basis for reason. This is a very common error.

The error with that is that human reasoning is not mathematical. Math is just one of the many tools of reason.

Under the hood would reveal that we think in terms of concepts, attributes, space-time experience etc, and that language is just a means of serializing that conceptual understanding. We do not think in language.

Also helps that the CEO of the company does Common Lisp Dev.

We have had a great experience using Common Lisp [1] for our causal space-time systems digital twin [2]

[1] http://sbcl.org/

[2] https://graphmetrix.com/trinpod-server

It’s also possible that you do not have information on our technology which models conceptual awareness of matter and change through space-time which is different than any previous attempts?

Not at all, we are building conceptual reasoning machines, but it is an entirely different technology than GPT/LLM dl/ml etc. [1]

[1] https://graphmetrix.com/trinpod-server

There are no LLMs that reason, its an entirely different statistical process as compared to human reasoning.

Actually, it's not about pointy, but about the high rise as a building type - structure as a tall tree. Wright's prototype for this is Price Tower in OK [1]

[1] https://www.gibson-design.com/sketch-price.html