Re: consciousness, and AI KR

Thanks Dave,

I am glad you brought up the graphs and nodes representation at the end of your reply.
I have been looking at the possibility of using category theory to generalize series of state transitions (mathematical proofs, logical deduction, philosophical language based reasoning, biochemical reactions, physical processes, algorithms and many other processes which appear in nature, human reasoning and knowledge representation etc.) and it is possible to arrive at generalized frameworks, which have limits and bounds dictated by both Godel-Skolem theorems and the inherent uncertainties dictated by quantum physics.
Because it is increasingly clear that both quantum and discrete processes are at play at the cellular node levels in brains and neural cell nets in creatures with neural cells we should be able to formally capture all processes that involve sensory perception, learning, cognition, input based response with some generalized form of graphs and nodes.
And in the process we can circumvent the issues of sentience, awareness and consciousness for now.
I have poured over hundreds of articles on neuro science, mathematical consciousness modeling, cognitive science and philosophy to arrive at the conclusion that generalized graph and node concepts and hyper graphs are our best shot at modeling knowledge representation and when we use higher dimension metric space representation for embedding these we can arrive at formal frameworks that also capture quantum processes.

And this seems to be in line with findings from the Blue Brain Program and many other human brain modeling programs.

Milton Ponson
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Project Paradigm: Bringing the ICT tools for sustainable development to all stakeholders worldwide through collaborative research on applied mathematics, advanced modeling, software and standards development 

    On Thursday, October 26, 2023 at 06:38:27 AM AST, Dave Raggett <dsr@w3.org> wrote:  
 
 Thanks Milton.
One consideration for the subjective experience of artificial agents is the ability for them to reason about their past, present and future, i.e. an awareness of time that forms the basis for planning, understanding cause and effect, inferring the intents of other agents, and for learning from an agent’s experience.
In humans, episodic memories are consolidated in the neocortex after initial modelling in the hippocampus. Memories of past events can be retrieved using a combination of cues for what, where and when.  An accessible account is “how does the brain make memories”, see https://www.eurekalert.org/news-releases/945017. Our brain includes so called boundary neurons that decide when to start a record for a new episode, analogous to creating a new folder. We also record links between these “folders” to represent temporal relationships. An open question is how to design artificial neural networks for managing episodic memories, and how to integrate this with artificial neural networks for language models and encyclopaedic memories.
From an AIKR perspective, the notion of episodes as “folders” relates to named graphs as a data type, something that is under active discussion in the W3C RDF-star working group that is currently defining RDF 1.2.  Quite how this would be represented in neural networks is still unclear. What we can say though is the potential for designing datasets that test an agent’s ability to form and reason with episodic memories.  Better yet would be the means to curate such datasets from existing resources and apply them for self-guided machine learning as has been done for large language models.


On 25 Oct 2023, at 17:44, ProjectParadigm-ICT-Program <metadataportals@yahoo.com> wrote:
Dear all,
I concur with Dave Raggett's take on the subject, science and engineering do not deal with soft issues of linguistic interpretation typically found in religion, philosophy and psychology.
The following link provides an interesting article on how language shapes our formation of abstract concepts
Exploring the brain basis of concepts by using a new type of neural network
https://medicalxpress.com/news/2023-10-exploring-brain-basis-concepts-neural.html
The funny thing is, that these findings are nothing new, Buddhist philosophers have pointed this out in many forms. It is just only now that all of these areas of investigation are converging.Linguistic ambiguity and cognitive bias are no new subjects, but are only now becoming important in the context of creating AGI.
I propose sticking to the path described by Dave, but be mindful of what we come across in the process as long as it contributes to enhancing our formal knowledge representation modeling.

Milton Ponson


Dave Raggett <dsr@w3.org>