Re: the intersection between AIKR and COGAI

FWIW: I think the idea of artificial minds being rendered consciousness is
an "ungodly" concept. Artificial minds being rendered in relation to
property rights laws / asset related considerations, entirely plausible.

I therefore think it's too dangerous to try to support peoples extension of
self (digital twins) as it's likely to be something companies want to
"own".  Whereas; the idea of democratisation ownership of AI agents - or
robots (whether their in a phone or some sort of others physical object)
doesn't really matter.

https://twitter.com/WebCivics/status/1585976653867405312

If humanity is under attack by dangerous robots, I'd like to have one that
I own fighting for me, kinda like r2d2 but different.

Timh.

On Fri, 28 Oct 2022, 11:25 pm ProjectParadigm-ICT-Program, <
metadataportals@yahoo.com> wrote:

> There may be a relatively easy way out of this confusion. But it starts
> with disentangling knowledge representation completely from AI.
>
> Following Dave Raggett's line of reasoning we posit knowledge
> representation to be a class of semiotic  (input) structured descriptions
> that lend themselves to analysis through logical, computational,
> mathematical and computability processes in order for these to create
> computable (output) algorithms, given a certain set of objects in an object
> system in physical reality (spatiotemporal defined set of confined spaces
> and objects therein) which together with a set of relevant interaction
> processes defining an interaction system.
>
> This way we eliminate the problem of distinguishing between structured
> data, information and knowledge.
>
> For this interaction system we now define classes of transformational
> mappings for the interaction system, (1) dealing with sensory input through
> observation, (2) converting the observation datasets to formats to compare
> to existing instances in the structured descriptions, (3) exchanging or
> passing observed datasets to another structured description, (4) add,
> delete, edit or deprecate instances to the structured description, (5)
> trigger actions in the interaction system..
>
> We can now use all mathematical, computer science, computability, and
> mathematical tools from theoretical physics, representation theory, and
> category theory to produce generalizations  of the basic components, being
> structured descriptions and interaction systems to build increasingly
> complex sets.
>
> Note that the concepts of mind, consciousness and sell awareness are
> avoided, but openness and explainability become embedded.
>
> Mind and consciousness come into play if we contemplate artificial general
> intelligence.
>
> And in doing so we avoid any ontological and epistemological discussions
> with philosophers, because those only arise at the AGI level.
>
> Milton Ponson
> GSM: +297 747 8280
> PO Box 1154, Oranjestad
> Aruba, Dutch Caribbean
> 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 27, 2022 at 09:05:10 PM AST, Paola Di Maio <
> paoladimaio10@gmail.com> wrote:
>
>
> Thank you all for contributing to the discussion
>
> the topic is too vast - Dave I am not worried if we aree or not agree, the
> universe is big enough
>
> To start with I am concerned whether we are talking about the same thing
> altogether. The expression human level intelligence is often used to
> describe tneural networks, but that is quite ridiculous comparison. If the
> neural network is supposed to mimic human level intelligence, then we
> should be able to ask; how many fingers do humans have?
> But this machine is not designed to answer questions, nor to have this
> level of knowledge about the human anatomy. A neural network is not AI in
> that sense
> it fetches some images and mixes them without any understanding of what
> they are
> and the process of what images it has used, why and what rationale was
> followed for the mixing is not even described, its probabilistic. go figure.
>
> Hay, I am not trying to diminish the greatness of the creative neural
> network, it is great work and it is great fun. But a) it si not an artist.
> it does not create something from scratch b) it is not intelligent really,
> honestly,. try to have a conversation with a nn
>
> This is what KR does: it helps us to understand what things are and how
> they work
> It also helps us to understand if something is passed for what it is not
> *(evaluation)
> This is is why even neural network require KR, because without it, we don
> know what it is supposed
> to do, why and how and whether it does what it is supposed to do
>
> they still have a role to play in some computation
>
> * DR Knowledge representation in neural networks is not transparent, *
> *PDM I d say that either is lacking or is completely random*
>
>
> DR Neural networks definitely capture knowledge as is evidenced by their
> capabilities, so I would disagree with you there.
>
>
> PDM  capturing knowledge is not knowledge representation, in AI,
> capturing knowledge is only one step, the categorization of knowledge is
> necessary to the reasoning
>
>
>
>
>
>
> *We are used to assessing human knowledge via examinations, and I don’t
> see why we can’t adapt this to assessing artificial minds *
> because assessments is very expensive, with varying degrees of
> effectiveness, require skills and a process -  may not be feasible when AI
> is embedded to test it/evaluate it
>
>
> We will develop the assessment framework as we evolve and depend upon AI
> systems. For instance, we would want to test a vision system to see if it
> can robustly perceive its target environment in a wide variety of
> conditions. We aren’t there yet for the vision systems in self-driving cars!
>
> Where I think we agree is that a level of transparency of reasoning is
> needed for systems that make decisions that we want to rely on.  Cognitive
> agents should be able to explain themselves in ways that make sense to
> their users, for instance, a self-driving car braked suddenly when it
> perceived a child to run out from behind a parked car.  We are less
> interested in the pixel processing involved, and more interested in whether
> the perception is robust, i.e. the car can reliably distinguish a real
> child from a piece of newspaper blowing across the road where the newspaper
> is showing a picture of a child.
>
> It would be a huge mistake to deploy AI when the assessment framework
> isn’t sufficiently mature.
>
> Best regards,
>
> Dave Raggett <dsr@w3.org>
>
>
>
>