Re: perfect knowledge in AI

I never implied completeness to be equivalent to logic. It is exactly logic as a foundation for mathematics that was proven by Godel to be at the center of incompleteness.
The point I was trying to make that there are currently many roads converging onto a central crossing of science, new discoveries by the Large Hadron Collider pointing to a possible break with the Standard Model of Particle Physics, new astronomy and astrophysics findings suggesting a rewrite of general relativity.The two most successful theories of all quantum mechanics and the Standard Model of Particle Physics seem to be up for revision, and may imply a shake-up of all basic physics theories, including dark energy, dark matter and information conservation in black hole physics.
All the knowledge we currently have was arrived at using describable processes of Scientific Discovery including all processes that are not logical.
From Buddhist philosophers to nineteenth and twentieth century philosophers all have weighed in on what is knowledge, what is consciousness, and what is cognition, and what role do our senses play in this, what is the role of perception, the role of language to record knowledge.
To this we add the ever growing field of neuro sciences and information sciences.
The groundbreaking artcile MIP*=RE (https://arxiv.org/abs/2001.04383), basically wipes off the table any uniform scalable to infinity framework for a mathematical basis for machine learning for anything, as finite dimensional matrices cannot represent or approximate theories that intend to model physical reality.
Thus machine learning is a dead end.
Most of the current efforts in coming up with "grand theories for the unification of mathematics"stem from work done by Alexander Grothendieck and others, and in the current Langlands Program. All of these efforts have a direct bearing on current physics, and highly specialized areas like string theory, quantum field theories etc.
If we want to arrive at knowledge representation frameworks for non-biased, open, accountable and explainable artificial (general) intelligence we must factor in (1) symbolic representation, (2) deductive reasoning, logic (3) linguistic representation, (4) sensory input of spatio-temporal events, objects and processes and corresponding uncertainty issues, (5) philosophical issues, such as the acceptance or rejection of the generalized concept of the axiom of choice, deontic, ontological and epistemological, deontology versus utilitarianism issues, and the more fundamental discussion about reality, objects and description and perception thereof in this reality and what roles to attribute to consciousness, cognition and knowledge.
In my humble opinion any perfect knowledge representation theory must conserve causality, have a formal reasoning process that has a clear cut and well defined structure to determine truths, and must be clear and well defined in how inputs are acquired, compared and processed, and how outputs are produced, recorded, stored and retrieved or edited.
Three additional general concepts may be added, being invariance, conformity and similarity which have well defined terms in many fields of mathematics, to describe interrelationships between theories.



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    On Wednesday, May 11, 2022, 06:27:31 AM AST, Dave Raggett <dsr@w3.org> wrote:  
 
 Logic works with perfect knowledge, but that is often impractical. We are always learning and never attain perfect knowledge and out of necessity have to deal with uncertain, incomplete and inconsistent knowledge.  We become aware of inconsistencies (cognitive dissonance) and then have to decide whether and how to revise our goals and/or our beliefs.  Prior knowledge allows us to make plausible inferences that go beyond what’s possible with deductive logic. This includes higher order reasoning for which logic scales poorly, something key to social reasoning. Prior knowledge is here taken to mean statistical knowledge, e.g. the chance of it being sunny tomorrow if today is cloud, and qualitative metadata, e.g. most birds can fly.
If you’re interested take a look at Burstein, Collins and Baker (1991) 
 http://www.bursteins.net/mark/docs/burstein-collins-baker91.pdf 

On 11 May 2022, at 03:09, Paola Di Maio <paoladimaio10@gmail.com> wrote:
Well, even simply language and communication andthe simplest reasoning require logic of course,  nobody can dis that.Buta) logic itself depends on other things being true, such as fact checking, There was a great un example once on ontolog list, where the first order logic was being demonstrated (if  fred is a bat and  bats are birds, then fred is a bird,)  that turned out a wrong answer because the premise was false (bats  not birds but mammals). It was a good lesson, showing that the logic is right but the fact is wrong 

b) there is a  probably a lot more than logic to achieve perfection, I posted an example on this list about the dying woman who, as last wish asked someone to take her urine to the first person who entered the gate in such and such place, who then saved her life. Such a request may not sound logical at first, but probably her intuition was greater than anybody s logic could have been, which became apparent at the end (the first person who crossed the gate was a doctor who figured out the poison)Here is the posthttps://lists.w3.org/Archives/Public/public-aikr/2021Feb/0012.html
I dont know if that is what DR intended in his reply, but I  would agree that even logic rests on correct and relevant facts, and possibly other things


Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
W3C Data Activity Lead & W3C champion for the Web of things