Hard vs. Soft or Hard + Soft KR, Re: neural networks being purported as KR?

Thanks, great info!

Knowledge graphs, semantic web, and other explicit knowledge representations are important.  But, as you point out, they currently 
don't capture everything, especially not probability and complex rational but not simple logic relationships.  ML and related 
enabled training, capture, and usage of this aspect of information, but most or all structure is opaquely inferred and usually 
shallow.  Somehow we need all of the capabilities of both in a merged or married structure.  I totally believe in the value of both.

My observation is that we almost certainly reason with structure, logic, math in general, language, etc. on top of ML-like 
mechanisms (albeit far more varied, numerous, and expansive), and we fill in the gaps all the time with ML-like sub-rational or at 
least sub-logical reasoning.  Most of us most of the time reason largely in this sub-logical reasoning mode, often interpreting, 
viewing, or fitting those thoughts and conclusions to formal structures afterwards or in a trailing overlapping fashion.


As a software & security architect and now robotic hardware designer, I never think in formal equations or systems, yet I 
automatically explore creative solution spaces with many, many constraints and patterns in mind.  I can often construct equations of 
various types to summarize and communicate.  I now find this to be true of business and interpersonal situations (various concerns, 
constraints, interests, conflicting aspects) and mechanical systems.  For instance, this is a summary of part of the robotic 
actuator I've been working on:

I1 * r/R * CVT - I2 * R/2 = RIVT

I never could have found that solution through explicit logical deduction, yet the process of working through and mentally testing 
possibilities is very concrete and not mysterious at all.  How do you experience your knowledge, thinking, problem solving, and 
creativity?  Do you feel strongly and deeply aware of it or is it mysterious and opaque?  Do you visualize a little, a lot, or not 
at all?  (We're recently realized that something like 10% of people cannot visualize anything, ever, yet a number of animators are 
among those!  They seem to use drawing as external working memory / thought space.)

We already have ML solutions that are able to learn far more than simple table lookups.  Creating a program to play a video game 
using strictly explicit logic, KG methods would be challenging while we have straight foward ML solutions already:


It seems promising to extrapolate from these for some distance.  Our challenge is to find a intersection method that allows us to 
combine these two mostly independent paths to create really interesting solutions.  My sense is that there are two possibilities:

We get an interface method working that allows an ML system to access computational algorithms / KG / semantic / logical knowledge 
through a kind of port, portal, map, or similar.

Or that we evolve the ML systems to the point where they can embody, in a reasonably efficient and accurate way, this computational 
/ KG / semantic / logical knowledge along with the more probabilistic, analog networked knowledge.

Or both.

Rather than connectionist vs. reductionist etc., perhaps these are useful labels:

Hard knowledge: computational algorithms / KG / semantic / logical knowledge

Soft Knowledge: ML, vision, everything connectionist or probabilistic

Or sharp vs. round or curved or smooth.


On 7/26/19 9:02 AM, ProjectParadigm-ICT-Program wrote:
> Thank you Paola for pointing this out.
> Again I must beat the drum.
> Knowledge is much more than extracting structure from facts and data. If I just recall that the collection of facts is subject to 
> the uncertainty principle, any structure deduced cannot be complete, and the application of free will, and/or axiom of choice 
> create a dichotomy, knowledge is much more.
> We are limited by our sensory apparatus, our hard wiring in our human brain, including the shortcuts made when processing visual 
> data, and the limitations of natural language.
> I agree that knowledge reasoning should be fairly straightforward, but making the jump from KR to knowledge itself implies we come 
> up with some consistent many worlds modeling scheme in which the virtual, mathematical and (many interpretations of) the physical 
> world coexist, reconciling incompleteness, uncertainty principle, sensory limitations and application of free will and choice.
> A convergence of efforts by string theorists, researchers in human brain cognitive and biological structure fields, theoretical 
> physicists and mathematicians working on finite groups, category theory, algebraic topology and logical structures for consistent 
> super theories, and an odd mix of linguists and philosophers (including Buddhists) is doing just that.
> But they are far from a consensus.
> The point I am trying to make is that KR is more than semantics and ontologies and knowledge graphs, graphs, category theory 
> diagrams and Feynmann diagrams and any other visualization tools we use.
> The implicate order David Bohm theoreticized underlying quantum reality and the reality of our physical world, cannot be captured 
> by some mix of formal logic, semantic structures, ontologies or computable frameworks.
> And we we want someday A(G)I to be able to grasp human knowledge in general, we must create a growth path towards formal 
> structures which have meta-layers above (knowledge) graphs, formal logic and ontologies.
> Mathematically speaking, using formal logic, ontologies and generalized graphs is necessary but insufficient for this general 
> formal structure.
> And now I must add that deep learning and machine learning also fall short in terms of KR'
> If we let computer scientists, logicians, mathematicians and software engineers try to come up with KR which is fit for the AI we 
> envision we will need for future applications we will fail miserably.
> We need neuroscientist, and specialists in the field of cognitive sciences, biologists and even psychologists, and philosophers 
> and physicists to help us complete the general framework for knowledge, and to establish which parts can be effectively captured 
> in a formal fashion, which provide suitable technologies and tools for KR.
> Mike Bergman did a nice expose on knowledge graphs at:
> A Common Sense View of Knowledge Graphs <http://www.mkbergman.com/2244/a-common-sense-view-of-knowledge-graphs/>
>     A Common Sense View of Knowledge Graphs
> This article, based on a comprehensive history and definitions of the concept, provides a common-sense view of h...
> <http://www.mkbergman.com/2244/a-common-sense-view-of-knowledge-graphs/>
> But historically even mandalas qualify as knowledge graphs, in a very stylized way. And they can be used to visualize very complex 
> mathematical structures without the use of edges or arrows, thus removing the time component associated with the transition the 
> edge or arrow represents, making knowledge representation in a time-independent fashion possible.
> 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, July 25, 2019, 11:52:23 PM ADT, Paola Di Maio <paola.dimaio@gmail.com> wrote:
> Sorry to bang on this topic, but its the task at hand at the moment
> I just found an article, which is good scientific survey then  purports NN as a type of KR
> (casually sneaks in NN as the latest KR)
> This is published in a Springer peer reviewed publication and my makes all of my hairs stand up on my head
> This is the kind of rubbish that without further qualification is being passed down
> as the latest research, and  which the future generations of AI scientists are being fed-
> wonder if anyone else has a problem with this proposition
> (sign of the times?)
> I am doing my best within my means to identify and contain this peril
> Article https://link-springer-com.nls.idm.oclc.org/article/10.1007/s00170-018-2433-8
> A survey of knowledge representation methods and applications in machining process planning
> The machining process is the act of preparing the detailed operating instructions for changing an engineering design into an end 
> product, which involves the removal of material from the part. Today, machining ...
> Xiuling Li, Shusheng Zhang, Rui Huang… in The International Journal of Advanced Manu… (2018)

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Received on Monday, 29 July 2019 03:42:41 UTC