- From: Stephen D. Williams <sdw@lig.net>
- Date: Mon, 29 Jul 2019 03:03:51 -0700
- To: paoladimaio10@googlemail.com
- Cc: SW-forum <semantic-web@w3.org>
- Message-ID: <6053f8c0-da83-6863-8ecc-d9c8abe2ec80@lig.net>
Paola, As far as I've been able to determine, there is no universal definition of concept so I find the concept of non-concept to be weakly defined at best. I think 'concept' has a granularity of meaning relative to a particular context, usually more than a triple and less than a large knowledge graph. I intuitively and practically define a concept as a set of elementary statements, facts, or reactions that can be used for prediction or action in a usually cohesive way somewhat separate from other cohesive facts. Good examples would be each mathematical operator, rule, or complete technique. The actual size, in whatever measure, is secondary to the cohesiveness and usual granularity of use. Somewhat orthogonal to this, my research a few years ago led me to an area that doesn't seem to have been explored: The morphology of concepts, or rather the morphology of capturing most or all of the essence of concepts: There is a somewhat finite set of morphologies that can capture the essence of most concepts. This is distinctly a different idea than the seeming (pre-ML boom) universality of triple graphs. Paola, I think you can see a set of triples or logic clauses as being able to represent a concept but are having trouble seeing an equivalence with a trained connectionist network. While, without visualization or similar extensions, it may be difficult to identify the cohesive locality of concepts in a ML network, I don't have so much difficulty in imagining that they are there, interlocking and perhaps haphazardly related. They can still be active in just the same ways, just only indirectly visible. (There are some good examples of building in visualization that sometimes largely indicates what is going on.) I think examples could be useful. Imagine we are trying to capture the logic of traffic and walk lights, sensors, and weekly schedules in a system. We could represent all of the states, events, and rules in first order logic, some equivalent set of triples, direct coding in software. Or we could hand design a logic circuit, timer chips (the 555 TTL etc.), relays, flipflops, etc. Or instead of designing such a circuit, we could assemble more than enough sensors, logic circuits, connection wires, signal lights, etc., just trying every combination in brute force but with no logic or intelligence until we produced a circuit that embodied the logic needed. Or, we could connect all of the sensors, switches, and add some test signals that indicate correct operation (also needed for the brute force solution and useful for testing the logic-based systems) and allow a ML system to converge on a working system through training on the error signal. Both the brute force circuit search and the ML result embody the same logic, the concepts in the explicitly logical approaches, even if they seem somewhat opaque. In a more complex situation, requiring internal or intermediate results that apply in different ways to many other cases, finding a solution with brute force may take a very, very long time to find. ML methods have been evolving to handle more depth and nuance in various ways. Stephen On 7/29/19 1:52 AM, Paola Di Maio wrote: > Stephen > > thank you, I am thinking along those lines and we could have a discussion about > hard vs soft KR > > My challenge today is to find a reference for ''non concept' > (as used here Concept Recognition with Convolutional Neural Networks to Optimize Keyphrase Extraction Andreas Waldis , Luca > Mazzola(B) , and Michael Kaufmann School of Information Technology, Lucerne University of Applied Sciences, 6343 Rotkreuz) > > The notion of non concept seems to be increasingly used in ANN to contrast > 'the notion of concept in traditional KR, > > But I cannot find out if this has been formally defined *I doubt it? > > So no only a lot of peer reviewed scholarly articles make false statements > (say things which are not true) but also reference notions which have never > been formally defined (most of this vague research seems to be happening in Switzerland) > > This is not science, is it? > > Together with other things I am not figuring out, I tind this unsettling and adding > to uncertainties > > > PDM > > On Mon, Jul 29, 2019 at 11:51 AM Stephen D. Williams <sdw@lig.net <mailto:sdw@lig.net>> wrote: > > 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. > > https://www.ted.com/playlists/384/how_your_brain_constructs_real > > 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: > > https://thenextweb.com/artificial-intelligence/2018/08/23/researchers-gave-ai-curiosity-and-it-played-video-games-all-day/ > https://towardsdatascience.com/an-exploration-of-neural-networks-playing-video-games-3910dcee8e4a > > 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. > > Stephen > > 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> <mailto: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) >> >> >> > > -- > > > *Stephen D. Williams* > Founder, Yebo, VolksDroid, Blue Scholar > 650-450-8649 <tel:650-450-8649> | fax:703-995-0407 | sdw@lg.net <mailto:sdw@lig.net> | https://HelloYebo.com | > https://VolksDroid.org | https://BlueScholar.com | https://sdw.st/in > -- *Stephen D. Williams* Founder, Yebo, VolksDroid, Blue Scholar 650-450-8649 <tel:650-450-8649> | fax:703-995-0407 <fax:> | sdw@lg.net <mailto:sdw@lig.net> | https://HelloYebo.com | https://VolksDroid.org | https://BlueScholar.com | https://sdw.st/in
Received on Monday, 29 July 2019 10:04:39 UTC