- From: Stephen D. Williams <sdw@lig.net>
- Date: Mon, 29 Jul 2019 10:25:30 -0700
- To: hans.teijgeler@quicknet.nl, paoladimaio10@googlemail.com
- Cc: 'SW-forum' <semantic-web@w3.org>
- Message-ID: <e5acbc28-ac75-43b6-32e8-77bdb055ef5b@lig.net>
Hans, Yes: I think for most people most of the time, defining a concept always results in paragraphs of natural language. This implies that, beyond has-a, is-a, etc., it is difficult to express the ideas of a concept without relying on the complex web of understanding of other concepts. And that, beyond ontologies like foaf, we don't have enough shared context of our webs of understanding to express many concepts in a definitive or directly machine understandable way. If anyone has a better definition and grounding for the concept of concepts, please enlighten me. It is difficult to research completely based on such a common and widely used word. Part of my thinking with the concept morphology thinking is that it is helpful to factor out as much concrete data as possible from a concept, clarifying what part is simple data relative to the understanding & web of understanding references. I was looking at this from a human training point of view, although i think it is instructive from the KR / KU (knowledge understanding) points of view: We are using 3D gaming to enhance training, making concepts more memorable. [1] My question: Can an adaptive, templatized, polymorphic game chassis adapt, in one or more senses, to different concepts. For concepts where the concept itself is easily understood and remembered and it is the data that is difficult to remember, and where that data has a morphology similar to other concepts, the answer is yes. My go-to example is concept morphologies such as 'hierarchy": Maslow's Hierarchy of needs has a similar form as recommended food groups or levels of architecture. Although they are completely different concepts, the hard to remember parts are data that can be captured in the same simple way. I a sense, what is left is a concentration of the concept, the extract of knowledge. For example, understanding the concepts of a movie, actors plots, directors, plot, etc. is separate from a knowledgebase of actual movies, actors, directors, and plots. Although the knowledge that the concept extract relies on may seem infinite, the concept extract itself (What is a movie?) is very finite and usually static while the concept data (What are all movies?) may be large and always growing. The tricky bit is representing or even referring to the essence of a concept, this concept extract, separate from its data. I can see several ways this is and can be done, but nothing seems satisfyingly complete: Simpler math & logic concepts are directly representable and sometimes even derivable although more complex concepts seem less so, but are far removed from most concepts. Relational operators, including things like Allen's Interval Algebra can be built into a system as fundamental and highly reusable features. But nothing explicit can represent more than a small range of concept building blocks, except in the most elementary and shallow way as in semantic web triples, perhaps used by first order logic or similar implication systems: Implication logic systems, even with the addition of probabilistic graph reasoning, only implement a subset of the types of knowing and understanding. Efforts to build large, complete triplestores, ontologies, and encyclopedia knowledge graphs are useful in certain ways now and may become very useful when we find ways to build more understanding depth, but uses now are usually just database queries. There are a lot of ways to think about AI / ML et al. One of the most useful to me is that an AI / ML system exists and is effective if it can predict accurate results based on inputs and its internal KR. Next levels include predicting sequences and time based events, learning and remembering on the fly, and various types of deeper reasoning, but not just apparent reasoning that turns out to be less-useful a shallow correlation. But, basically, an AI/ML system is a prediction machine for something more complex, probabilistic, and/or noise tolerant than a solution built from Algorithms book algorithms. As humans, we find it satisfying to capture explicit knowledge in more broadly applicable ways, such as knowledge graphs. There are many ways we can make that useful, especially with a human in the loop doing searches etc. But we continue to have a gap between that kind of KR and a useful and automatic prediction machine. Symbolic / logical / computationalist / reductionist / bottom-up (where the bottom is usually symbolic statements) / mostly deterministic vs. connectionist / analogical / holistic (global, sort of top-down) / emergent (bottom up from a neuron-like element) / mostly probabilistic / learning: There is a lot to be said about these alternatives. I think there is value in both / all. Any real system is going to have elements of both, perhaps in various senses, just like we ourselves do. Some interesting related links, although a couple of them seem very obsolete: https://www.simplypsychology.org/reductionism-holism.html https://psychology.wikia.org/wiki/Connectionism https://www.jstor.org/stable/27759324?seq=1#page_scan_tab_contents Connectionism, Reduction, and Multiple Realizability https://web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html https://journals.sagepub.com/doi/abs/10.1177/092137408800100102 Reductionism, Connectionism, and the Plasticity of Human Consciousness https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2395673 Who's Afraid of Multiple Realizability?: Functionalism, Reductionism, and Connectionism The exciting thing about ML related techniques is that they can already perform an interesting range of actual, useful prediction, in many cases from highly noisy, messy raw data all the way to final decisions and actions. We see the limitations, so far, of incorporating, leveraging, or an alternative for the KR in a number of interesting KBs. But some ML successes are encroaching on that territory in impressive ways. There is nothing definitive that prevents us from expecting that to continue to grow. I expect that soon there will be an interesting meeting of [1] Yebo (formerly Change My Path) https://cped.co/d Stephen On 7/29/19 4:15 AM, hans.teijgeler@quicknet.nl wrote: > > Stephen, > > Isn’t it so that, no matter how elementary you go, you always end up with an explanation in a natural language of those > information elements? > > Regards, > > Hans > > 15926.org <http://data.15926.org/> > > ______________________________________________-- > > *From:*Stephen D. Williams <sdw@lig.net> > *Sent:* maandag 29 juli 2019 12:04 > *To:* paoladimaio10@googlemail.com > *Cc:* SW-forum <semantic-web@w3.org> > *Subject:* Re: Hard vs. Soft or Hard + Soft KR, Re: neural networks being purported as KR? > > 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... > > 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 > > > <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virusvrij. > www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> > > <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> -- *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 17:25:59 UTC