## Strictly Speaking Can’t! Natural Language Won’t?

Physics is only complex, because it’s in someone’s interest to have it that way. The way to understanding, even if you don’t understand science, was paved with words. Even if those words led only to a symbolic form of understanding.

Common ordinary language is quite capable of explaining physics. Mathematics is simply more precise than common language. Modern Mathematics pays the price for that precision by being overly complex and subservient to causal and compositional relations. These are limitations that metaphysics and philosophy do not have.

Words in language have a structure that mathematics alone will never see as it looks for their structure and dynamics in the wrong places and in the wrong ways. Modern pure mathematics lacks an underlying expression of inherent purpose in its ‘tool set’.

With natural language we are even able to cross the ‘event horizon’ into interiority (where unity makes its journey through the non-dual into the causal realm). It is a place where mathematics may also ‘visit’ and investigate, but only with some metaphysical foundation to navigate with. The ‘landscape’ is very different there… where even time and space ‘behave’ (manifest) differently. Yet common language can take us there! Why? It’s made of the ‘right stuff’!

The mono-logical gaze with its incipient ontological foundation, as found in (modern) pure mathematics, is too myopic. That’s why languages such as Category Theory, although subtle and general in nature, even lose their way. They can tell us how we got there, but none can tell us why we wanted to get there in the first place!

It’s easy to expose modern corporate science’s (mainstream) limitations with this limited tool set – you need simply ask questions like: “What in my methodology inherently expresses why am I looking in here?” (what purpose) or “What assumptions am I making that I’m not even aware of?” or “Why does it choose to do that? and you’re already there where ontology falls flat on its face.

Even questions like these are met with disdain, intolerance and ridicule (the shadow knows it can’t see them and wills to banish what it cannot)! And that’s where science begins to resemble religion (psyence).

Those are also some of the reasons why philosophers and philosophy have almost disappeared from the mainstream. I’ll give you a few philosophical hints to pique your interest.

Why do they call it Chaos Theory and not Cosmos Theory?
Why coincidence and not synchronicity?
Why entropy and not centropy?

Why particle and not field?
(many more examples…)

## Are sets, in an abstract sense, one of the most fundamental objects in contemporary mathematics?

Yes and no.

The equivalence relation lies deeper within the knowledge representation and it’s foundation.

There are other knowledge prerequisites which lie even deeper within the knowledge substrate than the equivalence relation.

The concepts of a boundary, of quantity, membership, reflexivity, symmetry, transitivity, and relation are some examples.

http://bit.ly/2wPV7RN

## Universal Constants, Variations, and Identities #19 (Inverse Awareness)

Universal Constants, Variations, and Identities
#19 The Inverse Awareness Relation

The Inverse Awareness Relation establishes a fundamental relationship in our universe:

Micro Awareness = ```$\dfrac{1}{scope}$ and Macro Awareness = $\dfrac{1}{depth}$ or $\dfrac {Micro Awareness}{Macro Awareness} = \dfrac{depth}{scope}$ Which essentially state: The closer awareness is in some way to an entity, the more depth and the less scope it discerns. The farther awareness is in some way to an entity, the more scope and the less depth it discerns. (Be careful, this idea of closeness is not the same as distance.) ```
``` May 15, 2017 | Categories: Discernment, Holons, Holors, Hyperbolic Geometry, Identities, Insight, Knowledge, Knowledge Representation, Language, Learning, Linguistics, Mathesis Generalis, Mathesis Universalis, Metamathematics, Metaphysics, Philosophy, Philosophy of Language, Philosophy of Learning, Philosophy Of Mind, Semantic Web, Semantics, Understanding, Universal Constants, Variations, and Identities, Variations, Wisdom | Tags: knowledge, Language, learning, Linguistics, LogicaUniversalis, Mathesis Universalis, Philosophia Universalis, Philosophy, Philosophy of Language, Philosophy of Learning, Philosophy Of Mind, understanding | Leave a comment ```
``` Is Real World Knowledge More Valuable Than Fictional Knowledge? No. Here an excerpt from a short summary of a paper I am writing that provides some context to answer this question: What Knowledge is not: Knowledge is not very well understood so I’ll briefly point out some of the reasons why we’ve been unable to precisely define what knowledge is thus far. Humanity has made numerous attempts at defining knowledge. Plato taught that justified truth and belief are required for something to be considered knowledge. Throughout the history of the theory of knowledge (epistemology), others have done their best to add to Plato’s work or create new or more comprehensive definitions in their attempts to ‘contain’ the meaning of meaning (knowledge). All of these efforts have failed for one reason or another. Using truth value and ‘justification’ as a basis for knowledge or introducing broader definitions or finer classifications can only fail. I will now provide a small set of examples of why this is so. Truth value is only a value that knowledge may attend. Knowledge can be true or false, justified or unjustified, because knowledge is the meaning of meaning What about false or fictitious knowledge? [Here’s the reason why I say no.] Their perfectly valid structure and dynamics are ignored by classifying them as something else than what they are. Differences in culture or language even make no difference, because the objects being referred to have meaning that transcends language barriers. Another problem is that knowledge is often thought to be primarily semantics or even ontology based. Both of these cannot be true for many reasons. In the first case (semantics): There already exists knowledge structure and dynamics for objects we cannot or will not yet know. The same is true for objects to which meaning has not yet been assigned, such as ideas, connections and perspectives that we’re not yet aware of or have forgotten. Their meaning is never clear until we’ve become aware of or remember them. In the second case (ontology): collations that are fed ontological framing are necessarily bound to memory, initial conditions of some kind and/or association in terms of space, time, order, context, relation,… We build whole catalogues, dictionaries and theories about them: Triads, diads, quints, ontology charts, neural networks, semiotics and even the current research in linguistics are examples. Even if an ontology or set of them attempts to represent intrinsic meaning, it can only do so in a descriptive ‘extrinsic’ way. An ontology, no matter how sophisticated, is incapable of generating the purpose of even its own inception, not to mention the purpose of the objects to which it corresponds. The knowledge is not coming from the data itself, it is always coming from the observer of the data, even if that observer is an algorithm. Therefore ontology-based semantic analysis can only produce the artefacts of knowledge, such as search results, association to other objects, ‘knowledge graphs’ like Cayley,… Real knowledge precedes, transcends and includes our conceptions, cognitive processes, perception, communication, reasoning and is more than simply related to our capacity of acknowledgement. In fact knowledge cannot even be completely systematised; it can only be interacted with using ever increasing precision. [For those interested, my summary is found at: A Precise Definition of Knowledge – Knowledge Representation as a Means to Define the Meaning of Meaning Precisely: http://bit.ly/2pA8Y8Y May 11, 2017 | Categories: Consciousness, Insight, Knowledge, Language, Learning, Linguistics, Mathesis Generalis, Mathesis Universalis, Metamathematics, Metaphysics, Philosophy, Philosophy of Language, Philosophy of Learning, Philosophy Of Mind, Semantic Web, Semantics, Understanding, Wisdom | Tags: Big Data, Characteristica Universalis, insight, knowledge, Knowledge Representation, Language, learning, Linguistics, Logica Universalis, Mathematica Universalis, Mathesis Universalis, Metaphysica Universalis, Metaphysics, Philosophia Universalis, Philosophy, Philosophy of Language, Philosophy of Learning, Philosophy Of Mind, Scientia Universalis, Semantic Web, Semantics, understanding, wisdom | Leave a comment Strictly Speaking Can’t! Natural Language Won’t? Physics is only complex, because it’s in someone’s interest to have it that way.  The way to understanding, even if you don’t understand science, was paved with words. Even if those words led only to a symbolic form of understanding. I’m a mathematician and can tell you that common ordinary language is quite capable of explaining physics. Mathematics is simply more precise than common language. It pays the price for that precision by being subservient to the causal and compositional relations. These are limitations that metaphysics and philosophy do not have. Words in language have a structure that mathematics alone will never see as it looks for their structure and dynamics in the wrong places and in the wrong ways. Pure mathematics lacks an underlying expression of inherent purpose in its ‘tool set’. With natural language we are even able to cross the ‘event horizon’ into interiority (where unity makes its journey through the non-dual into the causal realm). It is a place where mathematics may also ‘visit’ and investigate, but only with some metaphysical foundation to navigate with. The ‘landscape’ is very different there… where even time and space ‘behave’ (manifest) differently. Yet common language can take us there! Why? It’s made of the ‘right stuff’! The monological gaze with its incipient ontological foundation, as found in pure mathematics, is too myopic. That’s why languages such as category theory, although subtle and general in nature, even lose their way. They can tell us how we got there, but none can tell us why we wanted to get there in the first place! It’s easy to expose modern corporate science’s (mainstream) limitations with this limited tool set – you need simply ask questions like: “What in my methodology inherently expresses why am I looking in here?” (what purpose) or “What assumptions am I making that I’m not even aware of?” or “Why does it choose to do that? and you’re already there where ontology falls flat on its face. Even questions like these are met with disdain, intolerance and ridicule (the shadow knows it can’t see and wills to banish what it cannot)! And that’s where science begins to resemble religion (psyence). Those are also some of the reasons why philosophers and philosophy have almost disappeared from the mainstream. I’ll give you a few philosophical hints to pique your interest. Why do they call it Chaos Theory and not Cosmos Theory? Why coincidence and not synchronicity? Why entropy and not centropy? … Why particle and not field? (many more examples…) Dec 2, 2014 | Categories: Insight, Knowledge, Knowledge Representation, Language, Learning, Linguistics, Mathematics, Mathesis Universalis, Physics, Psyence, Science, Semantics, Understanding, Wisdom | Tags: BadScience, insight, knowledge, Knowledge Representation, learning, Linguistics, organic intelligence, Philosophy of Language, Philosopohy of Mind, Psyence, Semantics, understanding, wisdom | Leave a comment Wonderful Graphics To Save a Disastrous Script The ‘Science’ behind the movie ‘Lucy’ cannot hold a candle to the graphics that ‘sell’ that script! Oct 18, 2014 | Categories: Big Data, Language, Linguistics, Mathesis Universalis, Semantics | Tags: insight, knowledge, learning, Linguistics, Philosophy of Language, Philosopohy of Mind, Semantics, understanding, wisdom | Leave a comment ‘Something Has To Give!’ – Speaking By Doing… ‘Something Has To Give!’ Speaking by doing… I appreciate the technology (as long as it isn’t weaponized) and even admire what has been accomplished thus far. Just I know that to get to the ‘promised land’, they’re going to need to transcend and include the ontologically-based methodologies as are shown in the video! One trip to Google translate reveals this to be mere hype at present. Hidden Markov models aren’t going to do it, people! That’s like trying to do a radar scan of the ocean and only seeing things you’ve been told to see beforehand. Their example involves capital cities and the meta-framing necessary to differentiate them. Essentially they are building structures (like fingerprints) of ideas and trying to do an ‘algebra’ with them. The AI paradigm must be ‘fortified’ by epistemologically-based perspectives and methodologies, before we can even think of cognition. Clearly they are already involved in the recognition process, but these missing elements in in artificial intelligence is originating from those doing the work in the video (through their intentions, desires, success criterion,…) without their even noticing it! (Or if they do, they don’t make that clear to the viewer.) Also, they believe in the mysticism that we need only create the necessary initial conditions (like a soup) and then, through emergence (which they cannot define precisely), intelligence (like life) will pop out! ヽ(•́o•̀)ノ They will most certainly manage to get the technology to a point that it will become useful (after they’ve shelled out huge sums of money to get there), but they will never reach cognition this way. They will have to part with one of their most sacred dogmas first: the mind is the brain. The brain is only a part of what we call mind. Our whole bodies are involved with the dialog of mind – from our brains right down to our digestive tracts and even cells (and their constituents). Oct 18, 2014 | Categories: Insight, Knowledge, Language, Linguistics, Mathesis Universalis, Semantics, Wisdom | Tags: Artificial Intelligence, insight, knowledge, learning, Linguistics, methodologies, promised land, Semantics, technology, understanding, voice recognition, wisdom | Leave a comment Knowledge Is What Awareness Does – Knowledge Representation as a Means to Define Meaning Precisely Knowledge Representation as a Means to Define Knowledge Precisely Video is finally here! Sep 3, 2014 | Categories: Artificial Intelligence, BigData, Holons, Holors, Insight, Knowledge, Knowledge Representation, Language, Learning, Linguistics, Logic, Mathematics, Mathesis Universalis, Metaphysics, Philosophy, Semantics, Understanding, Wisdom | Tags: ArtificialIntelligence, insight, knowledge, Language, learning, Linguistics, OrganicIntelligence, Philosopohy, Semantics, understanding, wisdom | Leave a comment ```
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