What do all things have in common?

Posts tagged “Artificial Intelligence

Science As a New Tower of ‘Babble’


Complexity – a patchwork quilt of misunderstanding and confusion tied together ‘by hook or by crook’.

Complex systems are the result of our collective blindness to the simple interconnectedness of our universe.

Why is the emerging view of our universe – no longer a Cosmological and Cosmogonic garden of the good, true and beautiful – now turning into this phantasm of complexity?

Where did we go wrong?
Was it the creation and maintaining of the expectation that we could comprehend and grasp the whole of our Cosmos within one perspective?

Were the applications of the science we created so profit bearing that we began to take more than our fare share?

Was it the tempo at which our scientists – not even slowed down by the ethical and moral considerations which constitute our navigation systems down the roads of evolution – that have brought us to this place much too soon and with so much needless suffering (for animals and humans)?

Are we to continue abandoning our organic (and real) ascendancy for artificial (and synthetic) correlates?

The ends are NOT justified by their means! They are determined by them.

Hiroki Sayama, D.Sc. – Created by Hiroki Sayama, D.Sc., Collective Dynamics of Complex Systems (CoCo) Research Group at Binghamton University, State University of New York

By Brian Castellani (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)%5D, via Wikimedia Commons

“Hunting” for Life in the Universe… Who Speaks For Earth?

Life as we know it

“Hunting” for Life in the Universe… Who Speaks For Earth?
Let’s be careful exactly WHO speaks for us. People who “want to hunt for other life” do that to their own too. Perhaps we need other ‘stewards’ of our world to make first contact with another planetary/galactic civilization.

Whoever ends up owning the world should know that our neighbors ‘out there’ will be very interested in who we are and our history. Will our ‘representatives’ tell those neighbors the truth or will they lie?

Many of the ‘problems’ we have in the world are as artificial as the solutions being manufactured to solve them. Some of the most well-known ‘problems’ are those like the myth of overpopulation (http://overpopulationisamyth.com/overpopulation-the-making-of-a-myth), world poverty, war, terrorism, financial crisis, peak oil,… and soon: peak water (caused by activities such as fracking and the merchandising of water).

Another artificial problem of recent times is known by many names: global cooling (1970s),… ah…, global warming (late 1980s to 2010),… ah…, climate change (Bilderberg 2010).

The ‘stewards’ of our world have been complaining about our problems (in many cases) for a long time now. The question arises as to why, if they were so important, that they haven’t been solved by now? We would be living in the clouds and traveling to the planets if they had done what we trusted them to do in the first place.

Instead they are busily animating the ‘makers and shakers’ of our world with loads of fake money to prematurely build a global civilization whilst running roughshod over all of our personal and collective sovereignty to get there. Those who execute the plans have no idea that they too will be ‘on the menu’ in a later phase after being of use in the earlier ones!

In order to perform this slight-of-hand, they require global problems (such as those above) to provide the thin lines of plausibility to their ends. All the while they have been telling the rest of us that we spread like a virus (like in the film: Matrix).

They believe the lie that “the ends justify the means” when the truth is, the ends are fashioned by their means. All ends are inextricably tied to the means used to arrive at them. We see the results of their lie when they develop technologies too soon, weaponize them and insert control mechanisms into them.

Everywhere there is artificiality: from AI (artificial intelligence) to artificial poverty (austerity); from artificial understanding (category theory) to artificial philosophy (systems theory, chaos theory,…); from artificial physics (Higg’s Boson, Big Bang,…) to artificial biology (synthetic biology in the video); from artificial finance (bankster bail-outs, inside trading, Libor, derivatives, CAFR,…) to fake money (central banking, money as debt); from artificial catastrophes (overpopulation, terrorism, war, financial collapse) to artificial scarcity (zero-sum resource mindset),… even artificial food (margarine, ‘ice cream’, and other fat-free junk, sweeteners,…), artificial people (robots) and artificial diversity (unchecked and unwanted immigration).

This video dovetails all of the aspects above and directs our attention towards premature globalisation arising out of the artificially created chaos around us. I’d like to know if we really want these kinds of people (who are only a small portion of our population) representing us in a galactic or even universal context?

Will our ‘representatives’ be proud of their ‘stewardship’ of humanity? Will they be able to show how they created constructive solutions to aid even the most needy of their own kind? Or will they need to keep a secret so large, that even our neighbors out there wouldn’t want anything to do with us? Humanity will never be trusted in a galactic or even universal context, if we don’t choose our way carefully now.

Complexity At the Cost of Being Simple

Computational ComplexityComplexity At the Cost of Being Simple
There are grievous problems with complexity ‘science’. Some of those problems are apparent here. I will note a few of them.

Reductionism at @13:00 is completely annoying. Epiphenomenological aspects of the problem are completely missing when you reduce into pure binary! It’s like taking you and your emotional life (with its incipient impact on your immune system) and reducing it down to DNA!

“There are way more problems than there are solutions.” @17:00!Sure! When you peel away the contextual embedding of any problem (via reductionism), then you’ve just committed a sort of lobotomy!

The definition of NP at @23:00 while correct, reveals how misguided this theory is. Not all choices are guesses, and correct answers aren’t always ‘lucky’.

Check out the response one receives from the system (algorithm) at @25:11.Did you notice something’s wrong or what?

@26:51 Does anyone notice who is supplying the criterion for the value of ‘correct’? The algorithm is being falsely attributed with properties it can only be endowed with and not arrive at on its own!

@30:00 The rules to Tetris are known by both (algorithm and human) however, the proof of a truth value cannot be computationally arrived at in NP, yet the proof – via a human being AND the skills necessary to ‘prove’ anything can do it in P! It should be obvious that we are going about the whole thing in the wrong way by now!

@31:00 the P<>NP Problem is described. The problem is meaningless and yet you’ll get a Millenium Prize for solving it! (Even sane and not sane find themselves in the balance! Whoa!) If you continue listening to the justification, you might want to be near a bathroom.

@32:27 Check out how NP is being determined to be ‘more’ than P! “Nobody in their right mind…”, “Obviously insane…”,… so naturally NP must be more than P!
Sounds reasonable? I don’t think so…

@32:37 Watch the disappointment: “…very annoying…” and I wonder why? The question is meaningless! Other phrasings of the P<>NP Problem are nothing special and are completely obvious: “You can’t engineer luck.” (Excuse me, but isn’t that the definition of luck in the first place?) and “Solving problems is harder than checking them.”

@34:17 “What could we possibly say… this is all kind of weired…” I don’t know anymore either and I sure hope you don’t tell me! Are we at the end of the lecture already?

@35:53 Now we are getting to the ‘meat of the potato’. If we just “believe in… have faith in…” P<>NP, then Tetris is within NP-P! Wait a minute? That doesn’t sound like any proof to me… perhaps it’s an axiom? We’ll see. It sure looks like begging the question, but I want to be convinced so I’ll just have to wait.

@36:43 He then moves on to a ‘proof’ that looks more like a set of definitions! NP-hard and NP-complete are correctly defined, but they do not prove anything! Tetris and chess act like a definitions, as well!

@40:33 Now he wants to talk about reductions. Wait, weren’t we talking about them already? Let’s take a look…

Yes, we stand upon giants [Authoritarianism]@46:15(Karp’s 3-Partition) and don’t need to think about it anymore and just reconfirm that all NP-complete is reducible to each other! You find some problem that was defined by a “giant” to be a member of your classification and then show that yours is at least as hard @48:47.

If we happen to find a better solution to a member of NP-complete, then either the whole house of cards falls down or we simply reclassify (by reduction) it to P! Now believe it or believe what you want, okay?

There will be a time when we have to revisit mathematics and do a house cleaning of this ‘cuddle muddle’.

‘Something Has To Give!’ – Speaking By Doing…

Behind the Mic‘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!

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).

A Precise Definition of Knowledge – Knowledge Representation as a Means to Define the Meaning of Meaning Precisely


A Precise Definition of Knowledge
Knowledge Representation as a Means to Define the Meaning of Meaning Precisely
Copyright © Carey G. Butler
August 24, 2014

What is this video about?
In this introductory video I would like to explain what knowledge representation is, how to build and apply them. There are basically three phases involved in the process of building a knowledge representation. Acquisition of data (which includes staging), collation and the representation itself. The collation and the representation phases of the process are mentioned here, but I will explain them further in future videos.

You are now watching a simulation of the acquisition phase as it collects and stores preliminary structure from the data it encounters in terms of the vocabulary contained within that data. Acquisition is a necessary prerequisite for the collation phase following it, because the information it creates from the data are used by the collation algorithms which then transform that information into knowledge.

The statistics you are seeing tabulated are only a small subset of those collected in a typical acquisition phase. Each of these counters are being updated in correspondence to the recognition coming from underlying parsers running in the background. Depending upon the computer resources involved in the
acquisition, these parsers may even even run concurrently as is shown in this simulation.

The objects you see moving around in the video are of two different kinds: knowledge fields or knowledge molecules. Those nearest to you are the field representations of the actual data being collected called knowledge fields. They could represent an individual symbol, punctuation, morpheme, lexeme, word, emotion, perspective, or some other unit of information in the data. Each of them contain their own signature – even if their value, state or other intrinsic properties are unknown or indeterminate during the acquisition.

Those farther away from the view are clusters of fields which have already coalesced into groups according to shared dynamically adaptive factors such as similarity, relation, ordinality, cardinality,…
These ‘molecules’ also contain their own set of signatures and may be composed of a mixture of fields, meta-fields and hyper-fields that are unique to all others.The collation phase has the job of assigning these molecules to their preliminary holarchical domains which are then made visible in the resulting knowledge representation. Uniqueness is preserved even if they contain common elements with others in the domain they occupy. Clusters of knowledge molecules and/or fields grouped together are known as ‘knowledge domains’, ‘structural domains’,’dynamical domains’ or ‘resonance domains’, depending upon which of their aspects is being emphasized.

We now need a short introduction to what knowledge representation is in order to explain why you’re seeing these objects here.

What is Knowledge Representation?
Knowledge representation provides all of the ways and means necessary to reliably and consistently conceptualize our world. It helps us navigate landscapes of meaning without losing our way; however, navigational bearing isn’t the only advantage. Knowledge representation aids our recognition of what changes when we change our world or something about ourselves. It does so, because even our own perspective is included in the representation. It can even reveal to us when elements are missing or hidden from our view!

It’s important to remember that knowledge representation is not an end, rather a means or process that makes explicit to us everything we already do with what we come to be aware of. A knowledge representation must be capable of representing knowledge such that it, like a book or other artifact, brings awareness of that knowledge to us. When we do it right, it actually perpetuates our understanding by providing a means for us to recognize, interpret (understand) and utilize the how and what we know as it relates to itself and to us. In fact – knowledge representation even makes it possible to define knowledge precisely!

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? Their perfectly valid structure and dynamics are ignored by classifying them as something else than what they are. Differences in culture or language make 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 catalogs, 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 objects to which it corresponds! The knowledge is not coming from the data itself, it’s always coming from the observer of the data – even if that observer is an algorithm!

Therefore ontology-based semantic analysis can only produce the artifacts 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 acknowledgment. In fact knowledge cannot even be completely systematized, it can only be interacted with using ever increasing precision!

  • What is knowledge then?
    Knowledge is what awareness does.
    • Awareness of some kind and at some level is the only prerequisite for knowledge and is the substrate upon which knowledge is generated.
    • Awareness coalesces, interacts with and perpetuates itself in all of its form and function.
    • Awareness which resonates (shares dynamics) at, near, or in some kind of harmony (even disharmony) with another tends to associate (disassociate) with that other in some way.
    • These requisites of awareness hold true even for objects that are infinite or indeterminate.
    • This is why knowledge, the meaning of meaning, can be precisely defined and even provides its own means for doing so.
    Knowledge is, pure and simply: the resonance, structure and dynamics of awareness as it creates and discovers for and of itself.
    • Awareness precedes meaning and provides the only fundamentally necessary and sufficient basis for meaning of meaning expressing itself as knowledge.
    Knowledge is the dialog between participants in awarenesseven if that dialog appears to be only one-way, incoherent or incomplete.
    • Even language, mathematics, philosophy, symbolism, analogy, metaphor and sign systems can all be resolved to this common denominator found at the foundation of each and every one of them.

More information about the objects seen:
The objects on the surface of the pyramid correspond to basic structures denoting some of the basic paradigms that are being used to mine data into information and then collate that information into knowledge. You may notice that their basic structures do not change, only their content does. These paradigms are comprised of contra-positional fields that harmonize with each other so closely that they build complete harmonic structures. Their function is similar to what proteins and enzymes do in our cells.

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