What I’m going to say is going to be unpopular, but I cannot reconcile my own well-being without giving you an answer to this problem from my perspective.
My only reason for reluctantly writing this, knowing what kind of reaction I could receive is, because I abhor that some of the best minds on our planet are occupying themselves with this problem. It pains me to no end to see humanity squandering its power for a problem that, as it is currently framed, is unanswerable. It goes further than this though. There will come a time when questions such as this one will be cast upon the junk heap of humanity’s growth throughout history. It will take its rightful place along such ideas as phrenology.
Here’s why I say this:
The problem is firmly and completely embedded in Functional Reductionism. I say this, because the problem’s framing requires us to peel away the contextual embedding of the problems for which it is supposed to clarify.
This is just one of its problems. Here’s another:
Since the data for this problem (and those like it) are themselves algorithms, they are compelled to be functionally reduced versions of mind problem solving (varying types of heuristics and decision problems) which reduces the problem’s causal domain and its universe of discourse even further. How can a specification based upon functionally reduced data be again used as data for the problem’s solution in the first place?
That means that this problem has no independent existence nor causal efficacy. Everywhere I have looked at this problem, the definitions of NP-Hard and NP-Complete do not lead to proving anything useful. We cannot ‘generalise’ the mind by reducing it to some metric of complexity. Complexity is also not how the universe works as Occam’s Razor shows.
I am prepared to defend my position should someone have the metal to test me on this. Another thing: I wish I could have left this alone, but we all need to wake up to this nonsense.
 http://bit.ly/2GHbRkW How Occam’s Razor Works
Is Mathematics Or Philosophy More Fundamental?
Answer: Philosophy is more fundamental than mathematics.
This is changing, but mathematics is incapable at this time of comprehensively describing epistemology, whereas, philosophy can.
Hence; mathematics is restrained to pure ontology. It does not reach far enough into the universe to distinguish anything other than ontologies. This will change soon. I am working on exactly this problem. See http://mathematica-universalis.com for more information on my work. (I’m not selling anything on this site.)
Also, mathematics cannot be done without expressing some kind of philosophy to underlie any axioms which it needs to function.
Implication is a ‘given’ in mathematics. It assumes a relation which we call implication. Mathematics certainly ‘consumes’ them as a means to create inferences, but the inference form, the antecedent, and the consequent are implicit axioms based upon an underlying metaphysics.
Ergo: philosophy is more general and universal than mathematics.
Often epistemology is considered separate from metaphysics, but that is incorrect, because you cannot answer questions as to ‘How do we know?” without an underlying metaphysical framework within which such a question and answer can be considered.
This work is a dead end waiting to happen. Of course it will attract much interest, money, and perhaps even yield new insights into the commonality of language, but there’s better ways to get there.
What’s even more sad is that they, who should know better, will see my intentions in making this clear as destructive criticism instead of a siren warning regarding research governed/originating through a false paradigm. These people cannot see or overlook the costs humanity pays for the misunderstandings research like this causes and is based upon.
It’s even worse in the field of genetic engineering with their chimera research. The people wasting public money funding this research need to be gotten under control again.
I don’t want to criticize the researcher’s intentions. It’s their framing and methodology that I see as primitive, naive, and incomplete.
I’m not judging who they are nor their ends; rather, their means of getting there.
“Quantification” is exactly the wrong way to ‘measure/compare semantics; not to mention “partitioning” them!
1) The value in this investigation that they propose is to extrapolate and interpolate ontology. Semantics are more than ontology. They possess a complete metaphysics which includes their epistemology.
2) You cannot quantify qualities, because you reduce the investigation to measurement; which itself imposes meaning upon the meaning you wish to measure. Semantics, in their true form, are relations and are non-physical and non-reducible.
3) Notice also, partitioning is imposed upon the semantics (to make them ‘measurable/comparable’). If you compare semantics in such a way then you only get answers in terms of your investigation/ontology.
4) The better way is to leave the semantics as they are! Don’t classify them! Learn how they are related. Then you will know how they are compared.
There’s more to say, but I think you get the idea… ask me if you want clarification…
The time is coming when we will exchange massive amounts of knowledge between us without any corporation standing in between.
My life’s work is dedicated to this vision and I’m actually carrying it out right in front of you!
We will not only create and share our books, documents, web sites, search results, and media with each other – we will be sharing their conceptual landscapes.
3D Scientific Visualization with Blender
It’s a book everyone in knowledge representation should at least know about. It has great tips and clarifications inside.
Unfortunately it is also based solely on ontologies so it provides only limited value for what I’m doing, but it is a valuable resource for understanding and creating visualizations just the same.
(click picture to show the video)
We are being fed disinformation telling us that we never had privacy and to get used to this new ‘environment’. We must reject this false set of memes!
If you think you have nothing to hide, think again! What if someone you love and cherish does have something to hide and the watchers frame you for being related to them? If they can watch your every move and thought, then they can replace them as well and say it was you!
We are in a whole new ball game of spying – watch and see!
When did association amount to real knowledge?
Am I the only one to recognize something’s wrong with this picture?
As if it were a ‘combination lock’, if we begin to ask the right questions… the ‘tumblers’ move…
UPDATE: http://www.istockphoto.com/video/world-network-20381126 (an attractive representation of data)
Currently 1.68 TB (as of this date) of research data available!
The free exchange of ideas… again!
(click picture to show the article)
Why aren’t we discovering and creating ourselves rather than pay corporations to create things that are going to replace us? Something is really wrong here.
We are creating a nightmare when we could have a wonderful dream made reality.
The name Edison is fitting (if you know how Edison treated Tesla.)
The first thing I notice is the fluid, direct and economical style of writing that makes reading the book a pleasant experience.
1 Definition found: Search, above all else, is marketing.
The Shift to Semantic Search
I disagree with the addage “Knowledge is power!” For me, the use of knowledge is what can be or manifests power. David’s proviso that “knowledge is power if it leads to comprehension” pleases me, because it comes closer to what knowledge is, as it relates to power.
2 Definitions found: Semantics, Tim Berners-Lee’s ‘Semantic Search’
At one point David refers to how an answer engine’s success, as well as Google’s brand, depends upon the answers it provides; I would include other critical factors having nothing to do with the answers the engine returns. Google’s reputation is already in question in meaningful circles, despite the usefulness of its algorithms. I suspect that David will address at least some of them as the book continues.
How Search Works
Definitions: Spiders, Index, SERPs, organic results, ranking elements (algorithm/signals).
How Google’s page is synthesized from an interplay of elements composed of front and back ends.
The incredible speed and volume of indexing data is touched on.
Where the system no longer works as it was intended (SEO gaming) and how semantic search has changed that.
I like the use of the term ‘signals’ (a characteristic of knowledge) with respect to ranking. I’m looking forward to a clearer understanding of what that entails as it relates to semantic search.
How Semantic Search Works
Definitions: URI, RDF, (semantic) ontologies, serendipity discovery (and the dynamics that govern it)
Requirements that semantic search must be supplied to understand words (URI, RDF, ontology library)
Learning more about serendipity discovery interests me, however at this point in the text, only its usefulness in marketing and finding customers in the future is discussed. There is mention, however, that the book formalizes a set of practices to use it.
I am looking forward to an explanation of why serendipity discovery is treated in this ‘condensed’ way. David offers a bibliography for those who need a deeper understanding.
I am concerned on one other point: the restriction of semantics solely to an ontological foundation. I am aware at this point that an interplay of 2 additional components (and their surrounding dynamics) are in use (URI and RDF), but I need to know more about them, before I am willing to trust them completely.
It is important to remember that up to this point no promise is made for _real_ understanding in the model presented, rather only its _simulation._ That seems to be, nevertheless, very useful and completely new.
Important questions we should be answering now… not later. Important examples throughout modern history and where it influences and is influenced by it.