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 ongoing perception deception masking malevolence with benevolence.
True intelligence can never be artificial and the mind is much more than the brain can be used to explain it.
He is wrong in many ways. Even if he has really foreseen other inventions, this is one where he is wrong. They will, however, made a good show of it, by convincing us of their success so that we buy into the fraud with investment or participation.
Using the 4th most powerful computer in the world: “The computer has 705,024 processor cores and 1.4 million GB of RAM, but still took 40 minutes to crunch the data for just one second of brain activity.”
read more: http://tinyurl.com/pexmtk6
(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.
For those barren of humanity, the next place is barren humanity.
Has anyone taken the time to ask themselves why we are being driven to create machines like we are instead of use our creative power in developing ourselves?
“Looking for consciousness in the brain is like looking inside a radio for the announcer” – Nassim Haramein
Sorry to burst your bubble, but the mind cannot be reduced to a brain.
(click picture to show the video)
They’re being built to help you. They just wana help mankind..
Now give them your money to build them,… ‘kay?
(You know it’s trendy…)
This is more about harvesting funding for computer hardware and software than about anything we can take seriously.
@01:20“[AI] It’s a bit like a particle. There are arguments as to why it should exist, but we haven’t been able to confirm it yet.”
And you won’t either. Particles are not fundamental, rather they are only a manifestation of an underlying (and completely fundamental) field.
The current AI is lost in, just like physics, a compulsion to explain the world in only physical terms. Meaning of World, Thought and Language will continue to elude them as long as they keep on chasing a failed and bereft paradigm. It sure is trendy, but it’s false.
This is an example of what I referred to earlier in some of my posts. This one is about probabilistic programming. Our government is spending our money on research that isn’t even capable of achieving the goals it sets for itself.
They may be able to create semi-intelligent drones with this kind of research, but they will never achieve their aim of intelligent machines.