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Re: [ontolog-forum] Grand Unified Theories

To: Avril.Styrman@xxxxxxxxxxx, "[ontolog-forum] " <ontolog-forum@xxxxxxxxxxxxxxxx>
From: Michael Brunnbauer <brunni@xxxxxxxxxxxx>
Date: Wed, 4 Mar 2015 16:40:25 +0100
Message-id: <20150304154025.GA12855@xxxxxxxxxxxx>

Hello Avril,    (01)

an infinite 4-dimensional space + an infinite time-dimension (past+future?)
just to hold our closed universe for 13+ billion years?    (02)

Regards,    (03)

Michael Brunnbauer    (04)

On Wed, Mar 04, 2015 at 04:43:02PM +0200, Avril Styrman wrote:
> Dear John and all,
> 
> >Physicists are the closest to finding a Grand Unified Theory (GUT)
> >of everything.  But every time they find one, it opens up far more
> >mysteries than it solves.
> 
> If the new theory/paradigm is really unifying, it should provide a unifying
> explanation of what were previously exceptional cases. That is, the new
> theory should get by without extra regularities, without different types of
> hypothetical elements, and all sorts of conflicts that are troubles for the
> old theory. The biggest problems of the standard model of cosmology are the
> following (the standard model is founded on GR, and was evolved into FLRW):
> 
> 1. The geometry of space is an open question.
> 2. It contradicts absolute time (the relativity principle)
> 3. It requires dark energy (currently 68.3 % of the total energy, i.e.,
> 68.3% of the value of the density parameter).
> 
> The role of the conservation law of energy is ambiguous in the standard
> model, because of the dark energy and the relativity principle.
> 
> A man called Tuomo Suntola has formulated what he calls "The Dynamic
> Universe" model.
> 
> http://www.physicsfoundations.org/library/The_Dynamic_Universe_e-book.pdf
> 
> 1. The geometry of space is fixed: 4 dimensional ball geometry
> 2. DU incorporates absolute time
> 3. The density parameter is not needed (and so dark energy is not needed),
> for the average density is calculated by dividing the mass of the Universe
> by its volume.
> 
> 
> In short, 4D ball geometry was contemplated in the early 1900's, but it was
> coupled with the idea of the static Universe. When there was consensus about
> the expansion, the static 4D model was abandoned. Suntola took the 4D model
> and combined it with the conservation law and the changing velocity of
> light: the velocity of light decreases as the Universe expands.  Compare the
> predictions of DU about the redshift-magnity proportions to the FLRW
> predictions.
> 
> Now how about that?
> 
> Avril
> 
> 
> 
> Quoting John F Sowa <sowa@xxxxxxxxxxx>:
> 
> >Physicists are the closest to finding a Grand Unified Theory (GUT)
> >of everything.  But every time they find one, it opens up far more
> >mysteries than it solves.
> >
> >Meanwhile, the very many practical applications use old theories that
> >are known to be be inadequate in the details.  Good old-fashioned
> >Newtonian mechanics is a prime example.  For big things moving at
> >normal speeds, GONM is the first choice.
> >
> >Two professors at NYU -- the psycholinguist Gary Marcus and the
> >AI expert Earnest Davis -- wrote a review of attempts to find
> >a GUT about intelligence, human or machine:
> >http://www.newyorker.com/tech/elements/a-grand-unified-theory-of-everything
> >
> >Opening paragraph:
> >>Here???s a way to make a lot of money. Publish a speculative scientific
> >>article with equations nobody understands, put out a press release,
> >>throw in a few credentials (say, a degree from Harvard or MIT), and
> >>get a few bloggers to spread the word. In the meantime, quietly
> >>start  a company based on the idea???the grander, the better.
> >
> >Marcus also wrote an article "Steamrolled by Big Data", which
> >discusses a GUT by Jeff Hawkins, which is supposed to do everything:
> >http://www.newyorker.com/tech/elements/steamrolled-by-big-data
> >
> >Quotation:
> >>As one skeptical industry insider, Anthony Nyström, of the Web
> >>software company Intridea, put it to me, selling Big Data is
> >>a great gig for charlatans, because they never have to admit to
> >>being wrong. ???If their system fails to provide predictive insight,
> >>it???s not their models, it???s an issue with your data.??? You didn???t
> >>have enough data, there was too much noise, you measured the wrong
> >>things. The list of excuses can be long.
> >
> >I went to the web site for Hawkins' company, Numenta, where I found
> >the following whitepaper about their Hierarchical Temporal Memory:
> 
>>http://numenta.com/assets/pdf/whitepapers/hierarchical-temporal-memory-cortical-learning-algorithm-0.2.1-en.pdf
> >
> >This draft is version 0.2.1 from Sept. 2011.  All the newer things
> >are videos, mostly by Jeff H., who talks very fast.  I also found
> >an MS thesis from 2011 by Ryan Price.
> >
> >If anybody can find anything newer and better, please let me know.
> >
> >John
> >
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> 
> Ystävällisin terveisin,
> 
> Avril Styrman
> avril.styrman@xxxxxxxxxxx
> puh. +358 40 7000 589    (05)


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