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[ontolog-forum] Grand Unified Theories

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: John F Sowa <sowa@xxxxxxxxxxx>
Date: Tue, 03 Mar 2015 08:11:54 -0500
Message-id: <54F5B31A.4030505@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.    (01)

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.    (02)

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    (03)

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.    (04)

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    (05)

> 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.    (06)

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    (07)

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.    (08)

If anybody can find anything newer and better, please let me know.    (09)

John    (010)

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