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Old Yesterday, 02:01 PM   #561
Reality Check
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Originally Posted by ProgrammingGodJordan View Post
You won't find any such evidence of such a "lie". ...
That is a lie because your "hypothesis" is not mentioned in Christopher Lu's code .
23 March 2017 ProgrammingGodJordan: Lies about Christopher Lu's code from Lu's Master's thesis (which does not contain his hypothesis).

I only skimmed Christopher Lu's thesis but did not see your "hypothesis" there either but if it was there in order to generate the code then it would be Christopher Lu's hypothesis !

So far it looks like you have the insane assertion that any? code for deep learning implements your "hypothesis" because your math word salad is about deep learning.
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Old Yesterday, 02:16 PM   #562
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Originally Posted by ProgrammingGodJordan View Post
That is silly, especially when Christopher Lu's work is referenced in the first line of the repository's readme.
That is extremely silly ignorance about your own source !
The first line in Readme.md is
Quote:
Followed by irrelevant gibberish. No reference to Christopher Lu's work.
The first line in the source files is "%% Author ~ Christopher Lu". No reference to Christopher Lu's work.
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Old Yesterday, 02:23 PM   #563
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Originally Posted by ProgrammingGodJordan View Post
(A)
Simply, it exists as a fundamental portion of the equations.

Some causal laws of physics are a part of the Supermanifold Hypothesis/Thought Curvature equations.


(B)
Lu's work shows that it is possible to query some mesoscale format in real time.


(C)
Also, deep mind shows that large scale reinforcement learning is possible.



(D)
Combining (A), (B) and (C) it is perhaps observable that my fabric is possible/time-space complex optimal.

This is long mentioned in the work presented.
HaHaHaHaHa! Look what you wrote! That's some funny **** right there!
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Old Yesterday, 02:32 PM   #564
Reality Check
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Thumbs down ProgrammingGodJordan: A valid hypothesis is not incoherent math word salad

Originally Posted by ProgrammingGodJordan View Post
A hypothesis means things done with incomplete evidence.
Ignorance about what a hypothesis is !

Simply put a hypothesis is a statement that might be true, which can then be tested or
Quote:
A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories.
...
A different meaning of the term hypothesis is used in formal logic, to denote the antecedent of a proposition; thus in the proposition "If P, then Q", P denotes the hypothesis (or antecedent); ); Q can be called a consequent.
A valid hypothesis has two parts:
  • A coherent and understandable statement of the explanation.
  • A coherent and understandable statement of how the hypothesis can be tested.
24 March 2017 ProgrammingGodJordan: A valid hypothesis is not incoherent math word salad as I pointed out yesterday.
Originally Posted by Reality Check View Post
That the hypothesis does not exist is a very big problem !
  1. A PDF that does not meet the standards of a high school science paper.
  2. A lie about having a hypothesis - you have few paragraphs of incoherent math word salad.
  3. Guesses (even "reasonable") are not mathematics.
  4. Actual gibberish, e.g. "Some tensor sequence of priors. (i.e. solid, liquid, gas)".
No statement of how to test the "hypothesis" is also bad.

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Old Yesterday, 10:57 PM   #565
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Originally Posted by Reality Check View Post
Ignorance about what a hypothesis is !

Simply put a hypothesis is a statement that might be true, which can then be tested or

A valid hypothesis has two parts:
  • A coherent and understandable statement of the explanation.
  • A coherent and understandable statement of how the hypothesis can be tested.
24 March 2017 ProgrammingGodJordan: A valid hypothesis is not incoherent math word salad as I pointed out yesterday.

No statement of how to test the "hypothesis" is also bad.
Supermanifold hypothesis' equations are essentially standard math in deep learning.

I recall that you mentioned you lack machine learning expertise, so this thread is probably not for you...
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Old Yesterday, 10:59 PM   #566
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Originally Posted by Reality Check View Post
That is extremely silly ignorance about your own source !
The first line in Readme.md is

Followed by irrelevant gibberish. No reference to Christopher Lu's work.
The first line in the source files is "%% Author ~ Christopher Lu". No reference to Christopher Lu's work.
Click the phrase hierarchical causal fabric in the first line.

That is a link to Lu's work.
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Old Yesterday, 11:01 PM   #567
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Originally Posted by MikeG View Post
No. No, it absolutely does not. You are flat out wrong.
hypothesis:

a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.
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Old Yesterday, 11:07 PM   #568
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Originally Posted by Myriad View Post
To follow up — another unpromising sign is PGJ's habit of responding to a single word in a sentence, out of context, with a complete non-sequitur, much in the manner of an Eliza-type program. For example, I posted:









Keying weird responses off of single words taken out of context is rarely a sign of an earnest attempt to communicate a sound but technically abstruse idea.
I noticed you conveniently skipped the remainder of my response to your transformation qualm:

Originally Posted by ProgrammingGodJordan

Anyway, it is common enough in deep learning, that deep neural nets may be observed to be learning via a sequence of transformations (continuous bijective-inverse wise functions), under certain constraints/topologies, such as differentiable manifolds.

Even before the manifold interpretation, transformations are commonly applied. Every layer that the neural net learns occurs because of activations or transformations. (eg sigmoid, hyperbolic tangential function, etc.)
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Last edited by ProgrammingGodJordan; Yesterday at 11:10 PM.
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Old Yesterday, 11:10 PM   #569
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Originally Posted by W.D.Clinger View Post
I'd have written "problems", plural, but I'll respond with just one because you wrote the singular.

In your image, you wrote:

C is the set of infinitely differentiable functions, which is not usually regarded as a Euclidean space.

Perhaps you meant the topological space obtained by taking an infinite product of the complex numbers with the standard product topology, but that is not usually regarded as a Euclidean space either.

Then there's the question of what you might have meant by writing C(Rn), but (depending on what you meant by C) that might well be regarded as a second problem, so I won't bother to mention it or any other problems I may have detected.
C is typical supermanifold term.
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Old Yesterday, 11:14 PM   #570
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Originally Posted by Myriad View Post
It's not at all odd. "Indicating" functions and operations etc. is not the same as reasoning about them or performing them. For instance, if I write:

2350!

. I've indicated a series of 2,349 multiplication operations, but I have not posted an equation or done any mathematics.
I observe my prior statement.

As mentioned, the super m equations contain many other operations, including tensor aligned operations, under continuous topological consistencies.

The deep learning book by Bengio et al is an optimal start, if you are not familiar with deep learning mechanics, or if you are
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Old Today, 04:27 AM   #571
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Originally Posted by ProgrammingGodJordan View Post
C is typical supermanifold term.
But you don't know what it means.

You wrote euclidean superspace in boldface italic, but C(Rn) is neither Euclidean nor a superspace of Rn.

Do carry on.


ProgrammingGodJordan probably found "C" by Googling articles such as Wikipedia's article on supermanifolds, saw phrases such as

Originally Posted by Wikipedia
look like a "flat", "Euclidean" superspace
and didn't know enough math to understand those adjectives were describing local coordinate charts rather than C.
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Old Today, 05:10 AM   #572
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Originally Posted by ProgrammingGodJordan View Post
I observe my prior statement......
Previous.

You see your previous statement? Really? My, how interesting. Is there anything we should see in it if we were to look at it as well?
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Old Today, 05:11 AM   #573
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Originally Posted by W.D.Clinger View Post
But you don't know what it means.

You wrote euclidean superspace in boldface italic, but C(Rn) is neither Euclidean nor a superspace of Rn.

Do carry on.


ProgrammingGodJordan probably found "C" by Googling articles such as Wikipedia's article on supermanifolds, saw phrases such as



and didn't know enough math to understand those adjectives were describing local coordinate charts rather than C.
Your comment is invalid. Pay attention to especially item (3) below:

(1)
Euclidean space may encompass Rn and manifolds are locally euclidean in nature:
https://en.m.wikipedia.org/wiki/Euclidean_space

Pertinently, other events are observed as noise, in the manifold regime:
http://www.deeplearningbook.org



(2)
Superspace may entail in its basis, some super manifold:
https://en.m.wikipedia.org/wiki/Superspace



(3)
Supermanifold may encode as "essentially flat euclidean super space" fabric:

https://en.m.wikipedia.org/wiki/Supermanifold
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Old Today, 06:49 AM   #574
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I think you missed W.D.Clinger's spoiler...
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Old Today, 07:50 AM   #575
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Originally Posted by W.D.Clinger View Post
But you don't know what it means.

You wrote euclidean superspace in boldface italic, but C(Rn) is neither Euclidean nor a superspace of Rn.

Do carry on.


ProgrammingGodJordan probably found "C" by Googling articles such as Wikipedia's article on supermanifolds, saw phrases such as



and didn't know enough math to understand those adjectives were describing local coordinate charts rather than C.
Makes sense that his math is as arbitrarily plagiarized as his code.
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Old Today, 10:39 AM   #576
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Originally Posted by fagin View Post
I think you missed W.D.Clinger's spoiler...
No, I didn't miss it. My response already addressed this reality.
..but here is a summary below:


(A)
He seemed to miss the instance that deep learning refers loosely to particular regions of manifolds, and it is those regions that appear to generate embeddings of interest. (Deep Learning Book, Bengio et al.)


(B)
In a similar way, the regions of supermanifolds may encode 'flat eulcidean like superspaces', also mentioned in wikipedia. (These are the regions I take interest in wrt causal reinforcement learning, as it is probable that separate regions may be 'noise'.)

Ironically, it is particularly the charts he mentions, that I reference in my supermanifold hypothesis paper. (See the sentence regarding 'result map sequences' from the super-m paper.)
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