Merged Artificial Intelligence Research: Supermathematics and Physics

[Reality Check]

Thought curvature v2 - the first abstract sentence is gibberish

The previous thought curvature nonsense has vanished so I will record the new nonsense more fully in case it vanishes too.
12 October 2017: Thought curvature v? - the "abstract" is word salad with no real meaning.

Abstract
Thought curvature designates that some euclidean superspace, reasonably permits uniform symbols on the boundary of some manifold distribution, particularly on some input space of form entailing the laws of physics, in the temporal difference horizon.

 

[Reality Check]

Thought curvature gibberish becomes obvious in the PDF abstract

Now into the PDF and
12 October 2017: Thought curvature gibberish becomes obvious in the PDF abstract

Abstract
Some Markov receptive C∞π(Rnπ) , reasonably permits uniform symbols on the boundary of Rn, betwixt some Uα, of φi; particularly on some input space of form η , in the scope of empirical evidence pertaining to supersymmetry in the biological brain.[12] (See preliminary encoding) .

C^∞π(R^nπ) is math gibberish, especially with no sources or explanation.
C^∞(Rⁿ) was a previous nonsensical symbol for supermanifolds which really have the symbol M like any manifold. Throwing in a couple of n" is makes it worse.

 

[Reality Check]

No "empirical evidence pertaining to supersymmetry in the biological brain" in [12]

12 October 2017: There is no "empirical evidence pertaining to supersymmetry in the biological brain" in [12].
Supersymmetric Methods in the Traveling Variable: inside Neurons and at the Brain Scale
This is a paper by material scientists modeling ideas (not empirical evidence)about "solitons and kinks" in any brain.

Nevertheless, there is no clear experimental evidence at the moment of any of these biological solitons and kinks

The entire paper is theory. Where supersymmetry appears is a " nonstandard application of Witten’s supersymmetric quantum mechanics" based on a fundamental assumption.

 

[ProgrammingGodJordan]

Now into the PDF and
12 October 2017: Thought curvature gibberish becomes obvious in the PDF abstract

You had long admitted that you hadn't much practical machine learning experience.

Notably, I have spoken to people like Bengio Yoshua (a pioneer in Neural Inspired, representation learning or Deep Learning), and he doesn't appear to think core parts of the paper are "gibberish".

Sample Bengio exchange:

Sample exchange with a person who deals with cosmology and particle physics:

 

[Reality Check]

....

Nothing about his Thought curvature nonsense and gibberish.
12 October 2017: Thought curvature v? - the "abstract" is word salad with no real meaning
12 October 2017: Thought curvature gibberish becomes obvious in the PDF abstract
An honest response would be giving a clear explanation of what the abstracts mean with sources.

 

[ProgrammingGodJordan]

12 October 2017: There is no "empirical evidence pertaining to supersymmetry in the biological brain" in [12].
Supersymmetric Methods in the Traveling Variable: inside Neurons and at the Brain Scale
This is a paper by material scientists modeling ideas (not empirical evidence)about "solitons and kinks" in any brain.

The entire paper is theory. Where supersymmetry appears is a " nonstandard application of Witten’s supersymmetric quantum mechanics" based on a fundamental assumption.

Ah, I see you began to evaluate some of the sources referenced in Thought Curvature.

A quote from supersymmetry paper: "Robinson has used the experimental parameters in this equation for the processing of the experimental data"

The supersymmetric paper by Perez' et al. is not entirely based on theory.
You will notice that it derives equations from works that are experiementally observed, such as the works of PA. Robinson from reference 25 in the aforesaid Supersymmetric paper, as referenced in Thought Curvature.

 

[ProgrammingGodJordan]

Nothing about his Thought curvature nonsense and gibberish.
12 October 2017: Thought curvature v? - the "abstract" is word salad with no real meaning
12 October 2017: Thought curvature gibberish becomes obvious in the PDF abstract
An honest response would be giving a clear explanation of what the abstracts mean with sources.

I stand by my prior quote, or rather it is observable that talks with other beings, with actual experience in the field, and or experience with cosmology/particle physics, don't result in accusations of gibberish.

Notably, I have spoken to people like Bengio Yoshua (a pioneer in Neural Inspired, representation learning or Deep Learning), and he doesn't appear to think core parts of the paper are "gibberish".

Sample Bengio exchange:

Sample exchange with a person who deals with cosmology and particle physics:

 

[ProgrammingGodJordan]

Now into the PDF and
12 October 2017: Thought curvature gibberish becomes obvious in the PDF abstract

C^∞π(R^nπ) is math gibberish, especially with no sources or explanation.
C^∞(Rⁿ) was a previous nonsensical symbol for supermanifolds which really have the symbol M like any manifold. Throwing in a couple of n" is makes it worse.

I won't comment your commentary about C^∞(Rⁿ), as that is standard, and not used in any strange way in my paper.

However C^∞π(R^nπ) is a novel term...

 

[Reality Check]

An image with no evidence of a person dealing with cosmology and particle physics

Sample exchange with a person who deals with cosmology and particle physics:

12 October 2017: An image with no evidence of a person dealing with cosmology and particle physics.
Mordred as a few replies with group theory and quantum mechanics.

• "Any SO group is reducible to SU most commonly SU(2)" with a comment about being applicable to machine learning in a reference 1.
• A few paragraphs of simple QFT (the subject of my thesis!)

The QFT material suggests familiarity with theoretical particle physics (or that they can read and understand Wikipedia :)

 

[fagin]

Is 'novel' the same as 'making **** up'?

 

[ProgrammingGodJordan]

Followed by a post with inane coloring and insults so:
12 October 2017: Resorts to a repeated insult of my level of knowledge of machine learning.

1. 8 August 2017: Ignorant math word salad on academia.edu (gibberish title and worse contents).
2. 14 August 2017: Thought Curvature abstract starts with actual gibberish.
3. 14 August 2017: Thought Curvature abstract that lies about your previous wrong definition.
4. 14 August 2017: A Curvature abstract ends with ignorant gibberish: "Ergo the paradox axiomatizes".
5. 16 August 2017: Thought Curvature DeepMind bad scholarship (no citations) and some incoherence
6. 18 August 2017: Thought Curvature uetorch bad scholarship (no citations) and incoherence
7. 18 August 2017: Thought Curvature irrelevant "childhood neocortical framework" sentence and missing citation.
8. 18 August 2017: Thought Curvature "non-invariant fabric" gibberish.
9. 18 August 2017: Thought Curvature Partial paradox reduction gibberish and missing citations.
10. 4 October 2017: Looks like an expanded incoherent document starting with title: "Thought Curvature: An underivative hypothesis"
11. 4 October 2017: "An underivative hypothesis": An abstract of incoherent word salad linking to a PDF of worse gibberish.
12. 4 October 2017: "Supermathematics ...": The "manifold learning frameworks" link is wrong because the paper does not have any manifold learning frameworks
13. 4 October 2017: Links to people basically ignoring his ideas in 2 forum threads!
14. 4 October 2017 ProgrammingGodJordan: It is a lie that I stated that manifold learning frameworks is in the paper.
15. 4 October 2017 ProgrammingGodJordan: Lists messages form someone mostly ignoring his work!
16. 5 October 2017: A link to a PDF repeating a delusion of a "Deepmnd atari q architecture".
17. 5 October 2017: A lie about an "irrelevant one line description of deep q learning" when I quoted a relevant DeepMind Wikipedia article.
18. 5 October 2017: No experiment at all, proposed or actual at the given link or PDF!
19. 5 October 2017: A PDF section title lies about a probable experiment no experiment at all, proposed or actual.
20. 6 October 2017: Insults about knowledge of machine learning when I displayed knowledge by looking for something I knew about (pooling versus non-pooling layers).

Notably, papers may appear like gibberish to you, if you don't really know anything about the field. (As I outlined you've demonstrated here)

 

[ProgrammingGodJordan]

Is 'novel' the same as 'making **** up'?

Well, science has progressed because novel paradigms constantly emerge...

 

[ProgrammingGodJordan]

12 October 2017: An image with no evidence of a person dealing with cosmology and particle physics.
Mordred as a few replies with group theory and quantum mechanics.

• "Any SO group is reducible to SU most commonly SU(2)" with a comment about being applicable to machine learning in a reference 1.
• A few paragraphs of simple QFT (the subject of my thesis!)

The QFT material suggests familiarity with theoretical particle physics (or that they can read and understand Wikipedia :)

Here is Mordred's profile.

Here is one of Mordred's contributions.

Here is one of Mordred's interests.

Recall that I said Mordred deals with cosmology/particle physics. (I didn't mention Mordred had a PHD)

 

[halleyscomet]

Supermathematics and Artificial General Intelligence / Thought Curvature

Notably, papers may appear like gibberish to you, if you don't really know anything about the field. (As I outlined you've demonstrated here)

Have you submitted any of these papers to a peer reviewed journal?

If not, why not?

I suspect the ideas whose only citations are correspondence with other people would be a barrier to surviving the peer review process. You really need to go back and rework them so you can support them or at least extrapolate them not from conversations but from other papers.

I would contact the people you originally collaborated with and see if you can get their assistance preparing the paper for the peer review process. Adding them as co-authors is one way to get their assistance if they think your ideas are any good. Having actual luminaries in the field ad co-authors on your paper is also going to help get more traction and attention in the field.

 

[ProgrammingGodJordan]

@RealityCheck:

[IMGw=230]https://i.imgur.com/pgXIb8F.jpg[/IMGw]

Here is one of Yoshua Bengio's recent remarkable papers, "The Consciousness Prior".

If one does machine learning, one can probably explain its essence in a few lines, (I've seen an experienced machine learning chick explain it after roughly 20 minutes of her seeing me notifying her the paper)

Could you care to explain, or is it "gibberish"?

 

[ProgrammingGodJordan]

Have you submitted any of these papers to a peer reviewed journal?

If not, why not?

I suspect the ideas whose only citations are correspondence with other people would be a barrier to surviving the peer review process. You really need to go back and rework them so you can support them or at least extrapolate them not from conversations but from other papers.

I would contact the people you originally collaborated with and see if you can get their assistance preparing the paper for the peer review process. Adding them as co-authors is one way to get their assistance if they think your ideas are any good. Having actual luminaries in the field ad co-authors on your paper is also going to help get more traction and attention in the field.

As I specified here in a related comment, there is some work to be done before that time.

 

[John Jones]

Well, science has progressed because novel paradigms constantly emerge...

Novel paradigms with supporting science, and explanatory and predictive power greater than existing paradigms.

These don't emerge constantly. Crackpottery does.

 

[ProgrammingGodJordan]

Novel paradigms with supporting science, and explanatory and predictive power greater than existing paradigms.

These don't emerge constantly. Crackpottery does.

Have you thought up any novel paradigms with supporting science, let alone crackpottery?
We must strive not to be worthless to humanity...

 

[Reality Check]

I won't comment your commentary about C^∞(Rⁿ), as that is standard, and not used in any strange way in my paper.

I will comment.
C^∞ is a classification of things being infinitely differentiable. For example a complex function can be infinitely differentiable and we write this as "a C^∞ complex function".
Rⁿ is a Euclidean space.
It is a lie to state that C^∞(Rⁿ) is a Euclidean superspace. It is math word salad. Charitably it is an irrelevant assertion that a Euclidean space is infinitely differentiable.

A mathematician commented on that nonsense months ago:

1. 24 March 2017 by W.D.Clinger: OMG (no pun intended). I always enjoy watching someone pretend to do mathematics.
2. 24 March 2017 by W.D.Clinger: The first of multiple problems is C^∞ is not typically a Euclidean space.
3. 25 March 2017 by W.D.Clinger: But you don't know what it means. (your "C^{∞[/SUP" is typical supermanifold term" statement). [*]25 March 2017 by W.D.Clinger: The ignorance of citing an informal "looks like" definition when discussing mathematics. [*]25 March 2017 by W.D.Clinger: "is telling us the supermanifold is Euclidean, when it isn't even Hausdorff." [*]1 April 2017 by W.D.Clinger: I am also not surprised by your inability to understand what I have said. [*]1 April 2017 by W.D.Clinger: Euclidean spaces are Hausdorff. Spaces that are not Hausdorff are therefore not Euclidean. [*]1 April 2017 by W.D.Clinger: But you didn't know any of that.[your ignorance of bundles and fibers] [*]2 April 2017 by W.D.Clinger: "a great deal of trouble understanding that locally Euclidean and Euclidean are not the same thing"}

 

[halleyscomet]

As I specified here in a related comment, there is some work to be done before that time.

Do you not have enough confidence in the theory to publish it without first having proven it experimentally? Do you have some sort of moral, ethical or philosophical objection to publishing a purely theoretical paper?

It occurs to me, the work needed to break your overall theory down into a project that could be executed on a grid computing platform would itself constitute a publishable paper.

You seem to have put yourself in an interesting chicken and egg situation. You’re unwilling to publish a theoretical paper but without a theoretical paper you’re unlikely to get the grants needed to purchase the hardware to do the experiments you want to do. To make progress forward you need either publish a theoretical paper or break down your theory so that it can be run on a grid computing platform.

What is your plan?