What were you thinking?!

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toborguy

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Re: What were you thinking?!
« Reply #15 on: May 22, 2019, 07:56:14 pm »
ANN's. My  program environment is designed for proof of concept development, not production. This means that single channel, low volume data with slow response times is sufficient for testing.  Therefore I don't have to use ANN technology at this time.  However, when we start work on the metacognition (ODEPC) functions, there will be a requirement for the program to do self-editing. This may be better addressed with ANN's due the inherent self-editing capabilities.

I'll see if I can get you  a jpg file of the mindmap diagram.

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LOCKSUIT

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Re: What were you thinking?!
« Reply #16 on: May 28, 2019, 08:58:46 am »
A B C D occur together with w x y z
A E F G occur together with w t u v
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Therefore A is the cause, or the effect, of w.

^

Also, I want to add, A=w may be true. This is how Glove/Word2Vec works.



By the way, I do have and have analyzed one large mindmap diagram from a year or so ago, but not sure if there is another one, or updated one. See attachment.
Emergent          https://openai.com/blog/

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toborguy

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Re: What were you thinking?!
« Reply #17 on: May 29, 2019, 10:20:40 pm »
Your diagram is almost the same. The changes were minor.

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Don Patrick

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Re: What were you thinking?!
« Reply #18 on: May 30, 2019, 08:05:25 am »
A B C D occur together with w x y z
A E F G occur together with w t u v
——————————————————
Therefore A is the cause, or the effect, of w.
The problem with this, and with neural networks and statistics in general, is that it does not distinguish between causation and correlation. Like when you look out the window and the sun is out, the sun is not out because you are looking out the window, nor do you look out the window because the sun is out, it just so happens that you're typically not awake when it's dark.
CO2 retains heat. More CO2 in the air = hotter climate.

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LOCKSUIT

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Re: What were you thinking?!
« Reply #19 on: May 30, 2019, 08:54:07 am »
I have great answers below, must see!

What you quoted basically says we have 2 observations where A & W occur at the same Time. And there is 4 items, not 2, so it doesn't fit the Look / Sun | Sun / Look statement you said. Oh, see below nevermind.

As for the Sun/Look statement, I have an answer. If you have 2 observations of them both occurring at the same Time (ah, I see how you saw this!), Look / Sun | Sun / Look, then it means that either one or the other or both follow each other (cause & effect) or that they are the same thing. However, if we took enough intelligent observations, we WOULD see that Look doesn't always have 'Sun' Follow, if it's night. Same for Sun, but decides not to Looky. However, let's say the sun is always out, and you always look. - Now, we can say Sun>Looks, and Looks>Sun. However, you have fallen into the wrong idea, looking does cause the sun to be seen rather. And the sun being there does cause the looking. So yes, Sun>Looks, and Looks>Sun. You put words in our mouths. No rule said it makes the sun 'be' there. In this case, it can be said looking makes the sun SEEN, and SEEN causes the looking!

Also cause & effect is not the best terminology. A fish swam by a rock. The best terminology is Sequences. One follows the other. At the rock the fish swam by. The fish swam by the rock. The rock slid by the fish. Near the fish the rock slid by.
Emergent          https://openai.com/blog/

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toborguy

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Re: What were you thinking?!
« Reply #20 on: May 30, 2019, 06:15:28 pm »
What is the value of observing that two or more events, objects or ideas have something in common (time, location, similar characteristics) and others do not ? Your observation makes them candidates for further investigation. Is there a correlation, and what is the correlation? This investigation may lead to a new discovery or not, but without the observation of possible correlation nothing would be learned.  The observations will not prove the correlation without additional corroboration, but with additional observations, may lead to probable correlations. The use of Mill's Methods, for instance, may lead us to identifying the cause and the effect with a high probability.

Just my two cents. ;)

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Don Patrick

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Re: What were you thinking?!
« Reply #21 on: May 31, 2019, 08:02:04 am »
if we took enough intelligent observations, we WOULD see that Look doesn't always have 'Sun' Follow
And that is precisely what was missing from your examples, as well as the more important sequential aspect. The same are missing in the operation of neural networks.
CO2 retains heat. More CO2 in the air = hotter climate.

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LOCKSUIT

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Re: What were you thinking?!
« Reply #22 on: May 31, 2019, 09:50:39 pm »
Yes Don, that's correct.

Toborguy is very correct here, my grand master net design can do that too, it can find X fast, find a correlation fast, and update and look again to refine its relations. And is fully editable/ scalable. The design can also take samples of sequences and check if all are Black Birds or if some are white.
Emergent          https://openai.com/blog/

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Art

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Re: What were you thinking?!
« Reply #23 on: June 01, 2019, 01:15:41 pm »
Yes Don, that's correct.

Toborguy is very correct here, my grand master net design can do that too, it can find X fast, find a correlation fast, and update and look again to refine its relations. And is fully editable/ scalable. The design can also take samples of sequences and check if all are Black Birds or if some are white.

Can you give us an example of this design and how it works?
In the world of AI, it's the thought that counts!

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LOCKSUIT

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Re: What were you thinking?!
« Reply #24 on: June 01, 2019, 01:18:39 pm »
Not until the staged release, as open ai calls it.
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LOCKSUIT

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Re: What were you thinking?!
« Reply #25 on: June 14, 2019, 04:41:49 am »
Most of those boxes in toborguy's grand diagram (see post #16) are all the same thing, and most of those is 1 system, a grand master net hierarchy.
Emergent          https://openai.com/blog/

 


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