Everything that is physics is also just networks. Working together.
Then it could be a an idea to model some parts of the algorithm in a 3D modeler like Blender.
For instance I suspect you need simple simulated physical wheel suspension systems for periodic objects.
?Everything can be written in a periodic way. Then I suspect that using machine learning models. With lots of error correction. Could possible brake the network. I guess the process could then be improved with proven real world examples of say suspension. Breaking the network is similar to the error goes to inf.
I got this idea from mechanical inventions trying to solve simple network type problems like showing time. Maybe there exist some advantage in modeling some parts in a 3D simulator than trying to come up with a limited math function.
Since machine learning models can be non linear. Would not the predicted probability of label output of the model change in a non linear fashion. With respect to the number of training samples. I will try to plot such a curve.
Since gravity is responsible for the position of earth. It would be irresponsible for the universe to leave it to a simple equation.
So it must be a stable Network Solution.
Maybe gravity is not a force. Gravity could be energy instead. Why? Since energy carries more functionality than a force. Much like a network function can assume *any function.
Then to get a force the mass network just calculates using the energy a new very little change in position. According to the random distance idea.
The random distance idea is used here to create a problem. Where does the ?atom exist relative to a boundary. A|A. So gravity energy is energy to a positioning network. The energy is used to settle this decision problem.
Choose between two positions requires lot of in data so here is where the precision comes in. It must be very accurate.
From GAN network models I wonder. Since the generator depends on noise. How little noise is necessary to make it work.
Could we get it to work with a little as a single random data value, like a bit. Varying with time randomly.
So from this I speculate that there is an effective way to compress information. But requires perhaps a fast computing power.
So physically, water is important to life and water is a network. Since life is also complex. Water might be complex to. The weight parameters of water to sustain all life on the planet might be so large that it needs to be compressed somehow.
So what do I mean by water is needed for life. I guess that ?computing ready weight parameters in the water acts like the surrounding network between life networks.
?All contact surfaces to water needs water to sustain life.
Compare two images below and feel. What image gives you the most intelligent feeling?
For me its the flat shaded sphere image. Possibly because a perfect photo would be too amazing and a non perfect photo feels a little bit wrong. The amazing feeling does not give much information to the imagination. Which is The tool for intelligence.
Between these is the revealing image. The flat shaded one. The one that reveals how it works. A little any way. But still to the brain it makes all the difference.
Whenever you don’t know the answer, assume a network. So what does the peace country network look like?
What infrastructure do you need etc. Could creating a city result in a positive win win. Creating jobs. Also could reading peace as a school subject help. How do you change a troubled country to peace. What input to the network works?
I guess there exist Problem, Question and Answer triplets that need to be adapted before solving. Problems are not always questions. Since you can ask different questions. They are not always related to the problem.
Where in the triplet possibility does the relation lay. What I mean is.
To solve certain triplets the problem might have to downsized or up sized. I guess this could be said for the question and answer also.
Also. Can we be sure we got it right for every problem? There might be probabilities for the triplet. At least to the answer.
One interesting insight is that the set of all possible problems are not as compressible as mathematics assume. That is. Math and problems go hand in hand. Then since problems changes so could math or network math.
One such example is that of singularity. I speculate that math functions gets adapted to the problem. Not assuming the singularity problem are like every other problem. If you start at a singularity problem its perhaps a little bit harder. But otherwise I assume you approach the singularity from an easier problem.
So if you have a variable with a limited range from 0 to 9. Then any function that carries that variable outside its range would impose a problem. Likewise I think the singularity is related to infinity as its an mathematical idea number. So the solution to the singularity is also an idea.
So here is what I propose. Under smart network mathematics you could replace functions with network model functions or network ideas.
So for a problem of 1/0 you replace / with a different network_division(a,b) function. This particular division model function knows your entire problem and adapts its result.
The result is depending on your problem so we got a new idea. There is always a (problem, math model) pair that exists. So a math model could be a machine learning model that solves division near the problem specific singularity.
Why not let dignity and peace win. I believe a key to peace lays with the poor people. Those on the brink of becoming homeless. Empower them regardless of political relations. I believe this strategy is a smart way for peace and the people.
The idea is to create simple source code like in the fantasy computers. Make a stubb or simple plugin that first time programmers can create their own plugin with. It does not have to be for public sharing. Just a simple plugin in a plugin for GIMP or Blender for instance.
In machine learning ?every in-data are assumed to make sense to the target label. You update and learn the weights of the model. According to errors you get from the comparison of the target and the prediction.
From this I can guess that every decimal in a constant like pi should make sense to a network.
For me its close to saying that there are many significant decimals at least for pi.
So then should every decimal make sense to a network? Are there a constants which has an infinite number of significant decimals?