Assume nothing is for random. On a planet with a functional sun and a planet system. Everything is optimized for life.
Since water is essential. I would like to answer this question. What is the machine learned causal prediction model that takes *every data available and predicts where its going to rain.
I suspect that rain has much more detailed decision model. Like where is the air pollution, pavement, cities, ocean, the lakes, green areas, forests etc. Basically everything that shines back light. So light is then a light fi of earth information.
Then from this model. We can predict what the affect will be of all our earth restoration actions. I think making the earth less polutant is one of those ideas that are very important. For everything that has to do with life. Since the rain network does not want to harm with bad water.
Be it greening the deserts. The distribution of life and insects could be key.
How should we deal with anger, hate and not let us get into more violence that leads to war.
For victims and for reconciliation. For Palestinians and Israelis perhaps. It could be so simple as giving out restaurant gift cards. Like a months worth of free dinners and lunches at different restaurants.
The idea is that if you make the family or close friends happy together with you. That is all the support you need.
So the idea is for sponsored restaurants for peace.
When you eat. Try to feel what the brain tells you. Remember those food situations when you feel good. Take some notes.
From what I know. Fruits have sugar. So its related to the brain. There is a higher chance of survival in the wild if a purpose exist for each fruit. This because they would be taken care of by animals, birds. So assume our eatable fruits have brain purposes.
So inspired by 12 fruits collection tradition at new year. To get to that fruit you need at the moment. I wonder if its as simple as buying just one of each fruit. Taste a small sample feel the brain and select. Then just eat one or two of that selected fruits.
Take some notes of the problem and which fruit that made sense to your brain network.
This apply to food dishes as well. Feel and remember.
Inspired by spline fitting where you have a tiny model with limited number of weights. I think you can have a tiny and a big machine learning model working together.
The Tiny model get a new parameter value for each step in the solution and the Big model provides them. A bit like spline fitting. This way you have one set of weights for the big model and many different weights for the tiny model.
So the solution will have like 1000+ different tiny models. Just the weights differ. So its a fitting, not a optimization of a limited number of weights.
If noise could be input to machine learning models. Some generation models. Then since space with its stars look like noise. What is the network that this star noise is input to? Black Hole network?
Also. Since you have formations looking like galaxies. Could this inspire function and noise at the same time. So everything makes sense. Has meaning but still function as noise?
It would be simple to test 3D noise and some scale functions in xyz Blender viewport. Then feed the projection by light emission to a python machine learning network.
One way to generate new noise is to scale the noise image, fly into the 3D model etc. This way you don’t need to generate a new set of points. This could be one reason the universe ?scales, change something dependent on input from this scale to the network.
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.