Higher Math Idea – Calculate With Raytraced Render Objects Instead Of Simple Boarder Defined Objects?

I wonder. Could it be an idea to calculate with raytraced renders of object instead of just the boarder and nothing else.

I mean. With machine leaning you could just input the raytraced object as data and do the calculation.

Machine learning does not care if the object is more complex than it should be.

If the render looks useful to the human eye than it could very well be useful complexity to the algorithm.

So the idea is to use renders or mesh objects of the objects you normally just use a boarder definition of.

I think this could be used for calculations of physical transfer of information by light between objects which are ?useful for physical networks.

Physics Guess – Energy And Information Black Hole Energy Bundle?

From machine learning there is the philosophical possibility to direct data from a later layer back to a previous layer. I call those networks internal loop networks.

Then if everything needs to be computed in some way. Could a magnet calculate its solution from recurrent information. The field lines look like loops so why not. So data and energy goes from one layer to the other along the magnet back to the input and output layer.

So I wonder. Is stored energy just a compressed mass information bundle. I mean. When you compress information you get a higher information density. So why could not energy storage be a compress and decompress process?

I mean. If energy is like the image you are trying to compress. Then decompressing it will restore the energy to its original state.

Its when you tap the decompression process its gets interesting. Since then I assume you get information loss in the image but you can create another image with that energy you withdraw.

So you can manipulate the choice for the decompression process. So energy can be iterated out to create another “image”.

So a black hole just compresses all the mass-information into one ?neat compressed energy bundle. To be decompressed I guess somewhere else for energy.

Wonderful Idea – Linear System Model Of Machine Learning – A,b = model(…) Where Ax = b

I think I found out something wonderful.

For example. If you choose the model() to represent A,b in Ax=b. That is. A,b = model(…). Then use a proper algorithm to calculate x = np.linalg.solve(A,b). The x here is the target value which also have some loss.

Then the idea is that you filter the iteration to always have some truth. You denoise it if you will. The machine learning model can not do all the calculations. Its not intelligent.

I think this can be used with diff equations also. Just let the model represent a system of linear differential equations. Then solve the system with some known algorithm. So the algorithmic solution is done for all iterations.

So the idea is to use algorithms together with the machine learning model to calculate a Linear System Model Of Machine Learning.

Machine Learning Physics – Could The Physics Solver In Blender Be Improved With Machine Learning And Denoising?

Very often the physics simulation in Blender gets a bouncing result. Pieces fly in all directions. So I wonder. If this could be a balancing problem. If so then machine learning have solved similar problems.

Then to improve the solutions I think a denoise_function(solution.reshape(biggest_rectangular_shape),weight=0.0001).reshape(org_shape) in the machine learning loop could be used.

So the idea is to improve the physics simulation in Blender.

Idea – Simulated Rainforests Manually Initiated

To create something like rainforests. I assume its not that easy. Much can go wrong. It is probably not just add seeds and water.

So when in doubt ?assume a machine learning network model.

I wonder if you can. Inspired by the old GIMP Heal Section function. Where you could magically replace a marked area with something that looked like the surrounding background.

So inspired by this it would be possible for a machine learning model patch = model(surrounding area). To replace the patch in the image or photo with a prediction of the patch area.

That is. The model would have learned from many examples what plants a rainforest would have at each position in the photos.

Then this prediction would be some inspirational data for a manually grown and watered successful rainforest.

Higher Math – Beneficial Truth Function Around 0.999… Repeating Decimal – Network Model Of Truth

The idea is simple.

One ?general way to filter truth is to see if its beneficial. Apply this to the repeating decimal 0.999… It then becomes a more precise truth function. That is. You have an answer = model(0.999…). Here a machine learning model will get you a network truth.

0.999…  equal 1 is True when its beneficial and False when its not beneficial.

Rain Idea – Creating The Environment Optimized For Rain – No Pollution? Salt?

Freshwater rain is ?meant I guess for life to exist. Following this guess I wonder if a decision network for rain does not want to waste freshwater if conditions are not right. If too salty or too polluted on the ground or lake.

That is. If conditions are so bad it absolutely does make sense to rain freshwater there. It probably wont either.

But there is a way. If people would help the condition. For example some alternative to covering the ground with 100% pavement?

If the physical network ?calculates by heat radiation or something else. That conditions are getting better. That the derivative is on a good path. Plants are growing. Then I think this would increase the chances of rain.

For instance. I think we need to instate simulated rainforests in urban areas with manual irregation. The humidity of many such places would be such a positive change.

Empower the planet. Optimize it for life.

Machine Learning Physics Speculation – Control The Speed Vector Of Probability

If something is complex enough assume a network. Here I speculate about the position of an object. In machine learning terms this position ?would be a classification.

That is. You have a set of integer numbers as the grid points. Then you get a position grid by hot encoding it like [1,0,0,0,0,0,0,0,0,0]. Here the object start a index zero. Then when you calculate a new position with pos_x = model(…) you could get something like [0.1,0.8,0.1,0,0,0,0,0,0]. I assume this can reflect the probability of the next position. Also I assume the misclassification are important.

If the universe is taking advantage of networks. Then this would be a general misclassification problem. But then again. Why not take advantage of the 0.1 misclassification. I wonder if this could be a way to get continuous movement. That is. You have a large but limited number of grid points and just probability in-between.

So the probability is tied to the grid points. Then at each iteration the probability of the position of the object changes. Then a goal of the universe is to control the speed of probability. The speed of the object.

Machine Learning Engineering – Decision Tree Of Motors In Electric Vehicles

Just as a philosophical possibility. If you have two motors there exist the decision possibility. To use  one or both at the time. Then could there exist a decision tree of ?small motors.

I mean. If you select motors from groups with each a specific capability. There could exist some advantage in each scenario.

Also I wonder. Even if the motors are identical. They would ?soon get individual characteristic. Some amplification of the difference in quality.

Machine Learning Idea – The Model Predictions Must Be Exact For A True Low Poly Version Of The Signal. Found Out That You ?Must Invert One Of Two -F.relu()

A strategy for getting the model just right could be to sample (not resample) the signal to a low number of true samples of the original signal.

This way when you increase the samples you immediately see where the problem arises in the prediction.

From this I figured out that you can try with great success to invert one F.relu(). That is.

// Per Lindholm


Computational Evolution Idea – Could Birds Have Turbulence Feather Memory For Prediction? Finding Food

The idea is simple.

Using machine learning you can solve impossible problems if you have the data.

Then since bird feathers I assume would vibrate in the air. I wonder. What problems could the data collected from this vibration help solve.

One problem is finding food and avoiding danger.

So storing the vibration time plot in some compressed way. Then it might be possible to match that signal with an unknown new route to find food.

So this could be a birds additional 6:th type sense.

// Per Lindholm 2018-june-10

Wild Guess – Does There Exist Smart Text Between Objects Like a Inspirational Transform In Machine Learning?

I wonder. Could you improve human thinking by calculating  possible object text transformations. Just connect two objects with some ?short text.

The idea is to predict the short text string. Even if you can not predict an innovation from others. The target is to get inspirational text. For our human imagination to work with.

This is what I suspect machine learning can help us with at the moment. Or is ?easy enough to do. Generate some text strings with some objects as input. Use that text as inspiration for some idea.

Empower Unemployed People Idea – Work For A Time Limited ?Project To Get A Rebate On A Car Or Driver License

The reason is simple.

The ability to generate income for a society is ?higher if people have cars. Its that simple.

It’s therefor of importance to empower people including the unemployed so they have a much higher chance to contribute to society and to today’s pensioners.

I wonder if its possible to make a much bigger rebate if you combine work practice with money for a car and driver license.

The path to success are different for everyone. Lets empower people.

Mathematical Idea – Using Simulated Noise To Stay Independent?

For noise to stay independent. I wonder. Do physical networks need to simulate its own noise and not transform input noise? Could this be important for heat? Turbulence etc.

So the idea is that if the output is classified to become noise. Then it could be better to simulate noise than to transform incoming noise.

Otherwise the random noise from the output would be dependent on incoming random noise.