The idea is vital for our survival.
We cant eat solar panels but we can eat food and both use the sun for energy. Therefor for our future. For our survival. I believe we need to make smart business opportunities for people. Even if the money invested per person is small.
Food costs a lot for people every day. If they could invest in food production, farming, innovation etc. Then get ?cheaper food. Then we would create a very smart resilient future.
For instance. The deserts could be green with food crops. Its going to cost money in innovation and tech but the planet is worth it. We are going to save it.
If an image can represent different voltages in the pixels then its possible to have higher version potential differences. Then there should exist something like physical voltage maps.
I suspect a network model of voltages can then be useful.
From this I would not be surprised if future batteries are aligned with gravity. Like lightning when discharging.
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.
A quick idea.
When data is not perfect. When its missing values in some column.
I wonder. Should there not exist a multidimensional function like the GIMP Heal Transparency?
With this you could fill in missing values with realistic predictions.
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.
Could information be stored in a material? I mean if you use an error function as a criterion between steel and plastic. Then maybe random() as input to the material model could make plastic with steel properties.
Something like that could be an abstract version of material design.
Since you look people in the eye. The recognition starts there. If you have on a pair of ?none strength glasses then peoples opinion changes.
The idea is that this ?could improve opinions and make it more safe. Housing first is still top priority though.
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.
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.
Just a quick idea.
Could electrons have process programs? I mean. It would make something like the sun more safe I guess. So what what does the electron need to do in the sun. What is its algorithm?
Im thinking. This could help us build more efficient batteries and motors. Better electric cars.
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.
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.
Just exploring the range of possibilities. There exist something like elastic membranes. Could something like this be used in a battery?
Just some speculation.
Since a process is a set of activities that interact to achieve a result. Then could atom forces be results of a processes.
If so then the process could on a small scale be like that in a CPU. A program.
So the idea is to speculate if atom forces, magnetism, gravity have process law programs.
The idea is simple.
What about little-meat products. With this I mean mixed meat products where the % veggi is much larger than the meat amount.
I think this could be a real game changer for the environment and peoples health.
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.
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.
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.
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
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
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.
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.
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.