Looking at pictures of old sheds. I wonder. How much has the shed meant for developing the country. Is there a way to innovate here. Is there a possibility for a new design. An affordable shed for rural Africa perhaps.
I’m guessing. Your are more inclined to buy carpentry products if they don’t get missing. So this will then enhance rural livelihood I think.
Machine learning allows for imagination to solve complex problems. Take a super battery for instance.
I imagine you can use image color data for this. Different metals have different colors.
So an idea is to use these image data for an inverse calculation. You got images of the battery on one side and maybe heat generation on the other.
So here I train with temperature as input and the images as output. Since we want to ?lower the heat generation after training. We just put in a lower temperature and see what the difference of the color in the image data that was predicted.
This will then be an indication of a better battery.
Maybe 3D software like Blender could help. Rendering a more generalized battery with the help of transparency. But still with the correct realistic colors.
I think machine learning ideas like this one could give us a peaceful future and a electric car future at the same time.
We have cleaning firms but what if you have a firm that helps ordinary house owners prepare for selling their house. See what typical things the owner need help with to sell their house and at the same time increase value. I guess it could be work in the garden, sheds or indoor. All the things that is requires too much effort for old people.
So the idea is for a House To Market Preparation Firm.
As leaves when shined upon show a little transparency. I wonder. Why is transparency a good choice. My general answer to the question why is that whatever it is it should make sense to network. So this could then effect the network. And if so how would this be beneficial.
In machine learning. You can add many columns of data. It does ?not matter if some columns are less important. So the leaf being a network in 3D. Light would get the network more data. More columns in the 3D physics network space. It would make the processes ?better.
So I speculate that there is some efficiency gain because a network with lots of good data can converge faster. Get to the right values faster.
So I wonder if a battery, electric cables could also benefit from some sort of transparency being physical process networks.
If you zoom in on many functions they just look like a line. But nature does not look like that. There exist a nano world. Fine grained structures.
So I had the idea that you could let a machine learning network train on the few examples of fine grained resolution there is. Like ?fractals. And carry that information over to ordinary functions.
Could you have functions that hold more scale levels. That is. A smaller boundary. Where you still get meaningful information at the particular scale. It looks more interesting than a line at more scale levels. Whats the practical zoom level where you can still have some useful information of the function.
Could you use machine learning to first resemble a function A then turn it into a function B at higher zoom levels?
Thats my math speculation for today.
Heres is an odd looking function. The two functions in one function function.
If you have money coming in then rebuilding anything is easier. Therefor I wonder if companies during the rebuild of areas can share buildings as they get constructed. This way the economy is more resilent.
I was thinking. Machine learning is probably very important for musicians using digital audio workstation software. With lots of parameters. It should be possible. With the proper error functions. To fine tune parameters of everything in the audio project. For example vsti instrument settings and effects and more.
The error function is the most important part. How can a network tell if the audio sounds good. Here some creativity is needed. Are there a number of smart error functions we can develop? Or maybe its not necessary maybe a network can learn to recognize each type of music and improve as much as possible with the settings.
Another idea with machine learning is to cooperate with the algorithm. If a simple algorithm can predict a tune then a listener can do the same. The idea is that this will make the song more pleasant to listen to. That is. You change the melody to be a little more predictable in the style of the music.
I think stationary computers are used because the are more adapted to rugged environments.
So I thought. Why should schools in Africa throw away their stationary computers when there should be a possibility to upgrade them. I was thinking that one could use Raspberry Pi’s or other single board computers.
Since there exist cheap hdmi to analog vga adapters. An old CRT monitor would still be of use.
However come to think of it. Considering the cost of electricity. A CRT screen is a bad choice. Much too warm and power hungry.
So if they could get hold of the raspberry pi’s together with used lcd screens I think power costs can be kept down.
I was thinking something like this. An equation is a little network that is easily discardable if there a single error. Like you could get 0 points on a math test. I then guess this means a bigger network is harder to discard. A parallel network could be a simpler approach because all the combinations of the meanings could have some truth to it.
So the idea is to write several parallel texts. Because if you try to melt all thought threads into one sequence it takes longer and you might make wrong mergers.
One example of this is to make comments to the original text.
If a problem of high complexity requires a complex weight matrix. Then I think you can just iterate and ?determine it by using Gaussian filter in the process of creating the weight matrices.
Then by looking at the softened weight matrices or using them in a classification network you then get a number or label of the ?complexity. With this I guess you can then later adapt a non smoothed network with the right model. That is. This method determines if the problem is easy or hard.
If you classify the different complexity and its corresponding model setup I believe a method similar to this one can help solve hard problems
If anything would solve the complexity problem it would be a network.
So I thought if I get a smooth converged weight matrix. Then train on an additional set of samples from the test set and then compare the change of the weight matrix to the original. I guess this would reflect how difficult the problem is. The ?more change. The more problematic the problem was.
Could this be a way to predict how hard a problem is. I mean. In the brain we think a little bit about the problem then decide if its difficult or not. So inspired by this. Could a machine learning network solve this question?
I was wondering why pytorch did not work on my AMD x4 computer. It seems it is too old. Does not support SSE4.
So I tested with success the Intel Software Development Emulator with pytorch and cuda enabled.
Yes it worked. Using only the CPU took more time than I would like to wait. But boy using the gpu. The GPU went up to 100% and the calculation took just a second. Way fast.
So with a CUDA enabled graphics card you can run pytorch on an old cpu.
Idea – Only emulate whats missing
This gave an idea. Is it possible to make a similar emulator only for sse4 and future functions? So the emulator does not emulate everything. Just what is missing. Otherwise I think its slower than it have to be. Then we could have that running by default running in Linux.
Has anyone tested to improve audio samples with machine learning? Like there exist super resolution example in pytorch. High quality samples for LMMS maybe.
I checked the wikipedia summation of a fuel cell. It converts chemical energy to electricity by oxidation.
So my idea is. Can you use a solid material instead hydrogen gas?
I wonder. Could there exist a reaction between silicon and oxygen if you just slam some oxygen molecules into the silicon. Is there a efficient way to create a reaction? Could this be a solvable problem. One of many to a sustainable energy future.
So the reason for the idea is that I think we need abundant materials like silicon for devices like fuel cells.
To get a force you need something like two charges. Two electrons maybe. So if you have something like gravity that produces a force then there should I guess be a ?temporary electron emergence caused by the gravity.
Maybe the speed of gravity being close to speed of light results in a ?temporary electron some short time after the gravity has hit the atom. Then it would result in a attracting force. An electron repelling from behind the atom.
If you rewrite a physical law as a 100% rule then a valid guess is that there are probabilistic rules.
So maybe there exists a motion law with efficiency. That is. For every energy unit in you get a chance of forward force or generation of light. Here it could be that you get 10% force and the rest as light. Maybe light generation in the atom has some odd states that only happen at a low probability. Which then generates a force.
Maybe an atom can generate a new ?temporary electron which it can use to absorb energy as motion if the energy transformation options are limited. Then from electron to ?temporary electron repulsive forces you can get motion.
Then it should be possible to create a probabilistic drive.
Part of this idea. The predicted electron generation is due to the universe being optimized not to harm life. So from this I guess that the universe is also optimized towards drinkable water. So here you got another smart power sentence to build ideas from.
( A guess of mine is that atoms can under ?very cold temperatures generate an electron cloud. Giving it some above zero kelvin temperatures. To maintain functionality. )
Besides housing needs. Particularly in the winter. Entrepreneurship training that leads to even the smallest livelihood is good knowledge and knowledge is something that you don’t loose. So regardless if they stay or return home. They can build a business here or there. Train others in the same skills.
I believe this is a sustainable way forward. Include everyone.