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?
The idea is to work the input towards an easier recognition. For a letter dataset this would mean that the first network makes a cleaner version of the hard to recognize letters. That is. A smart transformation that produces a simpler to recognize image.
I think this would mean that for already clean looking letters there would be no different image.
I wonder if you can take the images with the best confidence of score of recognition. Use them as examples for the rewrite network model. That is. Use the correct best selected images with the usual data,label network.
This so the network can ask if the generated image is clean or fake. It competes with the set of most recognizable images. That is. I think I should mix in all the recognizable images from the training set into one set of most recognizable images. To extract the clean property I mean.
Then I guess I run the procedure in reverse. First the clean competition then the recognition network.
One problem could be that if non letters get into the mix they would get produce the wrong decision.
So another problem is to recognize if its a letter at all. Again some network that tells if the image is a letter or fake. It almost feels like this is a kind of biological mimicry. Fooling the network that you belong to a another set of specie. But here we have another property, eatable.
For biological mimicry the fooling specie I think should be most successful if it can stay relatively close to the target specie but not too close. This so it will not try to reproduce with those it cant and are not eaten.
The little cheap Sound Enhancer hardware was a great addon to the Amiga Computer. I always wanted that sound ever since. Now hmm a little late I tried the calf audio plugins with Linux. So I thought I run some Amiga Demos from youtube into the calf audio plugins via qjackctl.
You need QjackCtl, pulseaudio-module-jack and calf-plugins. Which you apt install from the terminal.
Then you run ”pactl load-module module-jack-sink” in the terminal. Then you need to select Jack sink under the sound settings as output.
Be sure to check qjackctl so that you dont have multiple outputs to the system out channels.
I was wondering. How do you improve the train station experience?
If you start simple. To see if things work. Why not add an entertainment page at the website. The idea is to show something like Swedish nature films on the SL.se. Just youtube links. Then let travelers vote.
So you can create competitions on the best nature films. By the public and professionals. I got this idea from a TV program where they showed nature photos from places around the country from viewers.
High Resolution cameras are cheaper today. So why not take advantage of them.
Then maybe in the future if successful we could have best films air at the train stations somehow.
What if there is fast way to make a photo super high resolution?
Splitting the photo into a larger grid with photo pixels and additional transparent pixels should do the trick. Just use a GAN network to imagine the extra pixels to real looking color pixels. An extra pixel could be stored in a separated pixel matrix.
Then this could work for other GAN problems. Just use the model on a small downscaled photo and then apply pixel separation and GAN imagination to increase the resolution. Should be much faster than to do it all at once for a large image.
I was wondering. Philosophically you can place balls of steel in a 2D pyramid and apply a little voltage. So you created something that looked like a machine learning network.
So I was wondering. Would it be possible to learn a little more about the electric network. For use as inspiration in batteries and motors.
From my machine learning speculation it could be that the layers of the network oscillates a little in finding its target weights. Knowledge about this could perhaps make things more effective?
Are there electric machine learning elements objects one can use to enhance motors and batteries for instance.
Thinking outside the box creating a parallel electric network. Instead of the crude fully connected network. Convolution networks are important in machine learning. What counterpart is it possible to create physically.
Maybe we get a CPU Network electrical Wire in the future.
Using a GAN network I think I have the answer to the many difficult questions. Noise. Noise put trough a generator and iterated with the help of a discriminator can create just about ?anything. From mass, energy, time and so on.
So if you want to travel in time you need to figure out a way to generate mass and time from noise and iterate it with a discriminator associated with that mass and target time.
So to understand this idea use the following guess sentences.
Because it makes sense to a network.
Since it makes sense to nothingness.
So we have one new idea to the why question.
So in my mind what came before nothingness. Maybe there is some math here. If time is a generator(noise) and noise is related to frequency then there should be the possibility the noise somehow restarts itself. So we got the start of a new time ?counter. To make use of a fully expanded universe looking like empty ?noise another generator is needed perhaps with new time to use.