A cheap way to get digital education was to use a projector or ?TV. Then to make it interactive I would see if a wireless USB Pen Mouse could work. For writing or drawing. You don’t run the pen mouse on the projector screen or TV but on a table. Since the pen mouse is pretty inexpensive I think it could be an idea.
Then I wonder. Could a wireless pen mouse and a phone/tablet replace paper and pen? The problem I had not realised was that you don’t need to write on the screen. Better to use the large space of an empty table and transfer the pressed movement to the mobile screen. This way you have a pen with different colors and lots of large virtual paper.
”In quantum mechanics, the uncertainty principle, also known as Heisenberg’s uncertainty principle or Heisenberg’s indeterminacy principle, is any of a variety of mathematical inequalities asserting a fundamental limit to the precision with which certain pairs of physical properties of a particle, known as complementary variables, such as position x and momentum p, can be known.” – Wikipedia
I guess that the reason for the uncertainty is that no particle should ever easily enter a singularity. Where some property like gravity would be too extreme.
?So in order to achieve this safety the universe could perhaps use a function for the particle. Since the laws should be the same everywhere. My idea is that this function enables the particle to bypass a singularity.
That is. If the particle gets close to the singularity. The uncertainty gets larger as it approaches. This way I guess it can bypass it.
A desertification method has been to plant for trees. But I wonder. Since the goal is to get an environment optimized for life. Perhaps insects should be part of it. Since insects go along with plants for a ?reason.
So the idea is. Can optimizing for insects be the second level of fixing desertification?
As I understand it Markov chains are based on random. So it will produce a series that could have been. So to me. That resembles handwriting. With Markov chains you got many different outcomes. So I think they could be useful as training examples.
We place a lot of money to get students through college. Be it rent, books or food. Since there is constant shortage of student housing. Rent could be a large part of this cost.
I was wondering if there exist an additional approach. What if student could get CSN loans to buy or rent a tiny house.
A tiny house as defined by wikipedia https://en.wikipedia.org/wiki/Tiny_house_movement . A small house on wheels that is very affordable.
One idea with this is that when the student is done with their studies. They could sell this tiny house and earn a little profit and at the same time have some money to payback the tiny house loan. I mean houses often increase in value. So why should not students also benefit from this.
Perhaps there should exist a possibility for students to take part in the build of the tiny house. This way they get valuable education that can build up any country.
My dad had a camper when building the summer house. A tiny house could perhaps serve the same temporary housing problem when restoring a house.
I wonder. Could Newtons gravity law give inspiration to another form of gravity. I mean. It describes the force between two objects. So what if one object is another type of object. Like a empty space object.
Then does there exist Empty Space Gravitation?
I recall the universe is expanding. Since something that is almost empty is a lot easier to expand. Then could empty space expansion and gravity between mass and the empty space object explain this expansion?
I thought I test a little VR. Found a Blender tutorial which showed how you can make a starfield in 3D. I have just the phone version of VR. So I made a side by side version. One tricky problem was that you have to add *_L and *_R on two camera names for multi-view.
Will add a video if I make something interesting to show in 3D.
Jobs are something real something everybody understands. Thats why I’m wondering if as part of an action plan. China and other countries perhaps could outsource jobs to North Korea. To stabilize relations. To bring some hope to the situation.
In wikipedia it says “A clock that is under the influence of a stronger gravitational field than an observer’s will also be measured to tick slower than the observer’s own clock”
I wonder. Does It make sense to a network?
If you draw information transfer lines ( ?gravity lines ) between the big gravity object to the small object. Like the input data vector in machine learning. The small object needs to read all those lines before continuing its update. Then could an increase in input data the size of this vector cause a larger delay perhaps.
Further with my network theory in mind. I speculate that many things have time dilation. To be able to get deterministic physics.
An idea is to classify reactions. That is. The number of different reaction results are limited. Then there is a classification problem. That is. You need to wait before one class is more probable than the other.
I experimented recently with predicting histograms cutting the time series into slices and learning the histogram for each piece. Then using histogram(i) to histogram(i+1) predictions.
I wonder if the same could be done also with objects like machine learning networks. That is. Predicting the network parameters.
So the idea is to cut the time data into slices. Then I will try experimenting with setting the input as object(slice (i)) and the target as object(slice (i+1)). After training the model. I ?will be able to predict a network model object for a future object with the last object as input.
I probably need objects that does not have so sensitive parameters so it allows for a little error.
I was wondering if you could use machine learning for interpolation. A use of this method could perhaps be to control overfitting.
So you have discrete values in a target data vector. Zeros and ones. My idea here is to find a smart machine learning interpolation such that overfitting is reduced and output of the machine learning network converges to the discrete target values and is able to predict correctly.
Basically the in between interpolation curve should also make sense as well as the target values. Is it possible to give a large network a nudge in the right direction? Fitting for the smart interpolation curve.
Could information be data that make sense to a network?
So you start with information that makes sense. Some measurement. The target information to a machine learning network model. Then the idea is that you test some input data to see if you got some reasonable accuracy. Then you can say that the input data makes sense to this network. Which is based on sensible information. So the input data is relevant information to a degree which make sense to a network.
I think you can split the information into time information and frequency information ( histogram ). The frequency information is ?always present. Like different distributions that are somewhat predictable throughout the data.
To see the problematic areas of your training. After the training is done. I guess that it could be good to cut the time series into slices and compare loss for each time slice. It should show where the model has problems and perhaps ways to improve.
Image generated after one epoch. The plot shows different loss for different times of the year. Could it be that summer time has more energy to turn things around. Making a prediction more difficult?
I came across the desert twin water producer when search for ”water from air desert”. Hmm. Two parts. This sounds like a two part air conditioner with refigerants.
I think you can run an air conditioner on solar power these days. So you have one part for lowering the temperature for water extraction and one for removing heat from the process. Like an air conditioner. The idea was to build something upon availible technology to make it cheaper.
By generalization I mean handling more inputs. Then a strategy could be to update the weights so that you get a non random looking image. I think you can do this by applying a noise filter to the matrices.
Then to get a better generalization I wonder if you can apply a Google type super resolution to these weight matrices that now look like images. So with this you have updated the weights based on experiences from many previous images.
If something like this works then you have effectively trained your network with a ?lot of samples you did not have.