Since we use cardinal directions north, east, south, and west as some kind of position coordinates for places. I wonder if you just can add a fuzzy radial component to this.
So my idea is to split the radial component into something like outer and inner. So with this my town is located north of outer Stockholm. Without the radial component the location would be somewhere in between what considers inner and outer.
I assume many functions that machine learning tries to mimic has divisions in them. With division comes to possibility for division by zero. However I think there is a relation between high risk and high reward in machine learning also.
For anybody interested in education check out kaggle.com. Its a social education site for computer science and machine learning. Apart from tutorial project competitions you can learn from discussions on subjects. Pretty good. This is what I would like for university higher education. If you could learn from talented students. We would get much better value for the time and money spent.
The idea is that we could have very effective higher education if we build sites like these for the other subjects.
Here is my take on imperfect mimicry. I wonder if there are such a thing as imperfect mimicry.
If you take evolution into a count then the specie might just give the illusion that they are a closely related to the dangerous specie.
An example would be a bird coming to a different island where the wasps look a bit different in terms of size, shape or more. The wasps would probably keep some similarity which would not cost so much in terms of performance, like the color of a car.
So, perfect mimicry is not necessary. The specie just has to fool the predator that it is related.
I think it should be called kinship mimicry, not imperfect.
My phone has double the number of cores (8) of my stationary computer and it was still much more affordable. This might be the case for some future to come. The phone being a device people buy a lot of. Drops in price much faster and you can afford higher speeds.
So here you can create your own smartphone wifi connected compute stick.
What you need is the android app Termux and some googeling. A repository is available for termux which enables you to install gcc, numpy, scipy of the latest versions. This for jupyter notebook. A web app that lets you connect on the internal wifi network to your phone from a web browser on your slow computer.
I installed via pip the latest keras with theano and sklearn. Great for novice users in machine learning.
With this you could enable fast computing with old CRT screen computers, raspberry pi and the like.
Below I connected the phone computing device with USB internet sharing. Typed arp in the command window to get the ip of the phone. Plugged that ip:8888 in chromium browser. You need to enable ‘*’ all ip in jupyter config.
A powerbank and a usb powered screen would be great. Solar machine learning computing.
With an external keyboard you can get started with machine learning on the phone. The procedure is not perfect. But I attached some screenshots. The main app to use is Termux. It does not hog the cpu as much or not at all.
For termux you need the “pointless” repository that installs gcc, scipy and numpy. The rest can be installed by pip.
DONT run all cpu cores on the phone. My phone (8 cores) overheated temporarily shutting own one core. However this is probably not healthy for the phone.
You can limit the number of used cores by limiting numpy, see screenshot. I limited it to just two cores. I ran some test for keras using theano as the backend. I also got sklearn installed from pip.
You can use pip to install jupyter. It installs a http server app you then access from the regular android webbrowser “localhost:8888”.
With this I think you can follow some online courses on machine learning that does not use the GPU.
90 seconds per batch 2 cores mnist_mlp.py (theano)
If people pay a lot of money for games they might be interested to get the most out of them. Then there should be a demand for general as well as specific game playing courses. So I think creating courses for games could be something. This way gamers can profit from instructional material also.
I wonder. What would happen if you add rules for the weights in machine learning matrices inspired by molecules and atoms. I suspect it could get some clustering and pattern formation going. With large hidden layers I think you get some more flexibility for connected rules that make the weight matrix layer look like some kind of image.
The idea is to study these formations. Like an engineer would study materials or metals.
Could you then use this for direct weight matrix creation. That is with minimal training.
Another idea would be to create a network that does not need to do memorization. The idea is simple. For recognition of objects in pictures. At the classfier.predict() function you input some known images also. So if the network are about to learn letter fonts then the network should be able to take advantage of images already availible and labeled correctly. This way the network does not need to store memorization information in its weight matrices.
I imagine one advantage of a network like this is that you dont need to retrain it. Just add images that have correct labels.
So you got smartClassfier.predict( imageToPredict, imageDataThatHaveCorrectLabels )
To get a little money for education. I was wondering if schools could post interesting projects they would like to get funding for. Perhaps these projects can serve the rest of the public in the form of needed courses or material.
So the idea is for the school to get funding for elearning courses they can then charge a little money later for online. This money could then go to supporting free education for students.
A little money is perhaps is little modest. I have seen MOOCs with 10000+ paid users where the price was 599$ per student. That money could be spent well on the school.
Projectors are an important e-learning tool in schools. So why should there not exist a Linux distributions for projectors. Here there is a chance to make a first impression. What would be expected of a Linux distribution to be projector friendly? I mean a desktop is different from a phone os. So maybe something else could be done for the projector experience?
The guess is simple. Turbulence looks as if it has random elements. Random is used for generating solutions for computational problems. This should mean that turbulence is not a side effect. It is suppose to solve a problem.
So my guess is that is that if you have a construction that has a turbulence problem it is probably better to make a new type of construction.
I speculate that there should exist recognition beyond 100%.
Take free will for instance. Since you can classify simple robotic behavior from living things. You can recognize free will.
So the idea is that life started with more robotic behavior and very little free will. This because very little free will is easier to achieve through random events.
The super recognition is then needed because 100% recognition of the early free will is still very crude.
So inspired by this I speculate that we need a ”evolution” score together with the recognition percentage of 0..100%
Then setting the target of a generative process to maximize the score and updating the recognition would give you more than 100% recognition. Compared to the orginal data.
So maybe there should exist something like unsupervised ranking. That is. Putting objects with nearly the same quality into the same group. Here the algorithm could perhaps use two objects with a predefined ranking. An easy problem would be if the two objects represented the highest and the lowest score. Placing the rest into n groups should then be a lot easier.
Then with super recognition you could perhaps generate music that sounds better than the input data. So I speculate that there should exist something like super recognition so that you always can say that object A is better than object B.
I wonder if it could work by enhancing the weight matrix. If the weights are turned into parameters. That is. The parameters are data for a smooth 2D image. The image is then used as data for the actual calculating weight matrix. Much bigger. Then the image could follow ?simple transformation when more input features are added. Maybe then it could be enhanced with post processing filters at same time. Improving the filters would then also improve the network. Filters could include noise reduction, detail or sharpness.
Everything can be classified into groups. Its the core of machine learning. Therefor should not the tools in poor areas or slums be classified into groups. The reason is that you can adapt them for the environment and needs. So the idea that these food making machines or tools are cheap to repair, can be run on limited solar power and be effective enough to make money out of.
The real potential for solar energy could very well be for enabling electrically enhanced livelihood and elevated standard of living.
One idea could be for a TV program like Mythbuster to develop needed machines.
Could you improve Monte Carlo simulations by using input from different classified random distributions. What I guess is that machine learning could make something like a ?focus point where the result will converge.
So the idea is to use different distributions like normal and uniform with different parameters and feed it to a machine learning algorithm. Then see if it could be used to make the problem solution converge quicker.
What I would like is for the 2D software like GIMP or Krita to be able to handle 3D objects.
I think even simple low poly objects can work wonders for 2D drawings. You just create 3D objects like cylinders and boxes as drawing guides or helpers.
With this you don’t need to rotate the image in your mind. Just rotate the outlining 3D object for a new view.
Also. Like here in Blender you can layer in 3D and move or bend lines in 3D. Some A.I software could perhaps know where the line is in 3D space I would like it to be or present some suggestions. I tried to draw in 3D but the line was drawn in the wrong plane. Here machine learning could perhaps choose the right 2D plane in 3D space.
3D layer Blender Grease Pencil 3D Obj Idea For GIMP or Krita
There are probably a lot of single board computers like the raspberry pi that are just sitting idle. An idea would be to have a second-hand site for these.
There are several reasons for this. One is that you might think you have to many of these boards and you want to buy the latest but you don’t want to discard useful technology. Another is that maybe people don’t know what to do with the computer until you try some programming or projects.
After watching sonoluminesence. I wonder. Could very high temperatures (?20 000 K) affect gravity. I mean. The bubble seem to stand still. Even if this is caused only by the sound generators. Two stabilizing effects could take place at the same time?
From this I wonder if the sun could adjust its gravity. I mean. For a large hot object. It would not be so safe if it took off in some random direction. Or having its surface move like a ?tidal wave. Having a temperature of millions of degrees. It has plenty of energy for this would be ?smart adjustment.
I would be surprised if such a large hot object like the sun cant adjust a so important parameter like gravity even a little.
To spread solar in rural Africa and elsewhere where access to electricity and Internet is limited. I wonder. Could the cost of a solar kit be partly payed by ads. The idea is that you store good music on a memory in the battery device with ads in between the songs.
If the music is good then people will listen to it and at the same time local and city businesses can reach out.
So the idea is to equip the battery device in a solar kit with a built in digital music player and upgradeable music. Perhaps the ads can be updated by the phone signal.