Author Archives: Per Lindholm

Fractal Machine Learning Idea – Geometric Seed Shape Values From Layer Weight Matrix Outputs?

Looking at face recognition where there could be shapes combining eyes, ears, mouth and facial expressions. I wonder.

Could this be used for some sort of fractal version of machine learning. Like the old fractal mountain landscape generator.

My idea is to see if shapes could be used on the output from the weight matrices from the various layers. That is. You have some ?grid like shape that you drag connected lines on. Making the output shape look different.

Change the shape a little and see what happens. What can it be used for?

So this could be a GIMP or Photoshop like layer where you input insert non optimizable parameters. Like colors, contrast, local scaling etc. I think it can be artist preferences.

That is. Easy changeable user input. Like the mountain seed generator where you change a single number.

Job Idea – Build Your Own Tiny Business-Home In A Startup Village? Get Your Team Build-Experience And The Confidence To Build A Company?

Like a summer house village where people buy a piece of land to build a house. Why could not a startup company build their own home from scratch.

Get knowledge from house construction to physically get a build-experience. To get your company to the position you want.

So the idea is to iterate your team member experience and confidence to a perfect start.

Sustaining the Gradient Of Vegetation Growth In Any Desert Is Important. Grab The Chance. Sustain by manual watering?

I wonder.

I think the ?only way to get sustainable rain in the arid desert is to manually water those places which has gotten som rain recently. When the rain hits those places again you will have a ?secondary effect. Then a third.

The idea is that after this the weather ?network would have picked up on the vegetation changes in that place to direct more sustainable natural rain.

A Very Important Idea – Let People Invest In Farming And Get Cheaper Food?

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.

Higher Abstract Version Of Potential Differences – Network Of Voltage Maps?

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.

Higher Math Idea – Calculate With Raytraced Render Objects Instead Of Simple Boarder Defined Objects?

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.

Physics Guess – Energy And Information Black Hole Energy Bundle?

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.

Wonderful Idea – Linear System Model Of Machine Learning – A,b = model(…) Where Ax = b

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.

Machine Learning Physics – Could The Physics Solver In Blender Be Improved With Machine Learning And Denoising?

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.

More Efficient Electric Car Idea – Sun Inspired Electron Process Program? What Does The Electron Need To Do In The Sun. And What Is The Algorithm?

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.

Idea – Simulated Rainforests Manually Initiated

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.

Higher Math – Beneficial Truth Function Around 0.999… Repeating Decimal – Network Model Of Truth

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.