Could there exist green heat? The word combination is simple and there can not exist any surprises to the universe is the guess. Wow! I got it maybe. !!Greenshift!! vs redshift vs blueshift. It has to do with heat.

# Category Archives: Uncategorized

# Spiral galaxy is heat teleportation ( large distances ) going outwards to ever further away mass.

# Super Homeless Strategy – Donate a business. Its a simple idea. Teach a man to fish and he … Here you teach a group of homeless a donated business. Tiny home rental bitcoin service? Café etc. So Business Donation is the key idea. // Per

Super Homeless Strategy – Donate a business. Its a simple idea. Teach a man to fish and he … Here you teach a group of homeless a donated business. Tiny home rental bitcoin service? Café etc. So Business Donation is the key idea. // Per

# Water Treatement Guess – Since cold water tastes fresh maybe salt residuals get teleported away when the desalinated water is ice. Worth a taste check // Per

Business Idea Guess? – Freeze desalinated water bottles for better taste and body health.

# Water Treatement Guess – Since cold water tastes fresh maybe salt residuals get teleported away when the desalinated water is in ice form. So freeze desalinated water bottles for better taste and body health // Per

# Desalination Idea – Since desalination RO are tubes is it possible there exist focused desalination?

Desalination Idea – Since desalination RO are tubes is it possible there exist focused desalination? Like a camera lens

# Atom Physics Machine Learning Computing – Adapt input light so that when sampling the output light noise in a reflection setup. It solves a problem. Then adapt so model for the input light problem can solve the test set of problems with correct answer. All is supervised learning.

# Similarity Sampling and Iteration – Wow this method of mine works very well. You pick a random index vector and then select from X_train almost identical sampels to train with in a repeated iteration. Got 98.6% MLP

# Breakthrough Machine Learning – Generalizing loss. Transform the loss 0..1 with a model2 the loss of model1. So if the loss oscilates wildy the generalizing loss dont. Its confined between around 0..1. Can the iterate with the same input for 100000 times without degrading. IDea and method by Per Lindholm

Breakthrough Machine Learning – Generalizing loss. Transform the loss 0..1 with a model2 the loss of model1. So if the loss oscilates wildy the generalizing loss dont. Its confined between around 0..1. Can the iterate with the same input for 100000 times without degrading. By Per Lindholm

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# The Halting Problem – You can also learn if the function is going to converge or not. By random choice selecting between at=loss for converge = 1 and at= np.random()*loss for converge = 0 Then a model2 learns the converge label. Voila I guess the Halting problem is solved?

You can also learn if the function is going to converge or not. By random choice selecting between at=loss for converge = 1 and at= np.random()*loss for converge = 0 Then a model2 learns the converge label. Voila I guess the Halting problem is solved?

# Machine Learning Idea – There are alot of zeros in machine learning target. So what if you can make patterns in the zeros. Assume some kind of zeroToSmallValue Pattern is beneficial to the classification. // Per

Machine Learning Idea – There are alot of zeros in machine learning target. So what if you can make patterns in the zeros. Assume some kind of zeroToSmallValue Pattern is beneficial to the classification. // Per

# Machine Learning – Generalizing Idea – Sometimes convolutions produce shadows. Shadows from different objects can appear the same. I think you also get generalization from ||derivative similarity||. With a some small number of value similarities. // Per

Machine Learning – Generalizing Idea – Sometimes convolutions produce shadows. Shadows from different objects can appear the same. I think you also get generalization from ||derivative similarity||. With a some small number of value similarities. // Per

# Safe Affordable Life – Innovation – Wash Your Clothes Securely at the workplace. #Africa #Sweden #US

# Electric Airplane Energy Strategy – Since electric motors and battery are good at acceleration. Could a strategy be to accelerate faster the plane for longer periods of time. Accelerate – glide – accelerate? for electric Cargo Planes?

Electric Airplane Energy Strategy – Since electric motors and battery are good at acceleration. Could a strategy be to accelerate faster the plane for longer periods of time. Accelerate – glide – accelerate? for electric Cargo Planes?

# ?Lindholm Convolution – Inspired by random shape images. I wondered what x belong to an image which pixels colors are assumed to be random. cumsum(x-mean(x)) = image. I think I found a convolution style. // Per

?Lindholm Convolution – Inspired by random shape images. I wondered what x belong to an image which pixels colors are assumed to be random. cumsum(x-mean(x)) = image. I think I found a convolution style. // Per

# Association Intelligence – Random numbers has something to do with shapes and figures. Since you sometimes (randomly) can recognize shapes from a set of random numbers. ||Organic shapes are so difficult that they are adapted towards random numbers||. superWow! // Per

Association Intelligence – Random numbers has something to do with shapes and figures. Since you sometimes (randomly) can recognize shapes from a set of random numbers. ||Organic shapes are so difficult that they are adapted towards random numbers||. superWow! // Per

# Math Idea – Looking at Schrödingers equation. Every topic in math should be able to exist in a differential equation. Geometric diff. eq. Statistic diff. eq. Algebraic diff. eq. Programatic diff. eq. ?Analytic diff. eq.

Math Idea – Looking at Schrödingers equation. Every topic in math should be able to exist in a differential equation. Geometric diff. eq. Statistic diff. eq. Algebraic diff. eq. Programatic diff. eq. ?Analytic diff. eq.

# Remember Einstein: He made classification problems. Cold fusion could be a property light emission problem. Put a different atom layer between two equal atom layers. That’s enough for a classification problem//P

Remember Einstein: He made classification problems. Cold fusion could be a property light emission problem. Put a different atom layer between two equal atom layers. That’s enough for a classification problem//P

# Machine Learning Physics – Guess – Continuity Fusion? – I assume there exist a property effect when you got three layers with another atom type in between. Apply energy and does not want to interfere with the light emission. Otherwise a classification problem. -> Property fusion.

Machine Learning Physics – Guess – Continuity Fusion? – I assume there exist a property effect when you got three layers with another atom type in between. Apply energy and does not want to interfere with the light emission. Otherwise a classification problem. -> Property fusion.

# Machine Learning Physics Guess – With Quantum Loss functions maybe there are also Quantum Fusion. Since there is so much energy. A smart way would be to extract energy on a small scale. Quantum Fusion could be similar to glow plugs I guess.

Machine Learning Physics Guess – With Quantum Loss functions maybe there are also Quantum Fusion. Since there is so much energy. A smart way would be to extract energy on a small scale. Quantum Fusion could be similar to glow plugs I guess.

# Purpose of Materials give Innovation Clues – Guess – I wonder. If you know what the planet and ocean uses the material for. Could this information provide clues where we could use this material. By viewing the materials or atoms contact history. (in water?) // Per

Purpose of Materials give Innovation Clues – Guess – I wonder. If you know what the planet and ocean uses the material for. Could this information provide clues where we could use this material. By viewing the materials or atoms contact history. (in water?) // Per

# Machine Learning Physics – Materials are models() and as such they could store knowledge in the form of loss functions. An advanced universe will have figured out how to store a lot of loss functions without losing information. Oil, water, wood, rocks, air.. all carry information

Machine Learning Physics – Materials are models() and as such they could store knowledge in the form of loss functions. An advanced universe will have figured out how to store a lot of loss functions without losing information. Oil, water, wood, rocks, air.. all carry information

# Simulating 77 with a list of target data points and a model in between. Difference between absolute error and squared. Both are ?not suitable for diff eq. Maybe a diff eq is a perticular loss function diffloss.

Simulating 77 with a list of target data points and a model in between. Difference between absolute error and squared. Both are ?not suitable for diff eq. Maybe a diff eq is a perticular loss function diffloss.

# Machine Learning Engineering – There is another way to think of the world other than in differential equations. Using a x = model([1,0]) as the position. You only need a list of targets a list of xt values. Then when the first target is met you change to target two.

Machine Learning Engineering – There is another way to think of the world other than in differential equations. Using a x = model([1,0]) as the position. You only need a list of targets a list of xt values. Then when the first target is met you change to target two.

# Machine Learning Physics Guess – Could 1/(x+273.15) for x in Celsius degrees exist in some physics equation since you cant go lower than the absolute limit. There must then exist a singular equation with the temperature. // Per

Machine Learning Physics Guess – Could 1/(x+273.15) for x in Celsius degrees exist in some physics equation since you cant go lower than the absolute limit. There must then exist a singular equation with the temperature. // Per ( Like in black hole )