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Food And Drink Innovation – Are there ?natural cup structures that can enhance flavors of coffee or tea? Like an orange has an optimized outer skin

November 25, 2018 Per Lindholm

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Previous PostSolid State Battery Search – If A Solid-Battery Would Melt A Little Would It Be Easier To Discover It? // Per Next PostVelocities Can Create Random. From model(Random) You Can Create Everything. Gives Me That A Futuristic 3D Printer Could Be Built Like A Particle Accelerator. The 3D Object Is Then Built From 2D Sections Of That Circular Tube As The Particles Hit The Surface // Per Lindholm 2018

Ideas Are Free To Use As Long As You Give Me Credit

By Far The Smartest Idea - Hot Milk With Cooked Rice In A Cup

If free will can be recognized in the machine learning sense then it could probably also be optimized with max as the target. // Per Lindholm

Is a proof some knowledge of the probability of the network.

The Universe Is A Compression Decompression Model() // Per Lindholm 2018

Peace Idea - Any Type Of Probability Can Move Us Forward. Peace Will Do It 100% // Per Lindholm 2018

:wow

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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
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?
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
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?
?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
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
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.
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
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.

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