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Monthly Archives: February 2019

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Machine Learning Invention Speaker Piston Cylinder – Where Every There Is Pressure To Convert Heat Into Work. I wonder if you can have a speaker with anti sound for ?higher efficiency. So you have a piston and a metal speaker in the cylinder. With iterated sounds for more work.So you ?can run a passenger plane with a small number of fuel piston-sound engines to electric motors

February 27, 2019 Per Lindholm
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Machine Learning – Select Reasons – A reason could be select elements in A that is equal to B. Closer to the truth. A=B. Another reason could be select elements which are close to a regression line of two or more model_i() outputs. So a reason could be an equation Ax=b.

February 27, 2019 Per Lindholm
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Machine Learning Physics Fusion Innovation Strategy – Are there co-inventions we can develop to get to fusion faster? Like getting over local minimas like with the car batteries and overhead wires?

February 26, 2019 Per Lindholm
Future generation starting at a higher level. Look back and picture beutify the process
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Machine Learning Test – Gray To Color. Cheat method. Probability more closer to the truth for small steps.

February 24, 2019 Per Lindholm
Not so big. But you can see colors emerging.
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Machine Learning Innovation – Cheat a little – You can learn by y = model(X) and update or you can occasionly cheat by y = np.random.rand() + yt.

February 24, 2019 Per Lindholm
every others simulated learning by a little cheating
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Machine Learning Math – Two ore more equal signs – x + y = x + y = x + y is True then there is a possibility for two or more equal signs. I think its just a cut off which x and y are possible // Per

February 24, 2019 Per Lindholm
to change the equation you probably need to change three sides of the equation

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Machine Learning Math – The difference between A and B if A == B is that the history is different. A might come from sum(vec) = A. Different future? Pi and probability belongs a little since the decimals of pi show random distribution parameters // Per

February 22, 2019 Per Lindholm
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Machine Learning Agriculture – Allow the plants to iterate?. Energy efficiency, (Th-Tc)/Th = n related to plant problems. Guess. If looks like the plant is enjoying themself maybe they are. Leaf shelter if too much sun == drapes?

February 22, 2019 Per Lindholm
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Machine Learning Guess – Over Problemsolvning – Label simulations that are related to the specific problem. Instead of direct transformation learning. Then ?fit the parameters of the simulation so it becomes more relevant to the problem.

February 22, 2019 Per Lindholm
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Machine Learning Math Guess – Invent any imaginary Problem and solve them and you get a more complete Mathematical Framework. For example. Math Forces are ?objects that move problems out of deep layer models()?

February 21, 2019 Per Lindholm
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Machine Learning Physics – Black Hole Network. When in doubt assume a network. Gives a black hole is a network that stops harmful things coming out. Black holes ?are not new physics but new intelligence due a lot of added problems.

February 21, 2019 Per Lindholm
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Machine Learning Math – Thought Math – Draw a bubble around a number or variable in an equation and imagine what this could mean. Then try to program solve a problem like this in machine learning.

February 21, 2019 Per Lindholm
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Machine Learning Paradigm Thought – If You Cant Split The Problem In Part Truths You Can ?Always Split It Into Many Guesses // Per Lindholm 2019-jan-27 Guess Mathematics

February 21, 2019 Per Lindholm
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Idea – Could a cause of a vulcano eruption be that not wanted material got under into the lava? CO2? So a the eruption has some meaning and is not just a physical effect.

February 21, 2019 Per Lindholm
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Machine Learning Idea – Using GAN asking to solve an equation. Showing x,y,z as the iterate towards the answer // Per

February 21, 2019 Per Lindholm
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Machine Learning Guess – As a possibility you can wait for some of equations in the system. Why solve all at the same time. Could produce more complex paths otherwise. Here I waited for y1-y2 -> 0

February 18, 2019 Per Lindholm
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Machine Learning Innovation Idea – Generalisation by same language model0(). y1 = model1(model0(X1)), y2 = model2(model0(X2)),y3 = model3(model0(X3)),… Where X1 = MNIST sample and X2 = Cifar10 sample And X3 = something else in image format. Image showing most linear recognition //Per

February 16, 2019 Per Lindholm
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Towards a Machine Learning Super Battery – Are different laser printed hole patterns different in their ion properties like capacity and transport? Laser aided CD printed like battery// Per

February 15, 2019 Per Lindholm
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Machine Learning Innovation – Air Condition Device – If There is a need of a compressor. I wonder if you can alter the surface function with a cylinder. Or better yet use a peltier element connected to the front end of the piston. So it cools the hot gas. //Per

February 14, 2019 Per Lindholm
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Blender 3D Innovation Idea – What if you take three render models() and iterate until you have enough intersecting pixel color values. Could this set be the inital value basis of a differential equation that solves the overall image with super high resolution? Venn Diagram Usage

February 14, 2019 Per Lindholm
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Towards A Super Battery – Inspired by ?saltpaper. Could sandpaper or paper with crystals be a no brainer thing of a super battery?

February 13, 2019 Per Lindholm
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Machine Learning Physics Idea – Predicting simulated turbulence with a w = model(rnge()).

February 13, 2019 Per Lindholm
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Machine Learning Math Idea – choice_division(a,b). Where you get a rest that moves between the even parts. Dynamic object?

February 13, 2019 Per Lindholm
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Machine Learning Physics – My most important tweet ever. Small fusion so that we have time and money to get the data samples. Million+?

February 10, 2019 Per Lindholm
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Machine Learning Complex Math – Doing some complex root finding with machine learning model() and chainer framework. y**2 = 1+1j gives y = 1.0986842+0.45508987j wow

February 10, 2019 Per Lindholm

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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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  • Unsupervised Learning – Experiment X10 = X10 + 0.01*X3 train a model on the added causality label 10 and 3 then run it without inserted causality to find closest causality got 33.98% unsupervised^2 //Per Lindholm
  • To create *any function sun teleports water to the moon to bend light. //Per . Assume the sun needs to see Earth all the time. Bending of light could be a hack using gravity and water ice … Thats why ice is found on moons. //Per
  • Universe Machine Learning model – Learn 100+ small functions then train on their coeffients with the range index as label. //Per Mars 22 2021
  • Idea – Generalize water response by boiling different fruits, veggies and seeds. Mix the boil with much drinking water to see if plants can enhance their C-vitamin function or otherwise adapt to covid19. By watering them with residue boil.
  • #Covid19 – idea- Create a physical force that reacts to covid19 virus. Atoms are self learned. This can be used. The classfication problem f = a*b and f = a*c. Then move the object down for b and any motion for c. Atoms will investigate the probability. No surprise Theorem. //Per
  • Don’t Drink Boiled Sunflower seeds. Some fabricated ones are not good at all. Turns water green.
  • Fusion Energy Security – If fusion is made of hydrogen H in asymmetrical positions. High temperature in the sun assures this by random. Then there exist low temperature fusion. CPU Computation that moves quantum particles to change the trajectories of hydrogen into sun plasma pos
  • Per Lindholm Law of Physics – The higher the accuracy of a function the more is the cost of quantum particles. Since everything is adapted with running quantum particles. Quantum particles is needed to sustain high accuracy during training. //Per
  • Gravity idea – Gravity are functions or diff.eq. that are been hacked by quantum particles continously. Assume everything in the differential equation can change by single particles. Which came first g or h theory would suggest heat can be turned to gravity by quantum particles
  • High pitch noise in the ear code treatment? – Ran a multithreaded python loop for finding the fastest pair of numbers to add. The idea was to get rid of quantum noise. I think it worked because it stopped. //Per
  • Linux Hardware – We need Linux CPU’s that are machine learning ready. Use ml functions to simulate massive parallel computing. If you know the answer generalizing an approximation is possible. To outperform quantum computers save life. //Per
  • Math – From adapt math its easy to see that x^2 is used to generate a second derivative. //Per
  • Complex Math Idea – Self learning universe atoms need supervised information. Looking at the planet system it could be a complex object. Major C. Why? Since even if you calculated correctly is it right? If MComplex solution belongs to the planet system and life. Its ok //Per
  • Programming code Innovation – Create a dataset of titles of functions and algorithms. Then mix randomly with some rules and create new algorithms from these initial guesses. like https://ruggenberg.nl/titels.html but for code. //Per
  • #Blender 3D Idea – USB #dongle that outputs a video stream with blender 2.8+ for use with low end computers and raspberry pi’s. Hardware Blender USB dongle. Could support the blender community financially. //Per

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