Machine Learning Physics Guess – Is Our Water Organic And Optimized For Life?

I wonder if water. Being essential to life. Also is optimized for life. Even if life has evolved in relation to water. The other way sounds probable also.

I read that there can exist para- and ortho-water. From a philosophical point of view. It could make sense to separate properties of water if there exist a smart solution found with complex combinations. Like a pattern of ones and zeros.

Then these combinations would have to be computed/learned through ?millions of years. If so then splitting water is always a bad idea. You ?risk loosing properties that is needed for life.

Machine Learning Physics Guess – Engineering From Nothing

Constructive forces can make something. But like in a ?GAN machine learning network you need a discriminator force to shape it into something good. A destructive force. So I wonder. Could this be made into an engineering technique of some kind. For instance. Could it work for a fatigue engineering problems?

I mean. I think GAN networks produce the most advanced type of images. So it ?could work for producing fatigue predictions.

Machine Learning Idea – Do Cells Solve Problems By Splitting?

I think one problem with deep learning is that if you add an ?odd looking training sample to the model(). Then it could ?perhaps deflect a little. Give off a little on the prediction accuracy.

So the idea is to check what the response new samples have on the already learned samples. Maybe its possible to have a system that senses deflection in the weights. One idea would be split the problem into two units and so on? Something similar to cells?

My Machine Learning Idea – You Can Use A Parity Method To Check Classification Errors. Possibly Correcting Single Errors Given Three Or So Parity Bits. Testing On MNIST

Basically you encode the labels with the error correcting fomula. Like the Hamming Code. Just google ecc error correcting code.

Then train. On the test set you can then verify a little that the classification is correct by computing the three parity bits from the data and compare. It should also be possible to correct a classification.

Here I got a data bit error [1]. 4 bits index [0,1,2,4] were data bits. The other 3 were parity bits.

The correct was y_test[1].