It could be a good idea I mean.

Some magic for math class maybe.

I assume gravity. Since your dealing with forces. Is a differential equation. Loss functions in machine learning can look like some decreasing force. So I assume a force is like a iteration potential.

Then using the force mass acceleration equation and introducing iterations. The iterations mean that you need a force to flip a zero to one and one to a zero. That is the future decision data.

I assume that if you bring to masses together they need to calculate everything that could happen when two masses are brought together. Its like an axiom.

From this I guess that gravity as a machine learning model() of force changes. gravity = 1/m * model(F(iteration)).

So my conclusion is that gravity is iteration calculations that much like an A.I is seeking iteration energy which the universe assigns to each atom or particle in space and its decreasing surroundings.

Here the energy is taken from the rotation states. The different positions of the tilt rocker.

The power guess is that all our physics must be viewed in the light of supervised learning. So there exist explanation and progress value in looking at all the old physics.

So this should then apply to electromagnetic induction. The reason would be to develop super generators and engines.

My guess is that from looking at force diagram between two magnets. It looks like a loss function. It goes to zero in a typical machine learning loss fashion.

So invert that word and you get that there could exist an Iteration potential.

That is. Something iterates when the forces are high. It could be electron iteration maybe.

Some problems from looking at Faradays law of induction.

Since you integrate. I wonder if you then loose information. I imagine the magnetic field changes are like heat mirage lines and not perfect. Then could all summations be like machine learning decision results. Some technical loss of some kind.

The supervised learning then should come in the differential equation. That is. You have some prediction and a current value. I got this from the definition of the derivative.

Since electricity then is about iterations. I imagine it could buzz some kind of sounds. In some other dimensions maybe? I choose sound over light since sounds require less energy to produce. So how could the universe use sounds? Inspired life. Localization and communication could be a reason. To inform what is happing.

So this can also be supervised learning. If that particular sound goes faster than the electricity it could provide supervised learning data.

Multi sensor tracker for smart protection of your business or home?

Suggestion. Think of old thought experiments i terms of what the network could learn. By supervised learning for instance.

Since plasma is hot. Then could fusion be about vibration. Applying this to machine learning. Assuming the particles have something to do with the parameters in the matrices.

I wonder. Could you have vibrating parameter or weight matrices?

So the idea is what problems arise with a dynamic weight matrix.

Then for fusion. What are the targets. Are there signatures of the new material that needs to be met?

There has got to be a reason for vibrations. Maybe the noise changes all the signatures a little. To get the ?gradient optimization going again.

Then I wonder. Could you get ?like a surrounding network of ?slightly but different material due to the heat. Like layers in batteries work together and so on. But these ?are more dynamic.

Capillary action (sometimes capillarity, capillary motion, capillary effect, or wicking) is the ability of a liquid to flow in narrow spaces without the assistance of, or even in opposition to, external forces like gravity. // Wikipedia