If you can use imaginative networks like GANs to generate photo realistic images. Then why not use machine learning math to improve math.

What I mean is that machine learning can give you a new perspective on things.

For instance. What could a higher constant be. Since I donâ€™t know. I assume something noisy, something random. Then I could say that that the Normal distribution is a constant, a random constant.

Another example would be from fake but realistic image generation. Like painting a cat. There could exist a random constant in the form of a best type of noise for the generator model. The noise should give the generation model a typical looking cat. That is. There is a better chance to get to all different cats from this type of noise. The noise is action ready and constant shaped in some way.