Neural networks use smooth manifolds as their underlying inductive bias so in theory it should be possible to incorporate smooth kinematic and Hamiltonian constraints but I am certain no one at OpenAI actually understands enough of the theory to figure out how to do that.
How does your conclusion follow from your statement?
Neural networks are largely black box piles of linear algebra which are massaged to minimize a loss function.
How would you incorporate smooth kinematic motion in such an environment?
The fact that you discount the knowledge of literally every single employee at OpenAI is a big signal that you have no idea what you’re talking about.
I don’t even really like OpenAI and I can see that.
There are physicists at OpenAI. You can verify with a quick search. So someone there clearly knows these things.
> I am certain no one at OpenAI actually understands enough of the theory to figure out how to do that
We would love to learn more about the origin of your certainty.