It looks like it takes a snapshot of the view of the world at a given timestamp.
Let's say we have the model M1 at timestamp t. We have a model M2 at timestamp t + 1. Can we train another model on the incremental changes in models? So model M1 can tell us what is the answer at time t and model M2 can tell us the answer at time t + 1. A derived model learning on incremental differences is very interesting.
The differences really are the general understanding of the future, or rather predictive human nature? Is it possible that this derived model is more closer to human understanding?
Let's say we have the model M1 at timestamp t. We have a model M2 at timestamp t + 1. Can we train another model on the incremental changes in models? So model M1 can tell us what is the answer at time t and model M2 can tell us the answer at time t + 1. A derived model learning on incremental differences is very interesting.
The differences really are the general understanding of the future, or rather predictive human nature? Is it possible that this derived model is more closer to human understanding?