Just technically modern deep learning systems aren't generally a simulations. They are generally approximations. They take a large amount of data and predict im

Just technically modern deep learning systems aren't generally a simulations. They are generally approximations. They take a large amount of data and predict immediate results. A simulation can determine what the expected final state of an economic or ecological system is (but requires sufficiently accurate assumptions about the structure of the system). A neural network normally can't do this (you can approximate what the model of a system is from some data and then approximate the predicted final state if you have enough models). The way engineered solution go would be: A) Simulated systems using equation with exact solution or perhaps numeric approximations of the exact solution equations. This combined with principles like conservation of energy allow to talk of the long term behavior of a system. Lots of large bridges were built with pencil-and-paper math before computers. B) Simulated systems. Things like finite element analysis of car crashes. This allows prediction but not very long term prediction of system behavior. This is the easiest and most reliable way to build a bridge. C) Approximating systems using only data and deep learning approximations. This lets you do things that A & B can't do but these generally don't do reasonable prediction in any significant timeframe. One could imagine that Dall-E can't design a bridge you'd be confident of walking across.