It's not that it's only as good as it's training data. But depending on the underlying implementation it's also only as good as the quality of the random number

It's not that it's only as good as it's training data. But depending on the underlying implementation it's also only as good as the quality of the random number it uses in its implementation. Let me give a somewhat broad example. In cases where a state space to be explored is absolutely massive and we have no currently good way to 1. prune it 2. traverse it to land at the states we want(that have certain properties), and so on. Then the best we have right now is "sample the state space." But "sampling" requires very good random number generation so that we're 1. not biasing ourselves (this is clear to everyone) but deeper than that 2. Good random number generation reduces the problem of us only grabbing states from the state space that confirm our existing ideas rather than the ones that might challenge our existing ideas. How many scientists are developing their ideas on single workstations that are running out of entropy?