A single AI network is deterministic. If you apply the same input, you get the same output. If you train on the same dataset, in the same order, with the same initial weights and hyperparameters, you will get an identical training result.
The tricky thing is that AI is high dimensional and non-linear. So what appears to be a very small change to the input can cause a large change in the output. I think the clearest example of this is adversarial AI.
A single AI network is deterministic. If you apply the same input, you get the same output. If you train on the same dataset, in the same order, with the same initial weights and hyperparameters, you will get an identical training result.
The tricky thing is that AI is high dimensional and non-linear. So what appears to be a very small change to the input can cause a large change in the output. I think the clearest example of this is adversarial AI.