A more serious issue is the following; reproducibility. Do you always get a gibbon from the same panda input? Maybe, is it serious for NN in a heavy metal production process with real-time constraints?
The booming field of artificial intelligence (AI) is grappling with a replication crisis, much like the ones that have afflicted psychology, medicine, and other fields over the past decade. Just because algorithms are based on code doesn't mean experiments are easily replicated. Far from it. Unpublished codes and a sensitivity to training conditions have made it difficult for AI researchers to reproduce many key results. That is leading to a new conscientiousness about research methods and publication protocols. Last week, at a meeting of the Association for the Advancement of Artificial Intelligence in New Orleans, Louisiana, reproducibility was on the agenda, with some teams diagnosing the problem—and one laying out tools to mitigate it.
Seems some bridges need to be built between AI and engineering principles.