As part of a user study, I recently read through a number of old papers from NIPS, which is a major machine learning conference. Some of these papers were almost twenty years old. Although it isn't really surprising in retrospect, I was struck by how shoddy and ad-hoc many of these papers were by today's standards. What's more, many were written by researchers who are now very well known in the field.
Any hybrid or empirically driven field undergoes a "mathematization" or formalization process as it matures (some fields take longer than others). And while we're still very, possibly impossibly, far off from general artificial intelligence, we've at least learned to pose more precise questions. Progress, it's a good feeling.