I know this is a very general question, but I'm trying to illustrate this topic to people who are not from the field, and also my understanding is very limited since I'm just a second-year physics student with a basic understanding of R and Python. My point is, I'm not trying to say anything wrong here.
So according to Wikipedia, after the second AI winter, which happened because expert systems didn't match expectations of the general public and of scientists, AI made a recovery "due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards".
What I'm trying to understand now is whether the rise of AI is rather connected to greater computational power available to the public or whether there have been fundamental mathematical advances that I'm not aware of. If the latter is the case (because according to my understanding, the mathematical models behind neural networks are rooted in the 70s and 80s), I would appreciate examples.
Again, please don't be offended by the general character of this question, I know it is probably really hard to answer correctly, however, I'm just trying to give a short historic introduction to the field to a lay audience and wanted to be clear in that regard.