IMO AI means neural net. I get that people use it for other things, but that's what I use it to mean - there is just no other term that's easy to say. And at this point the idea of breaking problems down into "neurons" and activation patterns is inherent to most AI models. Here though the keywords are "ensemble machine learning" and "Bayesian" - they could have used a neural net for the machine learning but most likely it is just XGBoost or similar. https://ia.acs.org.au/article/2021/the-ml-technology-looking... mentions they are also doing full-physics joint inversions and computer vision, perhaps the vision is a neural net.
I dug into this a tiny but and learned that the ACM started having conferences in the early 1950s - https://dl.acm.org/doi/pdf/10.1145/30408.30418 - while the famous Dartmouth workshop that coined the term AI was later, in 1956. So I agree, it looks like computer science was already establishing itself as a discipline before the term AI started to be used.
There's been some overlap naturally, e.g. Yershov or Hopper attending both types events early on. But hardly any of content in say TAoCP was considered AI at any point.
my friend, XGBoost is not a neural net-based method, it represents a best-of-breed for an alternative family of methods.
"While the XGBoost model often achieves higher accuracy than a single decision tree, it sacrifices the intrinsic interpretability of decision trees. For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder."
Unless the parent edited their comment, you misread it, because they are specifically saying XGBoost is not a neural network:
> Here though the keywords are "ensemble machine learning" and "Bayesian" - they could have used a neural net for the machine learning but most likely it is just XGBoost or similar.
I.e. they could have used a neural network but they probably used something else, that something else being XGBoost.
