You're certainly right that AI/ML is often used interchangeably, though I think the distinction is pretty clear among practitioners: AI is the big circle, ML is a subset of AI, deep learning is a subset of ML. Symbolic AI is also a subset of AI, though I'm not familiar enough with it to say how much it interects the others.

So ML is AI, but just a small part of it.

As for papers, here's one showing GPU speedups for the 3 big GBM packages: https://arxiv.org/pdf/1809.04559.pdf. Their setup shows a 3-7x speedup for XGBoost.

Let‘s not forget Gaussian processes: https://gpytorch.ai/, https://www.kernel-operations.io/keops/index.html Some of the work I‘ve been doing in this field only became feasible in a reasonable amount of time due to the use of GPUs.