These categorizations tend to be arbitrary and inconsistent because what is an intelligence is probably subjective. People consider kNN and naive bayes to be machine learning. One is "just" sorting and the other is just counting. The learning in naive bayes and even in some higher order bayesian networks is of a similar flavor as the count structures generated for IR.

Because we understand how these algorithms work, we can always reduce them to just this or that. Prediction in many linear classifiers is just dot products. relu neural networks are just lots of clamped dot products. Random projections on simple count data can generate word embeddings.

Whether or not something is merely model-fitting, super-scaling, compression or AI, AI art and machine learning will depend on the field it originated from. It's indisputable that the algorithms are so reducible but I tend to think that we should care more about functional capabilities when compared to an appropriate subset of a known intelligence's abilities than details of implementation.