I really wish I could wave a magic wand and make everyone stop using the term "AI". It means everything and nothing. Say "machine learning" if that's what you mean.
To be pedantic, “machine learning” is even underspecified. It’s marginally better in that it sheds _most_ of the marketing baggage, but it still refers to a general concept that can mean may different things across different fields of study.
Machine learning: that definitely includes SVM and regression models. Oh and decision trees. Probably a few other things I'm not thinking of right now. Many people will unfortunately be thinking of just neural networks though.
(By the way, if something like a regression model or decision tree can solve your problem, you should prefer those. Much cheaper to train and to run inference with those than with neural networks. Much cheaper than deep neural networks especially.)
Expert systems are basically decision trees which are "gofai" (good old fashioned ai) as opposed to deep learning. I've never really seen a good definition for what counts as "gofai" (is all statistical learning/regression gofai? What about regression done via gradient descent?). There's some talk in [1]
Yes: you fit a decision tree to your dataset in an automated fashion, that fits the definition of machine learning. Just as you would use backpropagation to fit a neural network to your data.
Oh, if the tree is made by the computer based on training data, that feels to me like what most people would agree is “artificial intelligence” in 2026 (which is why I think people should actually say “machine learning”).
That is how decision trees are usually made in my experience. Though I guess you could make one by hand. You could also make a (small) neural network by hand.
In which case you could argue that neither DTs nor NNs are ML. Only the training itself is ML/AI. An interesting perspective, but this will probably just confuse the discussion even further.