[VorpalWay]

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.)

[Wowfunhappy]

Wait, a decision tree is machine learning?

[krackers]

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]

[1] https://www.beren.io/2023-04-10-Why-GOFAI-failed/

[VorpalWay]

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.

This is what I learnt at university some decades ago, and it matches what wikipedia says today: https://en.wikipedia.org/wiki/Decision_tree_learning

[Wowfunhappy]

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”).

[VorpalWay]

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.