[rachofsunshine]

I use "ML" when talking about more traditional/domain specific approaches, since for whatever reason LLMs haven't hijacked that term in the same way. Seems to work well enough to avoid ambiguity.

But I'm not paid by the click, so different incentives.

[Nevermark]

I like that.

AI for attempts at general intelligence. (Not just LLMs, which already have a name … “LLM”.)

ML for any iterative inductive design of heuristical or approximate relationships, from data.

AI would fall under ML, as the most ambitious/general problems. And likely best be treated as time (year) relative, i.e. a moving target, as the quality of general models to continue improve in breadth and depth.

[smj-edison]

Generative AI vs artificial neural network is my go-to (though ML is definitely shorter than ANN, lol).

[IanCal]

Huge amounts of ml have nothing to do with ANNs and transformers are ANNs.

[smj-edison]

I stand corrected! What are your go-tos?

[rachofsunshine]

Not the person you're replying to, but there are tons of models that aren't neural networks. Triplebyte used to use random forests [1] to make a decision to pass or fail a candidate given a set of interview scores. There are a bunch of others, though, like naive Bayes [2] or k-nearest-neighbors [3]. These approaches tend to need a lot less of a training set and a lot less compute than neural networks, at the cost of being substantially less complex in their reasoning (but you don't always need complexity).

[1] https://en.wikipedia.org/wiki/Random_forest

[2] https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Trainin...

[3] https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm