I think AI is a useful term which usually means a neural network architecture but without specifying the exact architecture.
I think Machine Learning doesn't mean this as a word, as it can also refer to linear regression, non-linear optimisation, decision trees, bayesian networks etc.
That's not saying that AI isn't abused as a term - but I do think a more general term to describe the latest 5 years advancements in neural networks to solve problems is useful. Particularly as it's not obvious which model architectures would apply to which fields without more work (or even if novel architectures will be required for frontier science applications).
This is incorrect. Machine Learning is a term that refers to numerical as opposed to symbolic AI. ML is a subset of AI as is Symbolic / Logic / Rule based AI (think expert systems). These are all well established terms in the field. Neural Networks include deep learning and LLMs. Most AI has gone the way of ML lately because of the massive numerical processing capabilities available to those techniques.
Eh, not really. All Deep Learning involves neural networks, but not all neural networks are part of deep learning. To be fair, any modern network is also effectively built by deep learning, but your statement as such is inaccurate.
I think Machine Learning doesn't mean this as a word, as it can also refer to linear regression, non-linear optimisation, decision trees, bayesian networks etc.
That's not saying that AI isn't abused as a term - but I do think a more general term to describe the latest 5 years advancements in neural networks to solve problems is useful. Particularly as it's not obvious which model architectures would apply to which fields without more work (or even if novel architectures will be required for frontier science applications).