These are excellent questions. I was using AI in the seemingly typical way people use to today, to refer to large neural network systems. But yes, part of the

These are excellent questions. I was using AI in the seemingly typical way people use to today, to refer to large neural network systems. But yes, part of the issue around these laws and indeed the whole discussion is a lack of nuance about what exactly people even mean when they say AI. If I were to switch to my computer science context, then yes, I view ML as a subset of AI, an even view logic systems and rules systems as a subset of AI too. I doubt that the average person is thinking of PCA or even word embeddings when they say "AI" today, and that's a huge problem. Many of the risks these AI regulation laws are worried about apply equally well to both simpler than LLM systems, and even apply to basic statistics and data aggregating. In my opinion risk mitigation laws should not be specific to AI, but be problem focused instead. Similarly, "AI training bans" for say web content would also be far improved with more nuance. Many laypeople argue that banning AI training doesn't ban web indexing. But based on my professional training I would not personally implement even a basic tf-idf system on any data that has "AI training banned" as tf-idf is a basic AI system according to the strict definition. We'd do far better being more explicit, and banning perhaps "generative AI systems" or "AI systems that are capable of generating substantial subsets of the content." It wouldn't be perfect but it would be a hell of a lot better than the trajectory we're on today.