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At this point AI basically means "we didn't know how to solve the problem so we just threw a black box at it".

I disagree. More often than not is "We know how to solve the problem, and the solution is some linear algebra"

I disagree with both of you.

It's not about linear algebra (which is just used as a way to represent arbitrary functions), it's about data. When your problem is better specified from data than from first principles, it's time to use an ML model.

I think what you're expressing is also known as "the Bitter Lesson".

Wrong! We do how to solve the problem, but the solution does not run on an electronic board at 100 nsec. So it has to be approximated with a function that runs within that time constraint. Also, if deciding to accept/reject events based on a learned metric of typicality is not AI, then why guessing the next token is AI? We have robots going around the LHC tunnel fixing things in high radiation environment. Is that enough AI? If not, we accept volunteers to replace the robots… Relax people and try to imagine that life might be more complicated than what an oversimplified general public article says

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