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I think I agree with your analogy, but would say 99% rather than 99.99999%.

Beyond that, I'm not entirely sure what a "perfect" LLM would even be defined as.

That makes sense, and just harder and harder to get more accurate. Same as humans I supposed :)

Past 99%, what does "more accurate" mean? I think it will vary from person to person and use case to use case, which is why I personally don't foresee a world where an LLM or any form of AI/ML is ever perfectly accurate.

I'm struggling to think of any medium that has ever reached 100% accuracy, so to target that for an ML algorithm seems foolhardy

I agree with this. Because it does seem that if it's based on NOT 100% accurate information in terms of training, it can never return 100% accurate results. Which I guess, as humans, we don't either, but as a committee, one MAY argue we could. I'm torn lol.

> I'm struggling to think of any medium that has ever reached 100% accuracy, so to target that for an ML algorithm seems foolhardy

How close to 100% is close enough?

Arithmetic and logic are close enough that cosmic rays are the limiting factor, and "computer" used to be a profession.

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