Technology

That's fair. I think fundamentally a false positive/negative isn't that much different. Pretty much all tests—especially those dealing with real world conditions—are heuristic, as are all LLMs by necessity of the design. Hallucination is a pretty specific term given to AI as an attempt to assign agency to a system that doesn't actually have any (by implying it's crazy and making stuff up instead of a black box with deterministic inputs and outputs spitting out something factually wrong but with a similar format to what is trained on). I feel like the nature of any tool where "you can't trust this to be entirely accurate" should have an umbrella term that encompasses both types of providing inaccurate info under certain conditions.

I suppose the difference is that AI is a lot more likely to randomly go off, whereas a blood test is likelier to provide repeated false positives for the same person with their unique biology? There's also the fact that most medical tests represent a true/false dichotomy or lookup table, whereas an LLM is given the entire bounds of language.

Would an AI clustering algorithm (say, K-means for instance) giving an inaccurate diagnosis be a false positive/negative or a hallucination? These models can be programmed on a sliding scale and I feel like there's definitely an area where the line could get pretty blurry.

Then it's strange that you are separating AI and LLM, because in CS LLM is a type of artificial intelligence.

Text to video, automated driving, object detection, language translations. I might be misusing the term, you could argue that the word is describing what LLMs commonly does and that is where the term is derived from. You can also argue that AI is sometimes correct and the human have issues identifying the correct answer. But In my mind it is much the same just different applications. A car completely missing a firetruck approaching or a LLM just spewing out wrong statements is the same to me.