This is what leads to people even in the industry to take at face value a human-like response in helping answer a query; and in this case, willingly downloading malware.
OpenAI was founded with that name in 2015; transformer models were introduced with 2017's "Attention Is All You Need"; Even ChatGPT's immediate in-house predecessors in the form of InstructGPT and GPT-3 were basically ignored by the general public.
No, what made these companies valuable is that ChatGPT specifically crossed the threshold into being vaguely interesting, and then everyone else copied it.
Likewise the finite state machines controlling NPCs in video games — and the search tree in Deep Blue and the learning from self-play system in AlphaZero — are called "AI": they're good enough to be interesting, regardless of your position on the question "what is this 'thinking' thing anyway?" which led to Turing coming up with the eponymous test.
Maybe it was this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619792/
The authors’ statement that ‘unlike hallucinations, confabulations are not perceived experiences but instead mistaken reconstructions of information which are influenced by existing knowledge, experiences, expectations, and context’ is pretty compelling.
That despite every example of an LLM failing in production giving results no competent human would actually produce unless they were committing fraud or had severe mental deficiency, that and no one else would find acceptable.
You sure it's "precisely" and not "analogously"?
Ironically, one common human failing is to use binary classification.
> or had severe mental deficiency
It seems I have worked with more human idiots than you. And at least one time where I was the idiot, despite literally scoring "off the charts" on a cognitive abilities test at school.
I'd agree that LLMs are not as smart as humans — they had to read 10% of the internet just to be as competent as an intern — but to me they're more like newspapers and the Gell-Mann Amnesia effect than "severely deficient", even when indeed unacceptable.
This is completely unnecessary.
If I imagine a very naive translation system that just does a dictionary substitution, and the dictionary has some bad entries, the correct and incorrect results look the same, but it's still a bug.
The system as a whole still messes up because "dictionary substitution" is not at all sufficient:
Hydraulic ram -> aqua ovis -> water sheep
… this probably counts as just "hallucination" while arguably not being a "bug", because the dictionary is correct (or close enough, I don't speak Latin), but the system as a whole can't ever be better because the model it relies on can't represent the right things.
But in an LLM, there are no explicit rules (or only in some obscure background layers), it's all statistics. Statistics stacked on top of more statistics in a fascinating self-stabilizing way, where each guess provides some support to its peers, like how the weak paper slabs support each other in a house of cards. But it's statistical guesswork all the way down. Even the most correct answers are merely statistics playing out favorably in a case that may or may not have been very easy to get right.
Actually people did this before software is even a thing.
and other emoticons?