Comment by hammyhavoc
3 years ago
And? We are talking about AI, not humans.
People on HN claim they're using it for XYZ in development, yet it can't even generate the necessary NGINX config, despite being given the URL rewrites it'll need to incorporate.
The point is that it hallucinates. It isn't that it failed, it's that despite giving it everything it needs to know, it hallucinated all kinds of things not in the documentation, not in my prompts et al.
Why? Because it's an LLM. It isn't fit for purpose in this context. A next word prediction AI (an LLM) isn't appropriate for these kinds of problems.
Is your argument that AI needs to be held to a different standard than humans? Because humans confidently hallucinate answers all the time.
To me, it feels quite intuitive that an AI trained on human knowledge would automatically learn to do the same.
Neither.
The point: it isn't that it failed. It isn't even that it "hallucinates answers", it's that it infers relationships between words that don't exist because it's an LLM. It predicts the next word. That's what it does.
Something that predicts the next word isn't an appropriate method of doing x in y of z cases, because its reliability in providing the designated function is important. Ergo, yes, LLMs may well have applications, but most of the problems that people are throwing it at are inappropriate, just like blockchain fetishism versus a database. For the overwhelming majority of problems, AI is not the answer, neither is a blockchain, neither is an NFT.
Call hallucination what it is: a fail state. It got it wrong. It didn't "hallucinate". With standard conditional logic, x yields y result. That's very useful where you want consistency and reliability, ergo, those problems are best not handled via an LLM. Why not use the appropriate tool for the job?
Deductive versus inductive versus abductive reasoning.