[mtkhaos]

Reading through there is a heavy fault in logic. Statically reasonable limitations to llm output based on training material, ignores hallucinations. And hallucinations ignore the sheer chance of new emergent information by odds.

[K0balt]

Hallucinations are not a different process from normal output. They are merely called hallucinations rather than insights because they are wrong.

There is no magic in LLMs , nor in human thought. It is all a matter of symbol vectorisation and inferring relationships within the multidimensional memetic matrix.

AI absolutely can achieve original insight- but not one that humans could not also make if looking at the same data. This is because the “intelligence” in generative AI is human bounded in the training data, not in the engine that processes it. I strongly suspect the same is true of humans.

After all, we modelled neural networks after our own brains, why would we not expect them to achieve similar results using similar means? Wasn’t that the whole point?

It never ceases to amaze me how people go all pikachu face when I say that our thought process is probably a lot like generative models. Step one, make a facsimile of thing. Step 2: be surprised when thing can also be viewed as being similar to the facsimile. Lol. Hubris is our primary characteristic.

[mtkhaos]

By odds and in the context of RLHF the human could very well thumbs up the output without recognition of said hallucination.

As hallucination is a general term giving to said phenomenon. Otherwise the question Emerges why 2023? And why was that not known colloquially before hand. When the basis of these Algorithms come the the 80s?

[K0balt]

Because models got sophisticated enough that wrong outputs started looking plausible answers rather than random garbage. Sometimes they still spew random garbage though.

[mtkhaos]

But isn't a binary absolute in this case and cannot be used in the basis of first principle's assumption of truth.