Sentience as in having some form of self-awareness, identity, personal goals, rankings of future outcomes and current states, a sense that things have "meaning" isn't part of the definition. Some argue that this lack of experience about what something feels like (I think this might be termed "qualia" but I'm not sure) is why artificial intelligence shouldn't be considered intelligence at all.
But what it does require: the ability to produce useful output beyond the sum total of past experience and present (sensory) input. An LLM does only this. Where as a human-like intelligence has some form on internal randomness, plus an internal world model against which such randomized output could get validated.
Isn't that what mathematical extrapolation or statistical inference does? To me, that's not even close to intelligence.
Obviously not, since those are just producing output based 100% on the "sum total of past experience and present (sensory) input" (i.e. the data set).
The parent's constraint is not just about the output merely reiterating parts of the dataset verbatim. It's also about not having the output be just a function of the dataset (which covers mathematical and statistical inference).
Citation needed would apply here. What if I say it doe require some or all of those things?
>But what it does require: the ability to produce useful output beyond the sum total of past experience and present (sensory) input. An LLM does only this. Where as a human-like intelligence has some form on internal randomness, plus an internal world model against which such randomized output could get validated.
What's the difference between human internal randomness and an random number generator hooked to the LLM? Could even use anything real world like a lava lamp for true randomness.
And what's the difference between "an internal world model" and a number of connections between concepts and tokens and their weights? How different is a human's world model?