Nothing about the conception of knowledge as "justified true belief" implies that machines can't accrue knowledge.
For that to be the case "belief" would have to be defined in a circular way to exclude machines (which we can reject), or "justified" would have to be restricted to mean "based of observation of the external world".
The more general "based on observation of their input" applies to both men and LLMs.
That man can't know the bare direct facts of the world, just his own input (it's sensory experiences, and whatever second hand input he's given by other people), and for neither can he be sure they're absolutely true, or that he got right.
>The idea that knowledge can't be defined in a non-circular way is only plausible for someone with no familiarity with epistemology.
The idea that this or that epistemology restricts what can be the case in reality is only plausible for someone with no familiarity with epistemology.
Interestingly, according to the parent's logic, there is a specific argument for that (took me a while to get the argument, and I think its not sound).
The reasoning is that, according to reliabilist responses to Gettier's problem, knowledge requires a reliable belief-forming process. But if we assume that LLMs operate probabilistically, they can't fulfill this, because they're "guessing".
The refutation to this argument is the usual one: levels of abstraction. LLM reasoning, like human intellect, can exhibit structured/logical behavior at a higher level of abstraction, even if the underlying processes are stochastic.
Therefore, if one bases the reasoning on the fact that both are stochastic at the lower level, then both can't produce knowledge, but if one bases it on the higher level, they both can.
(Having said that, I don't imply that AIs can reason like humans, but just that the criticism is not sound)
They can't accrue knowledge because they stop learning once their training is finished.
The difference is that LLMs we can talk to them when they're in that dead state.
And of course for an LLM this is an implementation detail. One could just as well keep updating the weights of the same model over time. And its being done, it's called "continual learning". LoRA is also a way of doing that.
In training they get feedback. Out of training, not so much.
Hard problem
So? We also have the exact same problem, and no solution that covers all facts, not even the most basic ones.
So them having this problem would be either neutral or supportive of the idea they're intelligent, not refuting it.
> Nothing about the conception of knowledge as "justified true belief" implies that machines can't accrue knowledge.
Even if the LLMs could adjust their weights in response to knowledge that they encountered post-training, they would need to make judgements about what is "true" so that they can only add "justified true beliefs" to their weights.
I agree that this is also a problem humans have, and we do really badly at it because we tend to weight social conformance more than factual accuracy.
And yes, I agree that this problem bears no relation to the question of whether they're intelligent, or even sentient. I was answering a different question.
Ha. Gettier actually argued that justified true belief is insufficient for knowledge, not that machines can't possess it. And JTB doesn't inherently exclude machines.
Interestingly, research suggests LLMs encode truthfulness in their internal representations: https://aclanthology.org/2023.findings-emnlp.68/