While one can quibble with its argument, Gettier's conception of knowledge as justified true belief (prob familiar to many philosophy undergraduates) implies that machines can't in fact accrue knowledge. The idea that knowledge can't be defined in a non-circular way is only plausible for someone with no familiarity with epistemology.

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coldtea 3 hours ago | parent | next [-]

>Gettier's conception of knowledge as justified true belief (prob familiar to many philosophy undergraduates) implies that machines can't in fact accrue knowledge.

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.

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marcus_holmes 3 hours ago | parent [-]

> Nothing about the conception of knowledge as "justified true belief" implies that machines can't accrue knowledge.

They can't accrue knowledge because they stop learning once their training is finished.

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gchamonlive 2 hours ago | parent | next [-]

By this definition if they could instead buffer recent facts and then offload to their weights reliably and efficiently they would be able to accrue knowledge despite their substrate.

	▲	marcus_holmes an hour ago \| parent [-]
		Yes, and they'd have to deal with the same problem that we do: how do we know what is true? what gets counted as a "recent fact" and what gets dismissed as mere opinion or disinformation? In training they get feedback. Out of training, not so much. Hard problem

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coldtea 2 hours ago | parent | prev | next [-]

Humans stop learning once their training is finished too, it's called death.

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.

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Sharlin 2 hours ago | parent | prev [-]

You mean they don't accrue knowledge during the training?

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stygiansonic 2 hours ago | parent | prev | next [-]

Gettier actually criticized justified true belief (JTB) as insufficient for knowledge and provided counter examples known as Gettier problems

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pizza234 3 hours ago | parent | prev [-]

> Gettier's conception of knowledge as justified true belief (prob familiar to many philosophy undergraduates) implies that machines can't in fact accrue knowledge.

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/

	▲	stygiansonic 2 hours ago \| parent [-]
		Indeed. In fact this criticism of JTB is known as the Gettier problem: https://iep.utm.edu/gettier/