> GPT4 can't know the real reason why it wrote anything. It can never tell you the real reason why it made a mistake.
Define "know" such that humans have it and GPT-4 does not have it.
Define "know" such that humans have it and GPT-4 does not have it.
This doesn't mean our justifications are accurate, but at least we do sometimes have "debug logs," some memories (perhaps faint and inaccurate) of internal thoughts that we can use to try to reconstruct what we were thinking.
An LLM cannot remember anything it didn't write down. It can't do that at all. They would need to change the API.
But it might be worth thinking about what a satisfying explanation of the LLM’s behavior would look like. It would have to be a high-level summary of the calculations that were done to predict each new token. Knowing which previous tokens got attention and what higher-level concepts it calculated would help. (Also, if the temperature isn’t zero, there’s a random number generator involved.)
The LLM doesn’t have access to that, once the calculation is done. It has the text, same as us. It can guess what might have happened, same as us. When it guesses it’s going to anthropomorphize by picking an explanation that would make sense for a human, because it’s trained to write justifications that a human would write.
Ok, but I'm not sure what that has to do with knowledge. You can ask ChatGPT to reflect on and correct its own answers, so it clearly has access to its own outputs, which is a form of memory.
It is merely statistical probability, which hardly can be classified as an epistemological basis. If we pretend for a moment it is, the ontologies formed are most certainly peculiar, and we can expect ideologies that emerge out of the ontologies are problematic.
Predictive processing is well established in the neuroscience community.
Capital T Truth is really of very little interest to anyone outside of faith-based epistemology like religion.
Numbers carry no meaning, nor do the magnitudes arbitrarily assigned to meaning. The map is not the territory.
Numbers can mean anything. A multitude of numbers as voltage potentials and ion gradients sufficiently describe your brain.
Biology manifests this arrangement as a brain, without which this arrangement would also be similarly meaningless.
Your argument against deriving meaning from statistics completely ignores that the brain also works this way.
> Your argument against deriving meaning from statistics completely ignores that the brain also works this way.
The brain is not predicting it's compressing.
Compression requires prediction, therefore your brain requires prediction.
If you meant this as a comparison to machine learning, then a predictive coding model closely matches.
No, not fabricated, but inferred from a structured corpus of information generated by other semantic processes (humans).
> Numbers carry no meaning, nor do the magnitudes arbitrarily assigned to meaning.
Prove it.
> The map is not the territory.
Except if the territory is information, in which case the map is literally the territory. Knowledge is information, is it not?
Your belief in epistemic truth is a statistical inference from your perception of apparently reliable causality. How do you ground this inductive inference?
Your attempt to appeal to epistemology 101 with a casual dismissal as "mere statistical probability" covers this deep, gaping maw. Bayesian inference reduces to classical logic when all probabilities are pinned to 0 and 1, but in what circumstances can we actually demonstrably infer absolute certainty? None that I can think of, except one's own existence.
So I would agree with you, entirely!
I was merely pointing out that statistical correlation alone affords no epistemological basis.