> It applies its knowledge to create bespoke solutions to the problem you pose to it, and is able to self evaluate its progress towards the completion criteria.
It imitates applying knowledge. The imitation may be uncanny, but assigning LLMs intentionality and ToM is a category error.
Does "applying knowledge" necessitate human-like intentionality and theory of mind? If you insist it does, and this is a category error, then we need a new category.
By analogy, consider that many have referred to classical, deterministic computing as some kind of "thinking" for the last half century+. Does this stop being kosher when the computer has an uncanny propensity for human language? Perhaps, but the computer is still clearly chewing through problems that would have required a lot of human thinking (e.g., arithmetic) in ages past.
I haven't seen any genuine proposals for words to replace the human mind analogues, let alone proposals that the anglosphere would plausibly adopt en masse.
Presumably they meant that they'd sacrifice some material value for some animals, not that every animal on Earth has infinitely more value than inanimate goods.
> infinities cannot be compared
That's either a mathematically illiterate assumption or a very strange philosophical hill to die on.
> some tragedies cannot be averted
Sure. The question is what to do about the ones that can be averted.
> some decisions are impossible to make
> and "prioritization" is a distraction that forces choices when choices are not strictly necessary.
Again, the question is what choices to make when you can (arguably must) make them. Saying they're impossible is just refusing to take responsibility. You either do something, or you don't.
Except LLMs are simulacra of actual intelligence. Frequently in a single conversation working on a single narrowly scoped task, I am both surprised by a few insights and cursing at how it can miss obvious issues. The "raw intelligence" of LLMs leaves much to be desired.
Active params for this model is 13B which takes about 6.5GB at full native quantization, or perhaps 3.25GB at the 2bit quant that's being provided here, that should take significantly less than 10s to fetch on Mac storage, especially given that some fraction of the model weights would be cached in RAM. Sounds like something worth testing out if it can be made to work out of the box with DS4.
If you charitable (like you should be), then a reasonable assumption is that they probably know what happens on a dairy farm, and that's actually their point.
> We fit continuous theories to discrete measurements--and the good ones fit really well!--but until we can measure it how can we actually know?
Well, physicists came up with quantum mechanics because they found a way to distinguish a genuinely discrete phenomenon.
Understanding the physical universe overlaps with a subset of math. It shouldn't constrain the abstract tools which may or may not one day be useful for that understanding.
I agree that continuity (and therefore infinity) are really useful tools. But it may also be useful to develop mathematical formalism that hews more closely to that which we can actually observe. Or not! But if nobody investigates we'll never know.
It imitates applying knowledge. The imitation may be uncanny, but assigning LLMs intentionality and ToM is a category error.
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