[klodolph]

No, I don’t think so. LLMs are good at a lot of simple tasks, but bad at certain simple tasks. Moravec’s paradox in a new iteration.

It applies to humans too. Calculus is “simple” but it takes something like sixteen years to train a human to do it, if all goes well. Meanwhile, most humans think that inverse kinematics is, like, the easiest thing in the world (it’s a super complicated task).

[fluoridation]

Calculus is definitely the harder task, considering it took a species developing the cognitive capacity for symbolic reasoning for it to show up, whereas any animal can figure out how to position its limbs. Yeah, we figured out how to make CAS programs before inverse kinematics software, but that's because computers were made to solve numerical problems, not to replace the cerebella of chordates.

[klodolph]

> Calculus is definitely the harder task,

You’re only evaluating “harder” or “easier” based on the perspective of somebody who has a mammalian brain with millions of years of selective pressure to make it suitable for solving inverse kinematics problems.

The point here is that when we start constructing agents or tools with different architectures to ourselves, it makes sense to reevaluate notions of whether something is ‘hard’ or ‘easy’. LLMs are bad at counting not because counting is hard, but because their architecture makes it hard.

[fluoridation]

I'm evaluating them using an objective metric, which is how long each took to arise in the universe. It could have never been the case that calculus arose before inverse kinematics, because a thing like that could not interact with the real world.

Also, I suspect you're comparing dissimilar things, because in one case you're looking at a brain doing both inverse kinematics and "calculus" (sense 1), and in the other you're looking at a computer doing both inverse kinematics and "calculus" (sense 2). The kind of calculus a CAS does is not the same kind that a human does. It's less versatile, for one.

>The point here is that when we start constructing agents or tools with different architectures to ourselves, it makes sense to reevaluate notions of whether something is ‘hard’ or ‘easy’.

Well, no, because when someone says that calculus is hard and moving their arms is easy, they're not talking about how hard it was to create each functionality, they're talking about how hard it is to employ each. We would need to ask a computer how hard it thinks the tasks it does are to do.

[klodolph]

> I'm evaluating them using an objective metric,

I don’t think the metric is at all reasonable, and the fact that it’s “objective” doesn’t make up for its other shortcomings. I don’t think we have a basis for agreement here—I think you’ve framed the argument in a way that supports a “calculus is hard” conclusion merely by defining “hard” in such a way that supports your conclusion from the start, but I think that approach is only useful as a way to win an argument, and we’ve failed to share ideas once you start using that tactic.

[fluoridation]

>I think you’ve framed the argument in a way that supports a “calculus is hard” conclusion merely by defining “hard” in such a way that supports your conclusion from the start

It seems to me you're the one who first did that by equivocating what is easier to do and what is easier to make a machine do.

>we’ve failed to share ideas once you start using that tactic

Well, I certainly don't agree with that.