Hacker News Reader

Comment by Animats | Hacker News Reader

Full paper: [1].

Not much new here. The basic criticism is that LLMs are not embodied; they have no interaction with the real world. The same criticism can be applied to most office work.

Useful insight: "We (humans) are always doing more than one thing." This is in the sense of language output having goals for the speaker, not just delivering information. This is related to the problem of LLMs losing the thread of a conversation. Probably the only reasonably new concept in this paper.

Standard rant: "Humans are not brains that exist in a vat..."

"LLMs ... have nothing at stake." Arguable, in that some LLMs are trained using punishment. Which seems to have strong side effects. The undesirable behavior is suppressed, but so is much other behavior. That's rather human-like.

"LLMs Don’t Algospeak". The author means using word choices to get past dumb censorship algorithms. That's probably do-able, if anybody cares.

[1] https://arxiv.org/pdf/2407.08790

View on HN

The optimization process adjusts the weights of a computational graph until the numeric outputs align with some baseline statistics of a large data set. There is no "punishment" or "reward", gradient descent isn't even necessary as there are methods for modifying the weights in other ways and the optimization still converges to a desired distribution which people claim is "intelligent".

The converse is that people are "just" statistical distributions of the signals produced by them but I don't know if there are people who claim they are nothing more than statistical distributions.

I think people are confused because they do not really understand how software and computers work. I'd say they should learn some computability theory to gain some clarity but I doubt they'd listen.

If you really want to phrase it that way, organisms like us are "just" distributions of genes that have been pushed this way and that by natural selection until they converged to something we consider intelligent (humans).

It's pretty clear that these optimisation processes lead to emergent behaviour, both in ML and in the natural sciences. Computability theory isn't really relevant here.

I don't even know where to begin to address your confusion. Without computability theory there are no computers, no operating systems, no networks, no compilers, and no high level frameworks for "AI".

Well, if you want to address my "confusion" then pick something and start there =)

That is patently false - most of those things are firmly in the realm of engineering, especially these days. Mathematics is good for grounding intuition though. But why is this relevant to the OP?

There is no reason to do any of that because according to your own logic AI can do all of it. You really should sit down and ponder what exactly you get out of equating Turing machines with human intelligence.

Sorry, I edited my reply because I decided going down that rabbit hole wasn't worth it. Didn't expect you to reply immediately.

I'm not equating anything here, just pointing out that the fact that AI runs in software isn't a knockdown argument against anything. And computability theory certainly has nothing useful to say in that regard.

Right.

Well, you know, elaborate and we can have a productive discussion. The way you keep appealing to computability theory as a black box makes me think you haven't actually studied that much of it.

Not much to discuss.

Reply on news.ycombinator.com