When I read this I feel people must be using it in the wrong way. I use it all the time to quickly solve tech problems I mostly know something about, however it’s so smart it regularly takes 1-2 hour problems for me and turns them into 10 mins ones. That is definitely not dumb from my perspective, but obviously it’s also not smart in it will give me profound understanding of something, but ok whatever, it’s still a massive productivity booster for many problem.

When you call it dumb, what do you mean? Can you give some examples?

Please don’t give computational examples we all already understand it does inference and doesn’t have floating point computational capabilities or reasoning, and so many give such examples for some silly reason.

It's dumb in the sense that it doesn't actually have a symbolic understand of what it's actually saying.

I use it quite frequently too, mostly for solving coding problems, but at the end of the day it's just regurgitating information that it read online.

If we took an adversarial approach and deliberately tried to feed it false information, it would have no way of knowing what's bullshit and what's legit, in the way that a human could figure out.

A lot of people who've never used ChatGPT make the mistake of thinking it has symbolic reasoning like a human does, because its language output is human too.

How many book mistakes have you found, as a human, so far? How about deliberate mistakes hidden in plain sight? I once re-validated for 3 times the same test set and was still finding mistakes.

>How many book mistakes have you found, as a human, so far?

All the time. Probably every single day I read something and say "that's clearly bullshit".

Agreed on getting excellent hints from it, shortening the time to figure out stuff. But eg. just now it gave me an example algorithm that, only at 2nd look, turned out to be complete nonsense. You know That colleague, who shines in the eyes of his managers, but peers know that half what he does is garbage.

Its ability to correct itself is very impressive when given the feedback. I imagine with the proper feedback loop it can advance very fast. E.g., when asked to write a piece of html markup, if it could "see" how the rendered layout is different from what was asked for, it could adjust its solution without human involvement. If it could run the deployment script and see where it fails, it could apply all the fixes itself until it works. If it could run the unit tests and see where its solution breaks the other parts of the system, it would need much less handholding.