These models can do a lot of things but they also can't do a lot of things. In order to use these models effectively you have to understand that they are next token predictors and how that allows it to do what they do.
For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new skills to them, so they will continue to get more useful.
You don't see how that is very different? For an LLM to be as smart as a human it has to be able to learn like a human. Like you don't evaluate how smart a human is based on how much he knows, you evaluate it based on how fast he learns. And LLM are so bad at learning its ridiculous, they lack that part of the brain that lets humans be smart and learn so fast and easily.
"For a plane to fly as well as a bird it has to be able to flap its wings".
"For a submarine to swim as well as a fish it has to be as light as fish".
Yes, but not because they are useful.
The issue is of course with using the word "dumb": they are next token predictors, no doubt about it, but whether LLms as a class of system are smart or dumb is entirely unknown and entirely variable in time.
To interact with them effectively you must know how they behave, just like you have to know how humans behave to interact with them effectively. If you disagree, find someone with autism and have a conversation with them.
So people call them dumb since like dumb people they make strong statements about things they don't understand. And it doesn't matter how much smart things you encode them with, they will keep making strong statements about things they don't understand until they are fundamentally changed.
But since LLM are very smart about things where they have extensive data they can still be used to reliable solve many problems and probably in the future where we understand that better almost completely replace most lawyer and doctors work etc, because a lot of what a frontline doctor or basis lawyer work is very repetitive and can be encoded with billions of examples and decision paths into an expert system framework the LLM will follow.
So people say LLM are dumb since LLM will always keep making dumb statements. This is the same way we call Elon Musk dumb for making a lot of dumb statements, he is a smart guy but he makes dumb statements so her is dumb.
If ever there was a human quality.
Also, your explanation of "dumb" is really favoring the anti-llm side, and its a very generous interpretation. I suspect what is much more likely meant, is that token predictors cannot be smart, not now nor in the future after improvements, because they are token predictors and predicting tokens is not how intelligence works.
All of this is of course unfounded, and hidden behind the word "dumb".
Why do you think that? LLM are used as expert systems today, in order to quickly navigate problems by breaking them down and iterating between different well known possible solutions and paths to check etc. That is how they work, they do that by using their next token predictions, and for things they aren't well trained on they will produce dumb results.
LLM has solved enough problems that almost nobody has the view you ridicule here, but there are still many who think LLM are thinking just like humans and that you can trust them just like humans. So its important to remind people these are just token predictors and lack many things humans do.
> If ever there was a human quality.
Humans can avoid doing that by using introspection, LLM can't. That some humans do it by not using introspection doesn't mean humans are incapable of it, we know humans are capable of it, which is why we can point out when the LLM is wrong with certainty, humans as a group make extremely good predictions.
And thats encoded as a set of next token predictions. So the way to see how reliably it solves a problem is to look at the chain of predictions, and see where it is unreliable at finding the next spot, or where it always fails and you need to add that link to the dataset to train it.
This isn't magic, today we understand pretty well how to add new skills to LLM, and the better this is understood the faster progress will be.
This also means that if a context doesn't have any good predictions, it will produce a dumb prediction for that context. This results in these bad outcomes, because currently LLM doesn't have a map for where predictions are good or bad.
Or should the discourse in a diverse community like HN only reflect the positions you personally hold?
My point is it's silly to whine that HN is a place where multiple points of view on the topic are aired out and discussed.
If you want a personal echo chamber where only your own beliefs are affirmed and anything else is flagged off or downvoted, I'm sure you can go find one or, worst case, vibe code one into existence.
I get the impression you want me to concede that the particular points of view you disagree with aren't worthy of representation here on HN.
I'm not going to do that.
Since we disagree on the present let’s informally do a “remind me 2 years” to this discussion and see what’s happened then.
You mean your particular version of it.
It's interesting to see you consistently missing this point.
You've decided LLMs are clearly more than just complex but mindless statistical models.
You've decided that based on, it seems, the very impressive things these tools are capable of.
Therefore if anyone claims they're just mindless stastical models--with or without any attached judgement as to their actual utility or usefulness--then they are ipso facto wrong.
(And yes I just used endashes, damnit!)
That's on you.
It is in fact possible to simultaneously believe that LLMs are mindless token predictors and that they're enormously powerful.
These are entirely orthogonal beliefs.
Heck you could equally believe that LLMs represent true emerging AGI and that they still remain deeply flawed and are only an incremental step along the path of automation.
Or somewhere in between.
And discussing that space of possibilities is, I'd hope, precisely what HN is for.
"These models are probabilistic, you shouldn't blindly trust them in spaces where accuracy is really important" seems like pretty sound advice to me.
I mean even perennially contentious topics will get this behavior.... some thing about emacs makes the front page, within a day or two there will be a vim post up there. Same with Rust is (good|bad), or if systemd creates an even more awesome tool, the haters will come along and recycle stories about bugs from over a decade ago.
There's a lot of people here. Not all of them read it every hour, and discussions like this among large groups often take a very long time with lots of repetition. Human group dynamics (aka politics) is slow.
> So tired of this discourse and this site.
You're welcome to leave if you don't like it. The site was like this long before you joined, and will like it long after you leave I'm sure.
It's also worth noting, that an awful lot of math discoveries are perfectly in line with dumb next token generators - they are finding a way to formally construct an argument and being surprised when it doesn't work, or surprised at the outcome of the grind. Not all of them are made by brilliant leaps of intuition.