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.
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.
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.