They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that.
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.
In all fairness humans can also be considered next token predictors. It could be said that’s how we communicate with one another today. Presently LLMs lack other things, like physical presence in the world and continuity of input sensory data.
Humans learn to be a next token predictor as a kid when they learn to speak, an LLM cannot learn to be a next token predictor or anything of the sort, we have no clue how you could have an LLM learn human language just based on a thousands conversations with a human.
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".
These are false equivalences. The post you're responding to defined intelligence as learning rate. LLMs unequivocally do not learn. You can disagree with OP or agree, but what you have done is out of bounds. You're implicitly claiming that LLMs learn, albeit differently from humans. This is categorically false, unless you count training as some kind of "learning".
That's of course absurd, almost no user of an LLM also trains it. Instead they rely on queries submitted to pre-trained LLMs ("inference"). And don't bother yapping about context windows, it's just not anything like learning.
> The post you're responding to defined intelligence as learning rate.
How is the learning method or rate related to intelligence? LLMs learn during the training, much faster than any human. Yes, they drastically slow down their learning afterwards, but they still can learn a bit from an uploaded document or a web site.
And in the end they may be better at intelligence than any human, depending on the task and time given. I though intelligence is not about history but current performance.
How is this different than my examples, in which a possibility to fly is judged by (learning to) flapping the wings and not by the actual result?
> And don't bother yapping about context windows, it's just not anything like learning.
That's debatable, but I don't see how it's relevant here. You can ignore that part of my reply above, and the argument will remain unchanged.
Nevertheless, I don't understand how this not learning. Without it, no meaningful intelligent task can really be performed. LLM/human must learn the relevant bits from current situation in order to answer meaningfully. Often it requires to actually acquire new knowledge like reading a new piece of text unknown before. Feel free to link to a relevant discussion for me if you find this boring and settled.
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.