Comment by Jensson
7 hours ago
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 an LLM to be as smart as a human it has to be able to learn like a human.
"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".
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