I don’t think people focus on language and vision because they’re less boring than things like decision trees; they focus on those because the domains of language and vision are much broader than the domains decision trees, etc., are applied to. If you train a decision tree model to predict the price of a house it will do just that, whereas if you train a language model to write poetry it could conceivably write about various topics such as math, politics and even itself (since poetry is a broad scope). This is a (possibly) a step towards general intelligence, which is what people are worried/excited about.
I agree with your argument that algorithms such as decision trees are much better at doing things that humans can’t, whereas language and vision models are not.
Hmh, I didn’t want to give the impression I’m discounting particular architectures, I just gave the boosting example to help outline the target class of problems.
I don’t think people focus on language and vision because they’re less boring than things like decision trees; they focus on those because the domains of language and vision are much broader than the domains decision trees, etc., are applied to. If you train a decision tree model to predict the price of a house it will do just that, whereas if you train a language model to write poetry it could conceivably write about various topics such as math, politics and even itself (since poetry is a broad scope). This is a (possibly) a step towards general intelligence, which is what people are worried/excited about.
I agree with your argument that algorithms such as decision trees are much better at doing things that humans can’t, whereas language and vision models are not.
Hmh, I didn’t want to give the impression I’m discounting particular architectures, I just gave the boosting example to help outline the target class of problems.