Well, let's learn to walk first.
Right now we have just massively jagged intelligence that performs stunning feats in math and single-shotting three.js games, and falls flat on it's face in many every day scenarios.
And, it can't even learn.
Right now we have just massively jagged intelligence that performs stunning feats in math and single-shotting three.js games, and falls flat on it's face in many every day scenarios.
And, it can't even learn.
In any case, human intelligence as a goal is special since we live in a human world, and one of the prime goals of AI is to be able to do human jobs. If a human can do something and AI can't then that is a limitation, while if a human can't and AI can't either, that doesn't matter for the time being.
Yeah, I shouldn't have said AI can't walk yet, but most of what it can do is just due to scale - we're still just building big transformers, basically using the same 10 year old architecture that accidentally set us off on this path. I'm not sure we're really progressing towards human/animal type intelligence, just learning how much of our own capability can be realized by automating language.
But anyhow I agree that inference-time learning doesn't exist and it's a big issue still I think.
That's learning, whether you agree or not, and whether you like it or not. Baby steps, yes, but walking nevertheless.
Yes, I'm aware of the linear regression example, and a few others, but these appear to just be specific capabilities that were learnt during pre-training, presumably pursuant to reducing errors on similar-but-different training samples, not any kind of generic run-time learning capability.