Quora CEO Adam D'Angelo proposes a practical benchmark for defining AGI during a discussion on AI capabilities.
Prediction Not checkable as stated
Adam D'Angelo: LLMs will perform all human tasks cheaper within 5-15 years
“I do think at some point you get to LMs can, they can do Everything, every single thing a human can do for cheaper. Like, I don't see a reason why we don't eventually get there. That may take five, 1015 years.”
Opinion
Adam D'Angelo: Current LLM architectures are not hitting performance limits
“I don't think so. I mean, I think there are certain things like memory and learning, like continuous learning that are not very easy with the current architectures. I think even those you can sort of fake and maybe are, we're going to be able to get them to wo…”
Prediction Not checkable as stated
Adam D'Angelo: Current AI paradigm is far from diminishing returns
“I think the current paradigm is pretty good. And I think we're nowhere near the sort of like diminishing returns of continuing to push on it. And I bet. Yeah, I guess I would just bet that you can keep doing different innovations within the paradigm to get the…”
Prediction Not checkable as stated
Adam D'Angelo: Top experts can solve fundamental AI challenges within five years
“Nothing seems fundamentally so hard that it couldn't be solved by the smartest people in the world working incredibly hard for the next five years.”
Prediction Not checkable as stated
Adam D'Angelo: AI computer use will automate major work within two years
“And then there's some things like computer use that are still not quite there, but I think we'll almost definitely get there in the next year or two. And when you have that, I think we're gonna be able to automate a large portion of what people do.”
Prediction Not checkable as stated
Adam D'Angelo: Brute-force scaling will yield AI capable of average human jobs
“I think that's going to be more a function of when we can produce something that is as good as human intelligence, even if it takes a lot more compute, a lot more energy, a lot more training data. We could just put in all that energy and still get to software …”