If deep learning yields AGI, the question is how far can its intelligence jump beyond human level before it runs out of compute available in the world, using the improvements that can be made very quickly at the current level of intelligence. In short sprints, a hoard of handmade constants can look as good as asymptotic improvement. So the latter’s hypothetical impossibility doesn’t put convincing bounds on how far this could be pushed before running out of steam. And if by that point procedures for bootstrapping nanotech become obvious, this keeps going, transitioning into disassembling the world for more compute without pause. All without refuting the bitter lesson.
If deep learning yields AGI, the question is how far can its intelligence jump beyond human level before it runs out of compute available in the world, using the improvements that can be made very quickly at the current level of intelligence. In short sprints, a hoard of handmade constants can look as good as asymptotic improvement. So the latter’s hypothetical impossibility doesn’t put convincing bounds on how far this could be pushed before running out of steam. And if by that point procedures for bootstrapping nanotech become obvious, this keeps going, transitioning into disassembling the world for more compute without pause. All without refuting the bitter lesson.