[mlyle]

I think there's a big focus on petaflops and that it may have been a good measure to think about initially, but now we're missing the mark.

If a human brain does its magic with 10 petaflops, and you have 1 petaflop, you should be able to make an equivalent to the human brain that runs at 1/10th of the speed but never sleeps. In other words, once you've reached the same order of magnitude it doesn't matter.

On the other hand, Kurzweil's math really comes down to an argument that the brain is using about 10 petaflops for inference, but it also is changing weights and doing a lot more math and optimization for training (which we don't completely understand). It may (or may not) take considerably more than 10 petaflops to train at the rate humans learn. And remember, humans take years to do anything useful.

Further, 10 petaflops may be enough math, but it doesn't mean you can store enough information or flow enough state between the different parts "of the model."

These are the big questions. If we knew the answers, IMO, we would already have really slow AGI.

[jart]

Yes I agree there's a lot of interesting problems to solve and things to learn when it comes to modeling intelligence. Vernor Vinge was smart in choosing the wording that we'd have the means to create superhuman intelligence by now, since no one's ever going to agree if we've actually achieved it.