Deep Neural Nets: 33 years ago and 33 years from now
karpathy.github.io
karpathy.github.io
I've been hearing some form of the "we're approaching diminishing returns" argument for decades now, and if anything, I've recently been seeing advancement compounding in ways it hasn't in the past.
As just one example, nearly every major tech school connected VC group I'm aware of has a photonics play in progress for enhancing AI workloads.
I wonder if those "things won't significantly change" folks are just broken records with it, or if there's new blood adopting the position each year while the old blood sees they were wrong and approach predicting future stagnation with greater trepidation.
(I'm sure it's the former, but the idealist in me likes to imagine it's the latter.)
> ...today's computers are terrible for neural nets. We are running in total emulation mode and there are many orders of magnitude of improvement to claim from a redesign of the architectures with neural nets in mind.