"<a href=""https://www.youtube.com/watch?v=Gfr50f6ZBvo&amp;t=35m47s"">35:47</a> The very fact that the brain  is a supercomputer on its own is a pointer to the fact that the current paradigm of hooking up separate GPU or TPU cores to crunch ML algorithms is in itself fundamentally flawed", and we must if it's feasible devise architectures that are standalone like the brain, in a manner that even if some transistors fail, as they do in most brains, the system can still function despite that. Something like error correction, albeit at the level of classical computers.<br>Regarding solving biology, I think multiple paradigm shifts are needed, just as for true AGI. We have to somehow figure out how nature does it despite the messiness, the non-linearity, the complex dynamics involved etc. From a physics standpoint it's utterly hopeless, yet there She is, trundling along with exquisite precision, despite all that. Before leveraging synthetic biology we need to first figure out organic biology, how nature does what she does.