I'm not seeing an argument here. Mammalian evolution is great. So is backprop. They're both methods to efficiently search the state-space of parameters. If your

I'm not seeing an argument here. Mammalian evolution is great. So is backprop. They're both methods to efficiently search the state-space of parameters. If your argument is scale, you have to argue how scale precludes a sufficiently general learning algorithm and enough computation from capturing the underlying structure. >The biochemistry of a single neuron is likely oodles more complex than the most complex AI we've made. There's a lot of biochemistry in service to supporting the neurons existence exclusive from its functional properties for the brain. In terms of a neurons I/O mapping, deep learning has that covered[1] >Yes, it's basically all just statistics... but we still don't understand how to go from statistics to intelligence. But this assumes intelligence isn't an emergent phenomenon of sufficiently general learning. GPT-3 suggests this might be the case. [1] https://www.biorxiv.org/content/10.1101/613141v1.full.pdf https://www.biorxiv.org/content/10.1101/613141v1.full.pdf