I'd go a step further, something which resembles how "wet brains" (biological) actually work, but which could be produced easily. Biological neural networks ar

I'd go a step further, something which resembles how "wet brains" (biological) actually work, but which could be produced easily. Biological neural networks are nowhere near as connected as ANNs, which are typically fully connected. With biological neurons, the ingress / egress factors are < 10. So they are highly local It is also an entirely different model, as there is no such thing as backpropagation in biology (that we know of). What they do have is lieu of backpropagation is feedback (cycles) And maybe there are support cells/processes which are critical to the function of the CNS that we don't know of yet. There could also be a fair amount of "hard coded" connectedness, even at the higher levels. We already know of some. For instances, it is known that auditory neurons in the ears are connected and something similar to a "convolution" is done in order to localize sound source. It isn't an a emergent phenomena - you don't have to be "trained" to do it. This is not surprising give life has had billions of years and a comparable number of generations in order to figure it out. I guess in theory this could all be done in software. However given the trillion+ neurons in primate/human brains, this would be incredibly challenging on even the thousand-core machines we have nowadays. And before you scream "cloud" it would not have the necessary interconnectedness/latency. It would be cool if you could successful model say, a worm/insect with this approach.