Well, actually, that’s a misinterpretation of what I said. I didn’t say I’d follow evolution, that’s a false assumption. What I meant was that I’ve always been approaching the problem of autonomy from the reward/punishment and behavior learning side, as opposed to the spatio-temporal pattern recognition/classification and abstraction side, which neural networks and now HTM has seemed to be making progress on at a respectable rate.
I think you’re missing the point of AI research if you still think that specific neural wiring is what’s needed to make a brain work. The vast majority of specific wiring is actually formed randomly and then pruned/reinforced throughout a creatures lifetime. If you mapped out the neocortex connections of an unborn baby, you would not find anything at all similar to what is in an adult brain, because very little information has been processed at that point in the brains development.
You will not find Broca’s area to be wired the same, or Wernicke’s. Infact, they don’t even exist in an infant, and are clearly areas of the neocortex that only come into being as the direct result of the brain processing information. It’s pretty obvious looking at the ‘areas’ of the neocortex that the various areas, such as the spatial association area, are a direct product of their proximity to whatever hard-wired input areas are surrounding it - where the spatial association area lies directly between the visual and tactile sensory processing areas. It only ‘becomes’ the spatial association area because the information processing activity of the immediately connected sensory organs to their respective areas processes into higher and higher level abstraction the further away from it you travel, and at the juxtaposition between such areas you will find these ‘magical’ areas that become responsible for handling more specific things.
Prominent patterns in sensory input, such as a spoken language, will find a place among the ‘balance’ of other prominent patterns that exist in the human experience. If a person loses sight, their visual regions will be taken over by all the surrounding areas that are still processing input and generating output. The cortex is highly dynamic and adaptive. You could remove any part of the cortex not immediately connected to a sensory input or a sub-cortical region that is regarded as handling a commonly recognized function, such as facial recognition, language recognition, etc… in a newborn baby, and they will grow up into a normally functioning adult, albeit with a slightly diminished capacity but still fully functioning in learning language and faces.
There is actually a lot of research on brains and the reward system. The dopaminergic pathways have been figured out, as well as serotonergic. Mammals have a common structure to the brain, regardless of the exact wiring of each individual structure amongst itself. It’s the overarching flow of information between the different parts, the ‘structure’ of the brain as a whole, which generates the brain wave ‘cycles’ that organize their connectivity into something relevant, not the exact wiring inside of each individual area - because that’s developed through the processing of information, exposure to the environment and learning behaviors through reward/punishment.
I’ve been studying brains a long while, in an effort to get to the cutting edge of the creative efforts, and we are definitely close. Between the promise and possibility of HTM and deep learning, someone has to connect these with a reward/punishment system to create a dynamic system capable of learning organically how to behave to achieve whatever it’s designed to achieve.
Personally, I believe that ANNs do not distill what it is that the brain is actually doing information-processing-wise, not nearly as much as SDRs do, computationally speaking. Even with the advent of so-called ‘neuromorphic’ chips that reduce the cost and increase the speed of running ANNs, I think that equivalent hardware designed to operate on SDRs would possess orders of magnitude more capability for the same power cost. It simply appears… sloppy, to manage every neuron’s synapse with every other neuron, and combinatorially more expensive.