No, most likely not. Our best theories of the brain are both computationally and evolutionarily parsimonious.
The brain is massively complex, we still don't understand the ways that it computes and learns in detail except generalities such as using electrical and chemical signals, neurotransmissors, and some knowledge of architecture and region differentiation.
Those models all point to an extremely complex system with quite a number of different specialized parts, with neurons individually having quite amazing complexity (and so on for capsules, neuronal systems).
I think it's unlikely a "simple" system (if you could consider a vanilla CNN simple, for example) will solve human-like AGI efficiently (either data-efficiently or computationally efficiently). There are too many requirements.
Also, while simplicity is good... a researcher shouldn't be caught obsessing over it exclusively, as it is not the end goal, which personally would be the advancement of understanding and of our capabilities instead.
Again, not really. Broadly speaking, the AI community is pretty far behind the neuroscience community in terms of understanding the basic principles underlying how brains work. https://mitpress.mit.edu/books/principles-neural-design
The often quoted 6×10^9 base pairs overstates the complexity as a lot of non coding DNA exists, and much of what’s left is redundant or does not relate to the brain.
PS: We actually know quite about about how the brain learns and computes information. The are really interesting information about how the optic nerve encodes information for example.
Same DNA that is referred to in other discussions as 'the ultimate spaghetti code'.
Basically, DNA includes the design for both the car, car factory, and all the component factories that feed the car factory, and the mines that feed the component factories. People have dependencies on their diet for things like vitamin C, but that’s a whole other issue.
PS: Mitochondria also have DNA, but that’s just location specific and can simply be added to the total for the cell.