human AI also evolved by solving constrained problems, one at a time. Life existed before the visual system , but once this was solved it moved on to do other things. In AI we have a number of sensory systems seemingly solved: Speech recognition, visual object recognition, and we are closing to certain output (motor) systems: NLP text synthesis systems seem a lot like the central pattern generators that control human gait, except for language. What seems to be missing is the "higher-level ", more abstract kernels that create intent, which are also difficult to train because we don't have a lot of meaningful datasets. Or maybe , we have too big datasets (the entirety of wikipedia) but we don't know how to encode it in a meaningful way for training. It's not clear however that these "integrating systems" are going to be fundamentally different to solve than other subsystems. It certainly doesn't seem to be so in the brain, since neocortex (which hosts both sensory and motor and higher level systems) is rather homogeneous. In any case, it seems we 're solving problems one after another without copying nature's designs, so it's not automatically true that we need to copy nature in order to keep solving more.
Do you have examples of those systems which are competitive in general use rather than specialized niches? The cloud offerings from Amazon, Google, etc. are good in the specific cases they’re trained on but fall off rapidly once you get new variants which a human would handle easily.
We don't have anything remotely close to a wetware-enabled transportation device, something that can move on flat land, climb mountains, swim in bodies of water, crawl in caves, hide in trees.
Within the constrained problem, the machine exceeds humans. But generally, the wetware handles moving around much better.
Same with AI: in a constrained problem, the AI can excel (beat humans in chess and go). But I doubt we will see a general AI any time soon.