Earlier quoted context omitted.
> even rodents can outperform state of the art models at general tasks Rodents can't play Go or a lot of other humanly-meaningful tasks. We don't need to build an artificial cell. A cell is too many components that by blind luck happened to find ways to work together, this is as far from efficient design as can be. The same way we don't build two-legged airplanes, we don't need anything that's close to the wet spiky…
The problem with the analogy is that the car, by far, is not a general transportation device. Practically, most cars are solving a very constrained transportation problem: moving on roads that humans made. 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 constraine…
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.