Earlier quoted context omitted.
>Live systems in nature seem to solve similar problems with way less compute available Do they really? They're certainly more energy-efficient in business-as-usual mode, but a human brain has 86 billion neurons, 600+ trillion synapses(!), and each instance takes 15-20+ years to train to do complex logical tasks. Even if the per-cell work is tiny (and, is it? cells are amazingly complex), 86 billion (or 600+ trillion)…
You are ignoring the fact that a toddler, once their musculature develops, is able to learn to walk after several tries. Show me a humanoid robot that can do that.
Don't get me wrong, I'm not claiming that Machine Learning is as generally capable as animals/humans, and I don't entirely disagree with the OP (i.e. I don't know if our current approach has a chance of scaling to human-level capabilities). I just don't think computation-wise it compares that badly to animals, considering that it's the result of a few decades of work, largely on repurposed silicon