I'm an ML researcher and a sheep farmer. My lambs are up on their feet, nursing, thirty minutes after birth. Once they have enough blood sugar they are capable of running to keep up with mom. They come prepackaged with a fully functional quadruped locomotion scheme and associated path planning and obstacle avoidance and fully functional vision, touch, and audio processing algorithms. And this is with basically zero embodied learning.
The difference between sheep and humans is, I think, that humans actually "learn" on three timescales instead of two, unlike every other living thing. Sheep learn on evolutionary timescales, through natural selection, and on the timescale of an individual sheep lifetime. But humans learn on the timescale of society as well, in between the other two. I believe that the difference between us and animals isn't so much our dramatically increased intelligence as it is the ability to pass detailed descriptions of what we know on to others, e.g. to language. The amount of information in the world has increased exponentially since the Renaissance. Societies advance on a characteristic timescale of about a century, as far as I can tell. Much faster than evolution, but quite a bit slower than individuals.
In ML, we are doing the same thing as sheep, basically. We have two "learning loops". One is the continual development of new DNN architectures, which corresponds to what evolution does; the other is the training of these architectures on data, which corresponds to what individuals do. But the outer one still mostly proceeds at the speed of human cleverness, not computation. But we are just... relying on the fact that society, and the data it produces, is there for the consumption of ML. We do not have any ideas for speeding up the production of that data except for the hope that we already have enough of it to kickstart GAI. If we do, we'll go through the singularity. If we don't, we won't.
But we can at least solve the "evolutionary learning" problem for ML. We'll need to bring back something like genetic algorithms, or make DNN architectures somehow differentiable so they can be efficiently evolved.