That’s not a meaningful technical obstacle. If you wanted to, you could just take the output of the model and use it at each iteration of the training phase to perform (badly) whatever task the model is intended to do.

The reason noone does this is you don’t have to and you’ll get much better results if you first fully train and then apply the best model you have to whatever problem. Biological systems don’t have that luxury.

Reinforcement learning on real robots in real time has been done lots of times, since back in the 90s at least. It’s painfully slow.

Why is it slow?

We know a human uses roughly 100 watts. And teaching a new specific task takes only showing maybe 10 times to get to 80%.

The learning function in humans are definitely connected with both training/recitation.

I'm seeing that as the big roadblock between thinking machines and a really big autocomplete we have now.