george talks about "A banana peel? Did you put all those things in your simulator?" -- it doesn't matter if it's in the simulation yet, the generalization of your algorithm is what matters... once it's ready to act with new objects by itself, put it in there to see if it can generalize around it. Humans dont know what a banana peel is until they've been exposed to it, and they work with it.
smart of carmack to say "almost anyone", clearly smart people are working via the humanoid aspect and I'd guess they'll have some discoveries before the simulation people, but not necessarily because they're going from the humanoid approach
At certain point, training / inference difference will be blur. Is GPT-3's prompt some kind of "learning" or just a different way to poke the model?
For RL, things like domain randomization, particularly, the way to train "teacher-student" network (like in MIT Cheetah running paper), when inference, it recovers physic parameters of the body, does that count as "learning"?
The simulation may not have "banana peel", but for a multi-modality model, it is not hard to imagine it has encountered an object with similar physic parameters before and "other models" in the system, after "fall over", can recover such parameters and won't be tricked again. Does that count as "learning"?
I was just saying that I think George is wrong to say "The only way forward is on device learning." Especially his use of the word "only". I think in order to interact in the real world, you will have to learn in the real world to some extent, but like pilots do in training, something/someone can learn a lot from simulations.
Something to consider is that living creatures in general have hundreds of millions (billions?) of years and (insert very, very large number) of variations of trail and error in the real world. Intelligence and learning came way before humans.
Oh, I am totally in the camp of "simulation is enough". My reply is a convoluted way to argue that once learned enough in simulation, adaptation in real-world should just work (if you treat adaptation as a way of learning).
smart of carmack to say "almost anyone", clearly smart people are working via the humanoid aspect and I'd guess they'll have some discoveries before the simulation people, but not necessarily because they're going from the humanoid approach