The world is not automatically divided up into lots of separate tasks.
If you divide tasks into too many small pieces, too many little buckets, many important problems can fall through the gaps.
For example. If you use RL to train a plumbing robot. And separately train an electrician robot. Then neither of these robots is can solve the problem that you get an electric shock whenever you turn on the tap.
If you train on a few huge buckets, then you have 1 robot that does everything, and that’s basically an AGI again.
And in this RL as a service model, wouldn’t there be people doing RL for AI research.
So, when this model gets good enough, someone can just say “build an AGI” and get one. Because all tasks are being automated, and that includes the task of building AGI.
Actually, RL is based on trial and error. It would be hard to train an AI researcher without giving it the opportunity to run arbitrary code in training.
The world is not automatically divided up into lots of separate tasks.
If you divide tasks into too many small pieces, too many little buckets, many important problems can fall through the gaps.
For example. If you use RL to train a plumbing robot. And separately train an electrician robot. Then neither of these robots is can solve the problem that you get an electric shock whenever you turn on the tap.
If you train on a few huge buckets, then you have 1 robot that does everything, and that’s basically an AGI again.
And in this RL as a service model, wouldn’t there be people doing RL for AI research.
So, when this model gets good enough, someone can just say “build an AGI” and get one. Because all tasks are being automated, and that includes the task of building AGI.
Actually, RL is based on trial and error. It would be hard to train an AI researcher without giving it the opportunity to run arbitrary code in training.