Correct. The easiest way to avoid slamming into the sdc bottlenecks would be to carefully deploy AGI to uses where the 1 percent failures won’t cause unacceptable damages.
Any kind of human free environment is like that. Robotic cleaning, shelving, hauling, loading/unloading, manufacturing, mining, farming. Each case is where you close the store and lock the doors, have a separate section of a warehouse for robots, robotic areas of a factory with safety barriers, or robot only mines.
This to me looks like you could automate a significant chunk of the world economy, somewhere between 25-50 percent of it, just improving and scaling and integrating currently demonstrated systems.
You could also use AGI for tutoring, assisting with all the things it already does, as a better voice assistant, for media creation including visualization videos, and so on.
So when it hits the failure cases, when a robotic miner triggers a tunnel collapse, when a robotic cleaner breaks a toilet, when a shelver shoves over piles of goods, when a machine generated video has some porn—all these cases are ones where so long as the cost to fix the damage still makes it net cheaper than humans it’s worth using the AGI.
Over time as the error rate slowly drops you could deploy to more and more uses, start letting humans into the areas with the robots, etc.
This is very different from sdcs where there is this requirement for near perfection before anyone can deploy the cars or make any money.
Correct. The easiest way to avoid slamming into the sdc bottlenecks would be to carefully deploy AGI to uses where the 1 percent failures won’t cause unacceptable damages.
Any kind of human free environment is like that. Robotic cleaning, shelving, hauling, loading/unloading, manufacturing, mining, farming. Each case is where you close the store and lock the doors, have a separate section of a warehouse for robots, robotic areas of a factory with safety barriers, or robot only mines.
This to me looks like you could automate a significant chunk of the world economy, somewhere between 25-50 percent of it, just improving and scaling and integrating currently demonstrated systems.
You could also use AGI for tutoring, assisting with all the things it already does, as a better voice assistant, for media creation including visualization videos, and so on.
So when it hits the failure cases, when a robotic miner triggers a tunnel collapse, when a robotic cleaner breaks a toilet, when a shelver shoves over piles of goods, when a machine generated video has some porn—all these cases are ones where so long as the cost to fix the damage still makes it net cheaper than humans it’s worth using the AGI.
Over time as the error rate slowly drops you could deploy to more and more uses, start letting humans into the areas with the robots, etc.
This is very different from sdcs where there is this requirement for near perfection before anyone can deploy the cars or make any money.