Jack, to be specific, we expect to have AI that can jump into specific classes of roles, and take over the entire niche. All of it. They will be narrowly superhuman at any role inside the class.
If right now, strategy games, both of the board and the realtime clicking variety, had direct economic value, every human doing it would already be superfluous. We can fully solve the entire class. The reason is, succinctly:
a. Every game-state can be modeled on a computer with the subsequent state resulting from a move by the AI agent provided
b. The game-state can be reliably converted to a score that is an accurate assessment of what we care about—victory in the game. That is, it’s usually a delayed reward, but a game-state either is winning or it is not and this mapping is reliable.
For real world tasks (b) gets harder because there are subtle outcomes that can’t be immediately perceived, or they are complex to model. Example: an autonomous car reaches the destination but has damaged it’s own components more than the value of the ride.
So it will take longer to solve the class of :
robotics manipulation problems where we can reliably estimate the score resulting from a manipulation, and model reasonably accurately the full environment and the machine in that environment.
This is most industrial and labor tasks on the planet in this class. But the whole class can be solved relatively quickly—once you have a general solver for part of it, the rest of it will fall.
And then the next class of tasks are things where a human being is involved. Humans are complex and we can’t model them in a simulator like we can model rigid bodies and other physics. I can’t predict when this class will be solved.
Jack, to be specific, we expect to have AI that can jump into specific classes of roles, and take over the entire niche. All of it. They will be narrowly superhuman at any role inside the class.
If right now, strategy games, both of the board and the realtime clicking variety, had direct economic value, every human doing it would already be superfluous. We can fully solve the entire class. The reason is, succinctly:
a. Every game-state can be modeled on a computer with the subsequent state resulting from a move by the AI agent provided
b. The game-state can be reliably converted to a score that is an accurate assessment of what we care about—victory in the game. That is, it’s usually a delayed reward, but a game-state either is winning or it is not and this mapping is reliable.
For real world tasks (b) gets harder because there are subtle outcomes that can’t be immediately perceived, or they are complex to model. Example: an autonomous car reaches the destination but has damaged it’s own components more than the value of the ride.
So it will take longer to solve the class of :
robotics manipulation problems where we can reliably estimate the score resulting from a manipulation, and model reasonably accurately the full environment and the machine in that environment.
This is most industrial and labor tasks on the planet in this class. But the whole class can be solved relatively quickly—once you have a general solver for part of it, the rest of it will fall.
And then the next class of tasks are things where a human being is involved. Humans are complex and we can’t model them in a simulator like we can model rigid bodies and other physics. I can’t predict when this class will be solved.