We made computers with billions of times as much compute and memory from the 1960s. Previously intractable problems like machine perception and machine planning to resolve arbitrary failures—were only really begun to be solved with neural networks around 2014ish.
Previously they were theoretical. Now it’s just a matter of money and iterations.
See previously to define a subtask for a von neuman machibe like “mine rocks from the asteroid you landed on in other tasks and haul them to the smelter” could have a near infinite number of failures. And with previous robotics each failure had to be explicitly handled by a programmer who anticipated the specific failure or a worker on site to resolve it.
With machine planning algorithms you can have the machine estimate the action that has a (better than random chance, ideally close to the true global maxima) probability of scoring well on a heuristic of success. And you need neural networks to even perceive the asteroid surface in arbitrary scenarios and lighting conditions. And you need realistic computer graphics and simulated physics to even model what the robot will see.
It’s still many generations of technology away but we can actually in concrete terms specify how to do this, and how we could iterate to a working system if we wanted to.
We made computers with billions of times as much compute and memory from the 1960s. Previously intractable problems like machine perception and machine planning to resolve arbitrary failures—were only really begun to be solved with neural networks around 2014ish.
Previously they were theoretical. Now it’s just a matter of money and iterations.
See previously to define a subtask for a von neuman machibe like “mine rocks from the asteroid you landed on in other tasks and haul them to the smelter” could have a near infinite number of failures. And with previous robotics each failure had to be explicitly handled by a programmer who anticipated the specific failure or a worker on site to resolve it.
With machine planning algorithms you can have the machine estimate the action that has a (better than random chance, ideally close to the true global maxima) probability of scoring well on a heuristic of success. And you need neural networks to even perceive the asteroid surface in arbitrary scenarios and lighting conditions. And you need realistic computer graphics and simulated physics to even model what the robot will see.
It’s still many generations of technology away but we can actually in concrete terms specify how to do this, and how we could iterate to a working system if we wanted to.
Fair enough.