There seems to be a fundamental assumption that post superintelligence world factories would look exactly like how it’s done today. A lot of work in factories and the machines that are designed are kept with actual humans in mind. The machines which automate the entire process look very different and improve the efficiency exponentially.
Most superintelligent systems predicated on today’s research and direction are looking at using Reinforcement learning. At some point, presumably we will figure out how to make an agent learn from the environment (still in RL realm) and then we will have so called superintelligent systems. My contention here is that, RL by definition is an optimizer where it figures out algos to do tasks that may or may not match human designed algos. (there is a 2023 deepmind paper where they taught robots to play football and they eventually played ). Most work happens in software even for robotics, and with enough compute you could arguably replicate years of learning within a week. Doubling times of a year are not too fast.
That being said: Robotics is unlikely to follow the human-like distribution of labor. Some of the places we will see first adoption and highest gains is where historically there is a shortage of labor, (Eg: Fab assembly lines, rare earths metal extraction) or you need a specialization to qualify. That is replicated even in software already.
Other aspect is what would assembly lines or factories look like if they are fully automated. I feel we havent even started to think about this in depth. At a very high level, the advanced form of robots will be like any other machines. Similar to the leap from washing clothes by hand or using a washing machine. That way, given the barriers for adoption are smaller (barriers and times are high if humans are supposed to learn how to use the said machines vs just take the final output and review its quality), the pace should be much much faster in theory.
There seems to be a fundamental assumption that post superintelligence world factories would look exactly like how it’s done today. A lot of work in factories and the machines that are designed are kept with actual humans in mind. The machines which automate the entire process look very different and improve the efficiency exponentially.
Most superintelligent systems predicated on today’s research and direction are looking at using Reinforcement learning. At some point, presumably we will figure out how to make an agent learn from the environment (still in RL realm) and then we will have so called superintelligent systems. My contention here is that, RL by definition is an optimizer where it figures out algos to do tasks that may or may not match human designed algos. (there is a 2023 deepmind paper where they taught robots to play football and they eventually played ). Most work happens in software even for robotics, and with enough compute you could arguably replicate years of learning within a week. Doubling times of a year are not too fast.
That being said: Robotics is unlikely to follow the human-like distribution of labor. Some of the places we will see first adoption and highest gains is where historically there is a shortage of labor, (Eg: Fab assembly lines, rare earths metal extraction) or you need a specialization to qualify. That is replicated even in software already.
Other aspect is what would assembly lines or factories look like if they are fully automated. I feel we havent even started to think about this in depth. At a very high level, the advanced form of robots will be like any other machines. Similar to the leap from washing clothes by hand or using a washing machine. That way, given the barriers for adoption are smaller (barriers and times are high if humans are supposed to learn how to use the said machines vs just take the final output and review its quality), the pace should be much much faster in theory.