Other examples of fields like this include: medicine, mechanical engineering, education, SAT solving, and computer chess.
To give a maybe helpful anecdote—I am a mechanical engineer (though I now work in AI governance), and in my experience that isnt true at least for R&D (e.g. a surgical robot) where you arent just iterating or working in a highly standardized field (aerospace, hvac, mass manufacturing etc). The “bottleneck” in that case is usually figuring out the requirements (e.g. which surgical tools to support? whats the motion range, design envelope for interferences). If those are wrong, the best design will still be wrong.
In more standardized engineering fields the requirements (and user needs) are much better known, so perhaps the bottleneck now becomes a bunch of small things rather than one big thing.
Importantly, this is an example of developing a specific application (surgical robot) rather than advancing the overall field (robots in general). It’s unclear whether the analogy to an individual application or an overall field is more appropriate for AI safety.
Good point. Thinking of robotics overall, it’s much more of a bunch of small stuff than one big thing. Though it depends how far you “zoom out” I guess. Technically Linear Algebra itself, or the Jacobian, is an essential element of robotics. But could also zoom in on a different aspect and then say that “zero backlash gearboxes” (where Harmonic Drive is notable as it’s much more compact and accurate than prev versions—but perhaps a still small effect in the big picture) are the main element. Or PID control, or high resolution encoders.
I’m not quite sure how to think of how these all fit together to form “robotics” and whether they are small elements of a larger thing, or large breakthroughs stacked over the course of many years (where they might appear small at that zoomed out level).
I think that if we take a snapshot in a specific time (e.g. 5 years) in robotics, there will often be one or very few large bottlenecks that are holding it back. Right now it is mostly ML/vision and batteries. 10-15 years ago, maybe it was the CPU real time processing latency or the motor power density. A bit earlier it might be gearbox. These things were fairly major bottlenecks until they got good enough that it switches to a minor revision/iteration regime (nowadays there’s not much left to improve on gearboxes e.g., except for maybe in very specific use cases)
To give a maybe helpful anecdote—I am a mechanical engineer (though I now work in AI governance), and in my experience that isnt true at least for R&D (e.g. a surgical robot) where you arent just iterating or working in a highly standardized field (aerospace, hvac, mass manufacturing etc). The “bottleneck” in that case is usually figuring out the requirements (e.g. which surgical tools to support? whats the motion range, design envelope for interferences). If those are wrong, the best design will still be wrong.
In more standardized engineering fields the requirements (and user needs) are much better known, so perhaps the bottleneck now becomes a bunch of small things rather than one big thing.
Importantly, this is an example of developing a specific application (surgical robot) rather than advancing the overall field (robots in general). It’s unclear whether the analogy to an individual application or an overall field is more appropriate for AI safety.
Good point. Thinking of robotics overall, it’s much more of a bunch of small stuff than one big thing. Though it depends how far you “zoom out” I guess. Technically Linear Algebra itself, or the Jacobian, is an essential element of robotics. But could also zoom in on a different aspect and then say that “zero backlash gearboxes” (where Harmonic Drive is notable as it’s much more compact and accurate than prev versions—but perhaps a still small effect in the big picture) are the main element. Or PID control, or high resolution encoders.
I’m not quite sure how to think of how these all fit together to form “robotics” and whether they are small elements of a larger thing, or large breakthroughs stacked over the course of many years (where they might appear small at that zoomed out level).
I think that if we take a snapshot in a specific time (e.g. 5 years) in robotics, there will often be one or very few large bottlenecks that are holding it back. Right now it is mostly ML/vision and batteries. 10-15 years ago, maybe it was the CPU real time processing latency or the motor power density. A bit earlier it might be gearbox. These things were fairly major bottlenecks until they got good enough that it switches to a minor revision/iteration regime (nowadays there’s not much left to improve on gearboxes e.g., except for maybe in very specific use cases)