Damn… nice work on the research! I will read through these as I get time. I genuinely didn’t think there would be much for manual labor stuff. I’m particularly interested in the plumber analysis.
I think augmentation makes a lot of sense for jobs where a human body is needed and it will be interesting to see how/if trade skill requirements change.
I’ll edit this as I read…
Plumbing. The article makes the point that it isn’t all or nothing. That as automation increases productivity, fewer workers are needed. Ok, sure, good point.
Robot plumber? A humanoid robot? Not very likely until enormous breakthroughs are made in machine vision (I can go into more detail…), battery power density, sensor density, etc. The places and situations vary far too greatly.
Rather than an Asimov-style robot, a more feasible yet productivity enhancing solution is automated pipe cutting and other tasks. For example, you go take your phone and measure the pipe as described in the link. Now press a button, walk out to your truck by which time the pipe cutter has already cut off the size you need saving you several minutes. That savings probably means you can do more jobs per day. Cool.
Edit 2
Oil rig worker. Interesting and expected use of AI to improve various aspects of the drilling process. What I had in mind was more like the people that actually do the manual labor.
Autonomous drones, for example, can be used to perform inspections without exposing workers to dangerous situations. In doing so, they can be equipped with sensors that send images and data to operators in real time to enable quick decisions and effective actions for maintenance and repair.
Now that’s pretty cool and will probably reduce demand for those performing inspections (some of whom will have to be at the other end receiving and analyzing data from the robot until such time as AI can do that too.
Autonomous robots, on the other hand, can perform maintenance tasks while making targeted repairs to machinery and equipment.
Again, technologies required to make this happen aren’t there yet. Machine vision (MV) alone is way too far from being general purpose. You can decide a MV system that can, say, detect a coke can and maybe a few other objects under controlled conditions.
But that’s the gotcha.Change the intensity of lighting, change the color temperature or hue of the lighting and the MV probably won’t work. It might also mistake diet coke can or a similar sized cylinder for a Pepsi can. If you want it to recognize any aluminum beverage can that might be tough. Meanwhile any child can easily identify a can in any number of conditions.
Now imagine a diesel engine generator, let’s say. Just getting a robot to change the oil would be nice. But it has to either be limited to a specific model of engine or be able to recognize where the oil drain plug and fill spot is for various engines it might encounter.
What if the engine is a different color? Or dirty instead of clean? Or it’s night, or noon (harsh shadows), overcast (soft shadows), or sunset (everything is yellow orange tinted)? I suppose it could be trained for a specific rig and a specific time of day but that means set up time costs a lot. It might be smarter to build some automated devices on the engine like a valve on the oil pan. And a device to pump new oil in from a vat or standard container or whatever. That would be much easier. Maybe they already do this, idk.
Anyway… progress is being made in MV and we will make far more. That still leaves the question of an autonomous robot of some kind able to remove and reinstall a drain plug. It’s easy for us but you’d be surprised at how hard that would be for a robot.
A big part of it seems to be manipulation of the results? So, like, devs writing tests for more parts of the code base, but ones that are written to always pass.
Ok I’m going to answer my own question because I’m too curious to wait lol
Goodhart’s Law states that “when a measure becomes a target, it ceases to be a good measure.” In other words, when we use a measure to reward performance, we provide an incentive to manipulate the measure in order to receive the reward. This can sometimes result in actions that actually reduce the effectiveness of the measured system while paradoxically improving the measurement of system performance. … The manipulation of measures resulting from Goodhart’s Law is pervasive because direct measures of effectiveness (MOEs), which are more difficult to manipulate, are also more difficult to measure, and sometimes simply impossible to define and quantify. As a result, analysts must often settle for measures of performance (MOPs) that correlate to the desired effect of the MOE. … These negative effects can sometimes be avoided. When they cannot, they can be identified, mitigated, and even reversed.
Use MOEs instead of MOPs whenever practicable and possible
Use the scientific method to generate new measurement data, rather than harvesting existing and possibly compromised data
Help customers establish authoritative and difficult-to-manipulate definitions for measures
Identify and avoid the use of manipulated data and data prone to manipulation
Use measurement data not generated by the organization being measured
Collect data secretly or after a measurable activity has already occurred
Measure all relevant system characteristics rather than just a representative few
Randomize the measures used over time
Wargame or red team potential measures
This report recommends that the organizations that employ analysts should do the following:
Return to the roots of operational research to focus more on direct measurements in the field
Answer the questions that should be answered, rather than the questions that can be answered simply because the required data are already available
Train analysts on MOEs, MOPs, and Goodhart’s Law and how they are interrelated
Make recognition of Goodhart’s Law part of the internal peer review process and part of all delivered analytical products