I don’t disagree with most of what you said. I think so far the following jobs are safe from direct AI replacement, because it is much harder to replace manual laborers.
Oil rig worker
Plumber
Construction worker
Landscaper/gardener
Telephone repair tech
Mechanic
Firefighter
Surveyor
Wildlife management officer
Police
What companies won’t realize until too late is that paying customers need jobs to pay for things. If AI causes unemployment to rise to some ungodly high, paying customers will become rare and companies will collapse in droves.
Thanks for actually rising to the challenge, it was actually fascinating to do the research to see how AI is affecting the various industries, and how deeply. I will say that I was able to find direct evidence of replacement in 7/10 of them, 1 was work that is similar and could easily be adapted (telecom line repair), one was an analysis that I think has a lot of good points (plumber), and one was genuinely all about augmenting the capabilities of workers already in place (wildlife conservation/officer).
What companies won’t realize until too late is that paying customers need jobs to pay for things. If AI causes unemployment to rise to some ungodly high, paying customers will become rare and companies will collapse in droves.
I wholeheartedly agree. Functionally, we are going to have to institute a UBI model. It is the only way that society will be able to distribute funds properly when population outpaces jobs due to the exponential growth of populations and the rapidly shrinking landscape of jobs. The corporations are going to need to pay us one way or another.
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.
Had a thought that deserved a separate post. Your selection of MV tasks was rather perverse for the tasks we were discussing. Identifying a pop can is definitely something that humans can do easily because pop cans were made for us to be able to easily identify them. Level the playing field and let’s start looking for internal stress fractures in the superstructure of a 100’ tall concrete bridge. That is something that AI drones are already being designed and deployed for. The drone can easily approach the bridge with a suite of sensors that let it see deep into the superstructure and detect future failure points. Humans would struggle to do that. I have also seen things about maintenance drones that are able to crawl on the bridge using a variety of methods (usually they are designed for specific bridges) that are able to fill cracks with sealant and ablate rust using lasers, then paint the freshly cleaned metal. The benefit of replacing a workforce with AI-driven robotics is that you can purpose-build and purpose-train the tool to do exactly what you need it to do. A robot that scurries into a crawl space to run a pipe for a plumber doesn’t need to know how to do anything but recognize where it goes, what not to touch, and how much force to use when installing it. It doesn’t need to identify a pop can, it doesn’t need to draw a Rembrandt. All it needs to do is pull a pipe and weld it in place (and yes, I am oversimplifying a bit, I know that).
The other thing that kinda gets me is the whole “cramped spaces” safety net that I kept seeing for why this job or that was going to be safe. Designing a small, agile robot is not really a challenge. Add onto it that in many situations you could use a tethered drone to do the actual work that is much smaller and the AI brain can be sitting safely outside the situation. You could even plug it into power, so battery tech doesn’t need to increase. shrug I guess I just see quite a bit of very fast advances in the tech that have a worrying trajectory to me.
All great points. I guess I need to think of this topic more from the “what is possible” mindset rather than the “this is too hard” mindset to get a fair assessment of what is coming. All while still framing it in the sense of improving worker efficiency and automating human tasks piecemeal over time.
Agree that other parts of the EM spectrum could enhance the ability of MV to recognize things. Appreciate the insights – maybe I will be able to use this when I get back to tinkering with MV as a hobbyist.
Of course identifying one object is one level. For a general purpose replacement for humans ability, since that’s what the thread is focused (ahem) on, it has to identify tens of thousands of objects.
I need to rethink my opinion a bit. Not only how far general object recognition is but also how one can “cheat” to enable robotic automation.
Tasks that are more limited in scope and variability would be a lot less demanding. For a silly example, let’s say we want to automate replacing fuses in cars. We limit it to cars with fuse boxes in the engine bay and we can mark the fuse box with a visual tag the robot can detect. The layout of the fuses per vehicle model could be stored. The code on the fuse box identifies the model. The robot then used actuators to remove the cover and orients itself to the box using more markers and the rest is basically pick and place technology. That’s a smaller and easier problem to solve than “fix anything possibly wrong with a car”. A similar deal could be done for oil changes.
For general purpose MV object detection, I would have to go check but my guess is that what is possible with state of the art MV is identifying a dozen or maybe even hundreds of objects so I suppose one could do quite a bit with that to automate some jobs. MV is not to my knowledge at a level of general purpose replacement for humans. Yet. Maybe it won’t take that much longer.
In ~15 years in the hobbyist space we’ve gone from recognizing anything of a specified color under some lighting conditions to identifying several specific objects. And without a ton of processing power either. It’s pretty damn impressive progress, really. We have security cameras that can identify animals, people, and delivery boxes. I am probably selling short what MV will be able to do in 15 more years.
I don’t disagree with most of what you said. I think so far the following jobs are safe from direct AI replacement, because it is much harder to replace manual laborers.
What companies won’t realize until too late is that paying customers need jobs to pay for things. If AI causes unemployment to rise to some ungodly high, paying customers will become rare and companies will collapse in droves.
Thanks for actually rising to the challenge, it was actually fascinating to do the research to see how AI is affecting the various industries, and how deeply. I will say that I was able to find direct evidence of replacement in 7/10 of them, 1 was work that is similar and could easily be adapted (telecom line repair), one was an analysis that I think has a lot of good points (plumber), and one was genuinely all about augmenting the capabilities of workers already in place (wildlife conservation/officer).
I wholeheartedly agree. Functionally, we are going to have to institute a UBI model. It is the only way that society will be able to distribute funds properly when population outpaces jobs due to the exponential growth of populations and the rapidly shrinking landscape of jobs. The corporations are going to need to pay us one way or another.
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.
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.
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.
Had a thought that deserved a separate post. Your selection of MV tasks was rather perverse for the tasks we were discussing. Identifying a pop can is definitely something that humans can do easily because pop cans were made for us to be able to easily identify them. Level the playing field and let’s start looking for internal stress fractures in the superstructure of a 100’ tall concrete bridge. That is something that AI drones are already being designed and deployed for. The drone can easily approach the bridge with a suite of sensors that let it see deep into the superstructure and detect future failure points. Humans would struggle to do that. I have also seen things about maintenance drones that are able to crawl on the bridge using a variety of methods (usually they are designed for specific bridges) that are able to fill cracks with sealant and ablate rust using lasers, then paint the freshly cleaned metal. The benefit of replacing a workforce with AI-driven robotics is that you can purpose-build and purpose-train the tool to do exactly what you need it to do. A robot that scurries into a crawl space to run a pipe for a plumber doesn’t need to know how to do anything but recognize where it goes, what not to touch, and how much force to use when installing it. It doesn’t need to identify a pop can, it doesn’t need to draw a Rembrandt. All it needs to do is pull a pipe and weld it in place (and yes, I am oversimplifying a bit, I know that).
The other thing that kinda gets me is the whole “cramped spaces” safety net that I kept seeing for why this job or that was going to be safe. Designing a small, agile robot is not really a challenge. Add onto it that in many situations you could use a tethered drone to do the actual work that is much smaller and the AI brain can be sitting safely outside the situation. You could even plug it into power, so battery tech doesn’t need to increase. shrug I guess I just see quite a bit of very fast advances in the tech that have a worrying trajectory to me.
All great points. I guess I need to think of this topic more from the “what is possible” mindset rather than the “this is too hard” mindset to get a fair assessment of what is coming. All while still framing it in the sense of improving worker efficiency and automating human tasks piecemeal over time.
Your points on MV are not unfounded, but they are also extremely homeocentric. All of your examples rely on the visible light spectrum as well as standard “vision” as we know it. Realistically any sensor can be used to generate an image if you know what you are doing with it. Radio telescopes are a great example of this. There is also a lot of research going on in giving AI’s MV senses access to other sections of the EM spectrum ( https://www.edge-ai-vision.com/2017/10/beyond-visible-light-applications-in-computer-vision/ and https://www.technologyreview.com/2019/10/09/132696/machine-vision-has-learned-to-use-radio-waves-to-see-through-walls-and-in-darkness/ ) as well as echolocation ( https://www.imveurope.com/news/echolocation-neural-net-gives-phones-3d-vision-sound ). There are many other types of “vision” that can be used that can definitely distinguish a popcan.
Agree that other parts of the EM spectrum could enhance the ability of MV to recognize things. Appreciate the insights – maybe I will be able to use this when I get back to tinkering with MV as a hobbyist.
Of course identifying one object is one level. For a general purpose replacement for humans ability, since that’s what the thread is focused (ahem) on, it has to identify tens of thousands of objects.
I need to rethink my opinion a bit. Not only how far general object recognition is but also how one can “cheat” to enable robotic automation.
Tasks that are more limited in scope and variability would be a lot less demanding. For a silly example, let’s say we want to automate replacing fuses in cars. We limit it to cars with fuse boxes in the engine bay and we can mark the fuse box with a visual tag the robot can detect. The layout of the fuses per vehicle model could be stored. The code on the fuse box identifies the model. The robot then used actuators to remove the cover and orients itself to the box using more markers and the rest is basically pick and place technology. That’s a smaller and easier problem to solve than “fix anything possibly wrong with a car”. A similar deal could be done for oil changes.
For general purpose MV object detection, I would have to go check but my guess is that what is possible with state of the art MV is identifying a dozen or maybe even hundreds of objects so I suppose one could do quite a bit with that to automate some jobs. MV is not to my knowledge at a level of general purpose replacement for humans. Yet. Maybe it won’t take that much longer.
In ~15 years in the hobbyist space we’ve gone from recognizing anything of a specified color under some lighting conditions to identifying several specific objects. And without a ton of processing power either. It’s pretty damn impressive progress, really. We have security cameras that can identify animals, people, and delivery boxes. I am probably selling short what MV will be able to do in 15 more years.