On a production line with 1000 different operations you'd have a thousand low skill people using their eyes, brains, and fingers to develop the assembly process.
You could never get 1000 roboticists working on getting an assembly line going. I don't know how you could even find that many, but if you did manage to do it, you'd probably end up with an assembly line that would spit out 60 iphones a second.
Instead what you might have is a setup where no individual process is getting the attention it truly needs. Each step is probably just barely working because the pressure to get 1000 automation steps working means that as soon as it works even poorly you've got to move on to the next step.
This is how we deal with problems in general. If we can't solve them on their own terms, we change the terms. For example, designing an all-terrain vehicle is hard, so instead we've been beating the terrain flat, and paving it so that it stays flat, to allow a simple box with wheels to work, and we've been doing that since beginnings of recorded history.
(And speaking of paving - we're not placing random stones in the ground anymore; we're either pouring liquid, or laying stones pre-cut to standard size. Both are much easier to automate, and to some extent they already are automated.)
I can't think of a job so specific that wouldn't yield to competition from equivalent but normalized job.
> Typical problems that arose include how Apple's use of glue required precision the machinery couldn't reliably match
Aren't robots commonly used to place adhesives (even replacing welds in metal fab) exactly because they can apply it far more precisely than humans? Picturing an iPad, I'd guess the issue would have been flexibility in placing glue in 3D space, around odd angles and tight corners, and not precision.
And despite Apple's high volumes, with changing their product line each year I'm sure humans are much more flexible when it comes to building different devices, rather than a full robotics line that needs to be redesigned.
Steve Jobs would’ve flayed the lot of them till he got exactly what he wanted. Cook’s mediocrity simply shrugs and rolls on, with nothing learned at all. #PerfectlyOiledCuckooClock
If they could not detect it in the confines of a highly controlled factory assembly line (not manufacturing but assembly) then how can a car detect novelty on a cityscape or even a highway?
For self driving cars, the controls are pretty similar, even across car models. Gas, brakes, and steering. It doesn't happen that a supplier ran out of engines and a decision is made to replace the car's propulsion by jet engines, thrust vectoring, or a hovercraft. It doesn't matter too much for self driving cars to be within 0.1 inches of the center of the road, but if your electrical components are offset by 0.1 inches or there's 0.1 fluid ounces too much glue because this batch of glue is more liquid than the previous one, the electronics probably won't work in the end.
There's a limit to the R&D budget for assembling THIS year's iphone, above which it's just better to use humans.
We jumped right to the hardest problem set. Probably because it's the most sensational and easiest to get broad financial support by selling people the promise of less rush hour drain.
It's also not going to significantly improve one's choice of transportation - you either have access to rail already, or you don't - regardless of who/what drives the train. Schedules are already tuned and if it was more profitable to put more trains, it would likely happen even with human conductors.
This goes along with my experience in new cars in that the best improvements are those that enhance my ability to drive (such as a backup camera).
Using advanced AI, or even a bunch of semi-decent models to condense information, highlight things humans might miss, enrich with predictions, etc so that humans don’t have to spend as much time wading through data themselves to try and extract meaning and can instead jump straight to more informed decision making seems like a better approach to me than “lol can we make a neural net that does lawyer things?”
* = economically, because low-skilled labor is toooooo cheap
Complex automation works best when placed in the hands of skilled humans, as an amplifier of human ability. Let them use the machines to accelerate all their mundane repetitive crap, while retaining the human ability to make reasoned decisions and handle corner cases and errors intelligently.
But perhaps a more logical place to start is by automating away the penny-pinching bean counters’ and talentless middle-management chair warmers’ jobs? Dog knows they’re reliably useless at it themselves.
I readed this on HN and your comment made me remember it. https://marshallbrain.com/manna1
Personally I see successful high-level automation as much more meritocratic, in that it both serves and is directed by the same individuals: the users themselves. The worst that can happen there is that you automate yourself out of your existing job; but if you can’t think of how to parlay that win into your next then your imagination picked a funny time to fail on you now.
This article is talking about the world in 2012. The world in 2012 was radically different to the world of 2020. Humans were still the best Go players on the planet, the landscape of image recognition looked rather different and the hardware was in a completely different place.
The skills to automate this stuff are developing right now. We're basically looking at a reset of these lessons that were learned in the early 2010s. The next wave has a much stronger foundation. Computers are now, potentially, better at pattern recognition. For all we know the insurmountable is currently being surmounted.
Bottom line is, humans are cheaper than robots in the parts of the world the make your iPhone. If you ever visit the factories where this sort of thing is made its all shockingly manual, because that's what is cheapest.
Super generic title. Has it replaced all its software with humans?
Took them quite a few years to fix a keyboard on their macbook line for example...
Similarly, vertically integrating battery construction and housing into the device body, like TSLAs battery day announcement.
Between these integrations, I’m not sure there will be much remaining to automate.
Silicon Substrate for OLED: https://arstechnica.com/science/2020/03/blinkin-fast-oled-ma...
Details of TSLA's push to make batteries part of the device body. https://www.forbes.com/sites/jamesmorris/2020/09/22/tesla-re...
Seriously? Did anybody even read this garbage before publishing?
The human hand sensors/fine muscle control is insanely good.
I don't think we are remotely close building anything that general-purpose and that precise.
