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Unfortunately, until we really need to push high scale manufacturing back to the states, it’s not changing anything for US manufacturing. I worked for a company that took almost a decade just to change from devicenet to ethercat, predictive analytics took 5. Any sort of “smart” system just doesn’t have a huge momentum unless we’re producing items at China rate and need to maintain cost low.

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Sort of agree but a couple counterexamples from my machine vision career:

- Agriculture and food processing, which cannot be offshored as easily, requires very challenging machine vision solutions. Dirty environment, unpredictable lighting, unpredictable object appearance.

- Proto and small scale high tech manufacturing, pre-offshoring or sensitive IP, requires machine vision solutions that are both sophisticated and quickly adaptable

Once robotics and computer vision gets there, there could be a lot of money in robotized regenerative agriculture.

How is this wishlist related to anything that is being talked about in this thread? I'm very confused by what you're adding to the conversation.

I also wish robots would do the menial labor that I do not enjoy and would take care of all of my basic needs.

But this is an article about basic computer vision beginning to impact basic manufacturing. What you're talking about is decades in the future if ever. I'm very confused.

Edit: The OP originally talked about an agricultural robot that could charge itself, do all the home chores, and fix things around the house. Now it's just one sentence.

A general-purpose robotic handyman for consumers is many many decades away (at least). And if such a thing did exist it would have massive massive implications for the labor market--both on its own and because of the implications for all the other things that AI could do were such a robot possible.

Computer vision in a very constrained environment is much much different and often isn't even suitable for many "simple" tasks that aren't constrained quite enough.

What if there was some sort of LLM like breakthrough? I could see someone doing a technique where they track all movements of a person going throug their day with a fine grained bodysuit. They could then use that as tokens for generative input that could give a humanoid robot intuition about how to move around and perform tasks that would match an LLM's ability to respond to arbitrary questions.

You'd walk face first right into Moravec's Paradox [1] which observes that higher order intelligence is far easier than basic locomotion & cognition. Probably because the former has only been evolving for millions of years in humans while the latter has been evolving in all animals for hundreds of millions of years.

We can't produce an electromechanical device that is capable of the kind of fine motor control 99% of animals are capable of, let alone doing it on an industrial scale. We're not even at the "promising proof of concept" stage and what use is more advanced software when we're not even close with the hardware.

[1] https://en.wikipedia.org/wiki/Moravec%27s_paradox

I suspect this is related to a serious upcoming bottleneck in AI. A skilled human can do all manner of physical handyman-style tasks, and I can imagine AI tackling a lot of the cognitive parts: reading the manual, figuring out what generally needs to be done, reading industry standards, etc. But a human has also trained themselves about how a screwdriver and a saw actually behave, how to get all the pieces to stay together while doing a job, and a bunch of other physical things work, and can real the manual, etc with this context. AI doesn’t have access to the physical part, at least for the time being.

This runs right into a lot of cognitive science research and debates. Which have not really resolved a lot of things related to AI historically and have been pretty much on the sidelines as GPU-driven machine learning has done some impressive things (and a lot of parlor tricks).

One of the ideas around cognition is that a lot of what we regard as intelligence in the physical domain, including intelligence below human levels, involves creating physical models about how the world works, which AIs are literally not able to do at all. You can instruct them in various ways, e.g. Boston Dynamics, but they have no way to internalize and actually understand novel physical world situations.

Some very smart people suspect that ML is a very powerful technique but that "better ML" only gets you so far.

> it’s not changing anything for US manufacturing.

I don't think this is true. I work for a US company producing industrial equipment based heavily on machine vision. Our products (along with those of our competitors) have changed the entire industry we support, for the better.

Ours is only one specific part of the manufacturing space, but I fully expect the impact to spread to other parts as well.

Does the industrial equipment enable manufacturers to eliminate human labor in the production process or is it more of a way to replace existing machines with a more reliable, performant etc. solution? If you want to share this info.

It increases the efficiency (that is, reduces waste) of the use of raw materials in production. In some circumstances, it probably does replace a small number of human workers, but that's not its main effect and isn't the source of the savings our customers get from it. It's mostly doing a job that wasn't being done before.

From what I understand, there are increases in efficiency due (in part) to the ability of machines to run at all hours, helping manufacturers to work with delayed and unpredictable supply chains. Also due to reducing tiresome, back-breaking manual labor.

High valued assemblies tend to be manufactured in the USA. Defect detection early and often prevents waiting until completion to scrap them (much better to scrap when you only have $10,000 in subassemblies and labor into it than then when you have $50,000). If that assembly is a bottleneck you are also reducing the impact to your larger production schedule (scraping it one week into assembly versus 4). Computer vision is hugely important for this. Computer vision capturing each stage also greatly helps to quickly isolates what is introducing the situation resulting in scrap. Instead of having scrap meetings trying to determine why a completed assembly is failing you identify the failure point as close to when it occurs as possible.

Being able to identify molds reaching end of life prior to parts failing QA for being out of tolerance is also huge for American manufacturing.

Where it's way less important is when you are spitting out eraser tips or other 'high scale' manufacturing.

Is this actually true, or is it just hysteria from someone unfamiliar with manufacturing? Because I live in New Zealand, which is on a similar deindustrialization path to America, but with obviously much less manufacturing in the first place. But we nonetheless have a burgeoning manufacturing robotics industry for what we still have - wood processing, pulp and paper, agriculture, etc.

In what sense do you mean deindustrialization? It appears from various reports that NZ manufacturing output it at a record high. Do you mean as a % of GDP?

In the U.S. the number of people employed in manufacturing is lower than ever but the value of manufacturing has been steady at 12% of GDP since World War 2 ended.

I've worked for an industrial vision equipment company for 21 years and North America has ALWAYS been our strongest market segment.

I guess it's cheaper to hire workers in China, but also cheaper to have automated machines running in China and have the Chinese build those machines.

definitely safer to have industrial automation systems running as well!

I can imagine that China also has massive infrastructure and a manufacturing environment built up over the last years that may become increasingly hard to replicate in the US. I bet there some "critical mass" for high-volume manufacturing that's needed, if you don't count subsidies. Even if it's all robots, you still need suppliers etc.

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