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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

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

narrator 4y ago
Once robotics and computer vision gets there, there could be a lot of money in robotized regenerative agriculture.
- Loughla 4y ago
  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.
  - ghaff 4y ago
    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.
    - narrator 4y ago
      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.
      - akiselev 4y ago
        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 https://en.wikipedia.org/wiki/Moravec%27s_paradox
        amluto 4y ago
        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.
        ghaff 4y ago
        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.

narrator 4y ago

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

Loughla 4y ago
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.
- ghaff 4y ago
  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.
  - narrator 4y ago
    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.
    - akiselev 4y ago
      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 https://en.wikipedia.org/wiki/Moravec%27s_paradox
      - amluto 4y ago
        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.
        ghaff 4y ago
        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.