4 ms·

Nobody said you would use an LLM for that. It's an example of a process where "industrial inspection, in particular, [would] benefit from lower latency in excha

Nobody said you would use an LLM for that. It's an example of a process where "industrial inspection, in particular, [would] benefit from lower latency in exchange for accuracy". The point of their comment isn't that you would use an LLM to sort fruit. It was just an illustrative example.

sorenjan 8mo ago
The discussion was about fine-tuned Qwen models, not industrial inspection in general. I would also find it interesting to learn about what kind of edge AI industrial inspection task you could do with fine-tuned llms, not some handwavy answer about how sometimes latency is important in real time systems. Of course it is, so generally you don't use models with several billion parameters unless you need to.
- arcanemachiner 8mo ago
  The thread you're in broke away from the main discussion topic. Again: Nobody is using LLMs to (for example) sort fruit. But there are some industrial processes that prioritize latency over reliability.
  - nl 8mo ago
    No, we are literally trying to find a use case where using a lower accuracy LLM makes sense for a vision task. But fine - what are these industrial processes where that prioritize latency over reliability and using a LLM - as mentioned by the OP - makes sense?
    - IanCal 8mo ago
      > No, we are literally trying to find a use case where using a lower accuracy LLM makes sense for a vision task. They're reconfigurable on the fly with little technical expertise and without training data, that's really useful. Personally in projects for people I've found models have fewer unusual edge cases than traditional models, are less sensitive to minor changes in input and are easier to debug by asking them what they can see.
      - sorenjan 8mo ago
        Seems like a way to use a sledgehammer to hammer in screws, and inviting nondeterminism in important systems. Besides being way larger and more complex than what most specialized industrial processes need, they are also vulnerable to adversarial attacks. https://www.lakera.ai/blog/visual-prompt-injections https://www.lakera.ai/blog/visual-prompt-injections https://www.theverge.com/2021/3/8/22319173/openai-machine-vision-adversarial-typographic-attacka-clip-multimodal-neuron https://www.theverge.com/2021/3/8/22319173/openai-machine-vi...
        IanCal 8mo ago
        > Seems like a way to use a sledgehammer to hammer in screws The lazy analogy the other way is that developing a custom system to do these jobs is like hiring a team of experts to spend 2 years designing the perfect crosshead screwdriver that fits exactly one screw (and doesn't work if the screw starts slightly rotated) when you have a flathead one right next to you that'll work and it'll work right now. > and inviting nondeterminism in important systems. Traditional ML is just as non-deterministic. > they are also vulnerable to adversarial attacks. Typically not relevant in these kinds of cases but also this is easily a problem in many traditional ML algos. Have you worked on things like this?
        sorenjan 8mo ago
        A flathead screwdriver is not a valid analogy, because LLMs are big complicated and opaque machines. And while other ML methods are non-deterministic as well, gaussian process, decision trees or even CNNs are easier to try to make sense of than these huge black boxes. And I still haven't seen a single example of anyone actually using a finetuned Qwen in industrial inspection, which leads me to believe than nobody is actually using it for that, but some people want to use it because it's their new favorite toy. You don't need a VLM to count cells in microscopy images, or find scratches in painted parts, or estimate output from a log in a saw mill. I can see the use case for things like describing a scene from a surveillance camera, finding a car of a certain model and colour, or other tasks that demand more reasoning or description. But in those cases latency is not super important compared to getting the right output, which was the tradeoff discussed from the start of this thread. The last thing I'd want to deal with is to have a computer say something like "You're absolutely right, it was wrong of me to classify the metal debris as food".

sorenjan 8mo ago

The discussion was about fine-tuned Qwen models, not industrial inspection in general. I would also find it interesting to learn about what kind of edge AI industrial inspection task you could do with fine-tuned llms, not some handwavy answer about how sometimes latency is important in real time systems. Of course it is, so generally you don't use models with several billion parameters unless you need to.

arcanemachiner 8mo ago
The thread you're in broke away from the main discussion topic. Again: Nobody is using LLMs to (for example) sort fruit. But there are some industrial processes that prioritize latency over reliability.
- nl 8mo ago
  No, we are literally trying to find a use case where using a lower accuracy LLM makes sense for a vision task. But fine - what are these industrial processes where that prioritize latency over reliability and using a LLM - as mentioned by the OP - makes sense?
  - IanCal 8mo ago
    > No, we are literally trying to find a use case where using a lower accuracy LLM makes sense for a vision task. They're reconfigurable on the fly with little technical expertise and without training data, that's really useful. Personally in projects for people I've found models have fewer unusual edge cases than traditional models, are less sensitive to minor changes in input and are easier to debug by asking them what they can see.
    - sorenjan 8mo ago
      Seems like a way to use a sledgehammer to hammer in screws, and inviting nondeterminism in important systems. Besides being way larger and more complex than what most specialized industrial processes need, they are also vulnerable to adversarial attacks. https://www.lakera.ai/blog/visual-prompt-injections https://www.lakera.ai/blog/visual-prompt-injections https://www.theverge.com/2021/3/8/22319173/openai-machine-vision-adversarial-typographic-attacka-clip-multimodal-neuron https://www.theverge.com/2021/3/8/22319173/openai-machine-vi...
      - IanCal 8mo ago
        > Seems like a way to use a sledgehammer to hammer in screws The lazy analogy the other way is that developing a custom system to do these jobs is like hiring a team of experts to spend 2 years designing the perfect crosshead screwdriver that fits exactly one screw (and doesn't work if the screw starts slightly rotated) when you have a flathead one right next to you that'll work and it'll work right now. > and inviting nondeterminism in important systems. Traditional ML is just as non-deterministic. > they are also vulnerable to adversarial attacks. Typically not relevant in these kinds of cases but also this is easily a problem in many traditional ML algos. Have you worked on things like this?
        sorenjan 8mo ago
        A flathead screwdriver is not a valid analogy, because LLMs are big complicated and opaque machines. And while other ML methods are non-deterministic as well, gaussian process, decision trees or even CNNs are easier to try to make sense of than these huge black boxes. And I still haven't seen a single example of anyone actually using a finetuned Qwen in industrial inspection, which leads me to believe than nobody is actually using it for that, but some people want to use it because it's their new favorite toy. You don't need a VLM to count cells in microscopy images, or find scratches in painted parts, or estimate output from a log in a saw mill. I can see the use case for things like describing a scene from a surveillance camera, finding a car of a certain model and colour, or other tasks that demand more reasoning or description. But in those cases latency is not super important compared to getting the right output, which was the tradeoff discussed from the start of this thread. The last thing I'd want to deal with is to have a computer say something like "You're absolutely right, it was wrong of me to classify the metal debris as food".