5 ms·

Because ML (and NNs ofc) is definitely a one-size-fits-all solution to interdisciplinary problems..

gfodor 5y ago
What happened to you that you decided replying to this with a emotional strawman was worth your time and energy? It's a particularly dumb strawman too because we already know AI can generate solutions to mechanical engineering problems that humans normally would not. https://medium.com/intuitionmachine/the-alien-look-of-deep-learning-generative-design-5c5f871f7d10 https://medium.com/intuitionmachine/the-alien-look-of-deep-l...
- antegamisou 5y ago
  It's generally a bad idea to invest too much in ML methods for physical world problems, especially considering it a panacea when their mathematical foundations are still poorly understood. The cost may be only computational when it comes to areas like Image Processing/NLP, however it's nowhere near the same for things like AVs (safety), Engineering Design problems (materials) etc. And this is because real world imposes real hard constraints, to the point that it'd be unfair to expect similar success to CS-related disciplines here from ML methods. This is no different for manufacturing problems. Excluding the absurd PoCs/artworks, most of the actual structures in the article you've linked are impossible to mass-manufacture without 3D printing, which is still limited to precisely printings parts with unsuitable materials for their target application. Keep in my mind that I was mainly referring to applying emerging trendy methods for which mathematical guarantees have not yet been established. Genetic Algorithms, for example, have been able to come up with successful antenna design optimizations like the one in the article for almost three decades.
  - gfodor 5y ago
    “Panacea” <— your strawman I noticed a lot of your replies are similar in talking down to people. Weird!
  - cyber_kinetist 5y ago
    I generally agree with your line of thought (CS is too accustomed to toying around with imaginary models/rules and not real-world physical ones), but I don't get the connection between mathematically sound models and manufacturing problems. Are even Genetic Algorithms (a bunch of metaheuristics) really mathematically that robust? I thought GAs were mainly discovered and developed purely empirically, and there's not that much theoretical guarantees behind it even as of today. The reason it's being used in the context of engineering is that it seems to solve non-convex or combinatorial problems really well compared to previous approaches, and that's it. And if GANs or Deep RL can explore solution spaces more effectively than GA algorithms, then why not? Many engineering problems can be reduced to optimization problems, and if a certain toolset can be used to effectively solve these optimization problems, then it should be applied pragmatically. You could still question if ML brings anything new to the table ("isn't it just curve fitting in higher dimensions?"), but I think there's some value in using data-driven ML toolsets judiciously. For example, a lot of engineering tasks are repetitive but vary in very slight deviations (like screwing things to holes), and for these cases statistical models might help in bringing efficiency and safety. Of course the system still cannot handle real catastrophic deviations from the norm (ex. small earthquake happens and the robot is shaking) - but at that point human intervention should definitely happen. A truly intelligent AI would handle even the exceptional cases with some degree of pragmatic decision-making, but that's not the goal we're chasing for here. I still agree with the sentiment that there needs more work to make ML methods safe though. This is in two grounds: one is to make it easier for the programmers to add domain-specific hard constraints to the model they're learning (enforce the output of the model always adhere to certain rules), and the other is to make the ML system report to the human operators for manual intervention when any abnormalities to the input and the output are detected. It's a bit disappointing that much of "safe" ML has focused on ethical issues mainly arisen from Silicon Valley (like gender/race issues in models: duh, your model will probably have the same bias as your data! The conception of using ML for solving social problems is fucked up in the first place...). The real focus should be the ability to steer, control, and constrain ML models as human operators would like, and I think the neglecting of this is the reason why ML has largely failed to be incorporated in industrial domains outside of SV as of today.
    - antegamisou 5y ago

What happened to you that you decided replying to this with a emotional strawman was worth your time and energy? It's a particularly dumb strawman too because we already know AI can generate solutions to mechanical engineering problems that humans normally would not. https://medium.com/intuitionmachine/the-alien-look-of-deep-learning-generative-design-5c5f871f7d10 https://medium.com/intuitionmachine/the-alien-look-of-deep-l...

antegamisou 5y ago
It's generally a bad idea to invest too much in ML methods for physical world problems, especially considering it a panacea when their mathematical foundations are still poorly understood. The cost may be only computational when it comes to areas like Image Processing/NLP, however it's nowhere near the same for things like AVs (safety), Engineering Design problems (materials) etc. And this is because real world imposes real hard constraints, to the point that it'd be unfair to expect similar success to CS-related disciplines here from ML methods. This is no different for manufacturing problems. Excluding the absurd PoCs/artworks, most of the actual structures in the article you've linked are impossible to mass-manufacture without 3D printing, which is still limited to precisely printings parts with unsuitable materials for their target application. Keep in my mind that I was mainly referring to applying emerging trendy methods for which mathematical guarantees have not yet been established. Genetic Algorithms, for example, have been able to come up with successful antenna design optimizations like the one in the article for almost three decades.
- gfodor 5y ago
  “Panacea” <— your strawman I noticed a lot of your replies are similar in talking down to people. Weird!
- cyber_kinetist 5y ago
  I generally agree with your line of thought (CS is too accustomed to toying around with imaginary models/rules and not real-world physical ones), but I don't get the connection between mathematically sound models and manufacturing problems. Are even Genetic Algorithms (a bunch of metaheuristics) really mathematically that robust? I thought GAs were mainly discovered and developed purely empirically, and there's not that much theoretical guarantees behind it even as of today. The reason it's being used in the context of engineering is that it seems to solve non-convex or combinatorial problems really well compared to previous approaches, and that's it. And if GANs or Deep RL can explore solution spaces more effectively than GA algorithms, then why not? Many engineering problems can be reduced to optimization problems, and if a certain toolset can be used to effectively solve these optimization problems, then it should be applied pragmatically. You could still question if ML brings anything new to the table ("isn't it just curve fitting in higher dimensions?"), but I think there's some value in using data-driven ML toolsets judiciously. For example, a lot of engineering tasks are repetitive but vary in very slight deviations (like screwing things to holes), and for these cases statistical models might help in bringing efficiency and safety. Of course the system still cannot handle real catastrophic deviations from the norm (ex. small earthquake happens and the robot is shaking) - but at that point human intervention should definitely happen. A truly intelligent AI would handle even the exceptional cases with some degree of pragmatic decision-making, but that's not the goal we're chasing for here. I still agree with the sentiment that there needs more work to make ML methods safe though. This is in two grounds: one is to make it easier for the programmers to add domain-specific hard constraints to the model they're learning (enforce the output of the model always adhere to certain rules), and the other is to make the ML system report to the human operators for manual intervention when any abnormalities to the input and the output are detected. It's a bit disappointing that much of "safe" ML has focused on ethical issues mainly arisen from Silicon Valley (like gender/race issues in models: duh, your model will probably have the same bias as your data! The conception of using ML for solving social problems is fucked up in the first place...). The real focus should be the ability to steer, control, and constrain ML models as human operators would like, and I think the neglecting of this is the reason why ML has largely failed to be incorporated in industrial domains outside of SV as of today.
  - antegamisou 5y ago