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I would add "AI" produces results that are hard to understand and work with. When you do "traditional coding" you are getting results that are easier to work w

I would add "AI" produces results that are hard to understand and work with. When you do "traditional coding" you are getting results that are easier to work with because you understand where they came form. Then it is easier to modify the model and understand the outcome. Your background is robotics. As a robotics example, training AI model to keep bipedal robot upright might be possible but having proper control model that came from control theory, identifying the system, etc. will mean you can actually calculate the envelope of the model, the limitations, etc. and that the model is not likely to surprise you in catastrophic ways. It is a case where AI would be a poor replacement for traditional modelling.

astrophysician 6y ago
I mean, part of the problem is that no one can seem to agree about what "AI" even is anymore; you described a control theory model that "calculate[s] the envelope" which I'm interpreting to mean some sort of confidence interval/posterior, but to me that's exactly what you should be doing whenever possible in an "AI" model, as in, control theory (it sounds like with Gaussian Processes or something) is the right "AI" approach to that problem. To me, linear/logistic regression is AI, as are neural nets, as are pretty much any statistical model. These days "AI" seems to be roughly interpreted as "whatever a DS person deems as a solution to our business problem" at least in my very limited experience. It's all a bit silly since AI has come to mean so many different things that may not fit the traditional definition of AI from e.g. the 90's, but as long as people aren't pidgeon-holeing themselves into particular methods, I'm happy to go along with slapping an "AI" label on pretty much anything.
- sobellian 6y ago
  I took envelope to mean the operating conditions under which the control system is stable. For many control systems this isn't really a confidence interval except insofar as the parameters of the control system are imperfectly known. For as long as I can remember, AI has been a synonym for "hard CS problem" or even just "hard math problem." It has more use as a marketing term than as a technical term with precise meaning.
  - lmilcin 6y ago
    "I took envelope to mean the operating conditions under which the control system is stable." Yes, that's exactly what I meant. I kind of borrowed the term from aviation where this is used to describe conditions under which the behavior of the aircraft is known and predictable. Doesn't mean it is not possible to fly outside the envelope, you just don't get any guarantees.
  - astrophysician 6y ago
    > I took envelope to mean the operating conditions under which the control system is stable. For many control systems this isn't really a confidence interval except insofar as the parameters of the control system are imperfectly known. Ah gotcha, totally agree. > It has more use as a marketing term than as a technical term with precise meaning. yea exactly; not a problem for me that this is the case (buzzwords are just a fact of life), but really what should be happening underneath any "AI" solution is finding and implementing the right solution to the problem. If the right solution is classical control theory or linear models, but the business leaders insist on deep learning because to them that's what AI means, that's where there's a problem.
  - deleted 6y ago
    [deleted]
- numpad0 6y ago
  I’m confident* that “AI” these days means DNN that takes n-dim input in n>2. Anything else is “contemporary“ or “conventional” maths or theories. * Read “not at all”
  - ganstyles 6y ago
    That's right. ML and AI are interchangeable at this point and both mean basically what you said. Though not necessarily a DNN per se. But that's the idea. Academically we mostly specify AGI to mean things more advanced or speculative.
- fatbird 6y ago
  I think the point GP was trying to make was that robot control model is likely a process control model, which is an area of math that's already well-explored and involves no AI. Single and multi-variable control systems[0] get into some advanced math but are not obviously mappable to machine learning models--they're much more like heavy calculus. [0] https://www.controleng.com/articles/exploring-the-basic-concepts-of-multivariable-control/ https://www.controleng.com/articles/exploring-the-basic-conc...
NalNezumi 6y ago
>I would add "AI" produces results that are hard to understand and work with. Sorry but no, this rhetoric is often used as an easy escape of responsibility but I don't buy it. Sure, if you're talking about the neural nets used in Google translate or YouTube recommendation system, or any big tech company domain of problem that is based on vast amount of data with extreme size of state space and variability - fine. But we are not talking about interpretability of the result but the reliability of it, and in robotics domain you rarely have to tackle that size of state space, but rather a highly noisy one. Let's take your example of low level control of bipedal robot. At whatever stage of control/manipulation you're talking about, you always have to consider that the input will have some noise, and how to handle it. This is the reliability; you know how to(or how much) of the input noise you can handle, and how much you will leave (or amplify) to the rest of the system. Based on this you also know the limit of the system, so you can also get an indication of failure. You can make AI/Neural Network to consider the noise and also give an indication of failure, but you have to think about that from the data gathering step! Most control algorithms have this: "what input noise can I handle" question answered; The same requirement should be set to the data. I assume you thought about Reinforcement Learning of sorts when you thought about control, but what most people don't realize is that those algorithms works only because you can use high precision sensor that reduce these noise level (combined with sensor fusion) to negligible level. But now you're relying on high precision, extremely expensive sensors. They almost always fail when you get out of the lab and have to suddenly consider cost. (and at this stage traditional control algorithm creeps back) Let's take a real example. On my original comment I wrote "work in lab at 1pm" and I weren't sarcastic. Apparently not only did the AI solution (in Computer Vision) I were given only work in the lab, but at the exact lightning condition under high noon at the lab, with all the ceiling lights on all the time. We are now not talking about interpretability of the AI, but the reliability; This is easy to identify beforehand. You can also mitigate this (the lightning noise) by including it in the model (data). This should have been glaringly obvious if the person actually knew his toolset rather than just retraining a existing model using tensorflow magic.
- ssivark 6y ago
  The problem is that a lot of AI folks bury their heads under the sand in the name of “PAC learning” and claim that you’re unfair in asking for out-of-sample generalization. They are loathe to accept that the popular algorithms are overfitting. I’ve had this debate with numerous NN/RL researchers, and somehow it always comes to this impasse.
mywittyname 6y ago
I used to go with decision trees where possible for exactly this reason. It's easier to explain than a solution that uses PCA, but has roughly the same results. They are probably not what most people would call ML, but they are simple and solve a number of common problems.
- mrosett 6y ago
  Single decision trees? Or forests?
  - mywittyname 6y ago
    In most of my work, I could get away with just one big decision tree. I was analyzing user traffic patterns, which has a limited set of metrics associated with it (and lots of boolean values). I wrote a tool that would go through and find "representative examples" of sessions for each tree path, then I could sit down with the UX designers and show them exactly what the user was doing. This was helpful as that group was the most vocal critics of our work, mainly because the data we provided began to drive UX design from senior management, and they weren't happy about it. But when I could sit down and show them exactly how the user was spinning their wheels, they were more likely to buy into changes (and offer effective solutions). Plus, I could inform the UX team which design detail was the primary driver for some event we were testing for. An example would be if the user was applying more than three filters, they were less likely to complete their goal. Thus, the UX team was able to devise a couple of reworks to the filtering mechanisms, which improved customer feedback. This tactic would probably be much more difficult if we used NN to solve the same problem.
- Der_Einzige 6y ago
  Uhh, PCA is reversable so you can explain the solution of any PCA based transformation nicely, unless you have a different black-box somewhere in your pipeline. If you're in sklearn, the method you want to use is the "inverse transform"
  - nocitrek 6y ago
WWLink 6y ago
In other words: Oh dear my fuzzy PID has gone sentient! lol. :)

astrophysician 6y ago

I mean, part of the problem is that no one can seem to agree about what "AI" even is anymore; you described a control theory model that "calculate[s] the envelope" which I'm interpreting to mean some sort of confidence interval/posterior, but to me that's exactly what you should be doing whenever possible in an "AI" model, as in, control theory (it sounds like with Gaussian Processes or something) is the right "AI" approach to that problem. To me, linear/logistic regression is AI, as are neural nets, as are pretty much any statistical model. These days "AI" seems to be roughly interpreted as "whatever a DS person deems as a solution to our business problem" at least in my very limited experience. It's all a bit silly since AI has come to mean so many different things that may not fit the traditional definition of AI from e.g. the 90's, but as long as people aren't pidgeon-holeing themselves into particular methods, I'm happy to go along with slapping an "AI" label on pretty much anything.

sobellian 6y ago
I took envelope to mean the operating conditions under which the control system is stable. For many control systems this isn't really a confidence interval except insofar as the parameters of the control system are imperfectly known. For as long as I can remember, AI has been a synonym for "hard CS problem" or even just "hard math problem." It has more use as a marketing term than as a technical term with precise meaning.
- lmilcin 6y ago
  "I took envelope to mean the operating conditions under which the control system is stable." Yes, that's exactly what I meant. I kind of borrowed the term from aviation where this is used to describe conditions under which the behavior of the aircraft is known and predictable. Doesn't mean it is not possible to fly outside the envelope, you just don't get any guarantees.
- astrophysician 6y ago
  > I took envelope to mean the operating conditions under which the control system is stable. For many control systems this isn't really a confidence interval except insofar as the parameters of the control system are imperfectly known. Ah gotcha, totally agree. > It has more use as a marketing term than as a technical term with precise meaning. yea exactly; not a problem for me that this is the case (buzzwords are just a fact of life), but really what should be happening underneath any "AI" solution is finding and implementing the right solution to the problem. If the right solution is classical control theory or linear models, but the business leaders insist on deep learning because to them that's what AI means, that's where there's a problem.
- deleted 6y ago
  [deleted]
numpad0 6y ago
I’m confident* that “AI” these days means DNN that takes n-dim input in n>2. Anything else is “contemporary“ or “conventional” maths or theories. * Read “not at all”
- ganstyles 6y ago
  That's right. ML and AI are interchangeable at this point and both mean basically what you said. Though not necessarily a DNN per se. But that's the idea. Academically we mostly specify AGI to mean things more advanced or speculative.
fatbird 6y ago
I think the point GP was trying to make was that robot control model is likely a process control model, which is an area of math that's already well-explored and involves no AI. Single and multi-variable control systems[0] get into some advanced math but are not obviously mappable to machine learning models--they're much more like heavy calculus. [0] https://www.controleng.com/articles/exploring-the-basic-concepts-of-multivariable-control/ https://www.controleng.com/articles/exploring-the-basic-conc...

NalNezumi 6y ago

>I would add "AI" produces results that are hard to understand and work with. Sorry but no, this rhetoric is often used as an easy escape of responsibility but I don't buy it. Sure, if you're talking about the neural nets used in Google translate or YouTube recommendation system, or any big tech company domain of problem that is based on vast amount of data with extreme size of state space and variability - fine. But we are not talking about interpretability of the result but the reliability of it, and in robotics domain you rarely have to tackle that size of state space, but rather a highly noisy one. Let's take your example of low level control of bipedal robot. At whatever stage of control/manipulation you're talking about, you always have to consider that the input will have some noise, and how to handle it. This is the reliability; you know how to(or how much) of the input noise you can handle, and how much you will leave (or amplify) to the rest of the system. Based on this you also know the limit of the system, so you can also get an indication of failure. You can make AI/Neural Network to consider the noise and also give an indication of failure, but you have to think about that from the data gathering step! Most control algorithms have this: "what input noise can I handle" question answered; The same requirement should be set to the data. I assume you thought about Reinforcement Learning of sorts when you thought about control, but what most people don't realize is that those algorithms works only because you can use high precision sensor that reduce these noise level (combined with sensor fusion) to negligible level. But now you're relying on high precision, extremely expensive sensors. They almost always fail when you get out of the lab and have to suddenly consider cost. (and at this stage traditional control algorithm creeps back) Let's take a real example. On my original comment I wrote "work in lab at 1pm" and I weren't sarcastic. Apparently not only did the AI solution (in Computer Vision) I were given only work in the lab, but at the exact lightning condition under high noon at the lab, with all the ceiling lights on all the time. We are now not talking about interpretability of the AI, but the reliability; This is easy to identify beforehand. You can also mitigate this (the lightning noise) by including it in the model (data). This should have been glaringly obvious if the person actually knew his toolset rather than just retraining a existing model using tensorflow magic.

ssivark 6y ago
The problem is that a lot of AI folks bury their heads under the sand in the name of “PAC learning” and claim that you’re unfair in asking for out-of-sample generalization. They are loathe to accept that the popular algorithms are overfitting. I’ve had this debate with numerous NN/RL researchers, and somehow it always comes to this impasse.

mywittyname 6y ago

I used to go with decision trees where possible for exactly this reason. It's easier to explain than a solution that uses PCA, but has roughly the same results. They are probably not what most people would call ML, but they are simple and solve a number of common problems.

mrosett 6y ago
Single decision trees? Or forests?
- mywittyname 6y ago
  In most of my work, I could get away with just one big decision tree. I was analyzing user traffic patterns, which has a limited set of metrics associated with it (and lots of boolean values). I wrote a tool that would go through and find "representative examples" of sessions for each tree path, then I could sit down with the UX designers and show them exactly what the user was doing. This was helpful as that group was the most vocal critics of our work, mainly because the data we provided began to drive UX design from senior management, and they weren't happy about it. But when I could sit down and show them exactly how the user was spinning their wheels, they were more likely to buy into changes (and offer effective solutions). Plus, I could inform the UX team which design detail was the primary driver for some event we were testing for. An example would be if the user was applying more than three filters, they were less likely to complete their goal. Thus, the UX team was able to devise a couple of reworks to the filtering mechanisms, which improved customer feedback. This tactic would probably be much more difficult if we used NN to solve the same problem.
Der_Einzige 6y ago
Uhh, PCA is reversable so you can explain the solution of any PCA based transformation nicely, unless you have a different black-box somewhere in your pipeline. If you're in sklearn, the method you want to use is the "inverse transform"
- nocitrek 6y ago

WWLink 6y ago

In other words: Oh dear my fuzzy PID has gone sentient! lol. :)