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>It seems like you are not well versed in ai. Not especially. I audited a course as an undergrad, and hardly remember anything from it now, but I have a layma

>It seems like you are not well versed in ai. Not especially. I audited a course as an undergrad, and hardly remember anything from it now, but I have a layman's understanding of the basics. >There was a debate about whether "hand engineering" vs "dumb simple algo's" would get results. Dumb algo's won. There are design tasks for which "algorithms" are better suited, and there are design tasks where experienced human engineers still do far, far better. The statement "Dumb algo's won" is certainly true for some applications, but not all. >Your mentioning of MTI and STAP, and how difficult radar design is etc. etc., Now it's my turn: you clearly are not well versed in aviation or in radar principles. Not every problem can be magically solved by throwing AI at it. Radar theory is well established, and the equations are well known. Unfortunately, they are hard equations to solve: determining the location of an object using radar involves some complicated math with a lot of variables, and the only way to solve those equations is to chew your way through them. You can simplify them, but then you have to accept increased errors from the terms you throw out. Where "algorithms" come into play is tracking, and depending on how you define AI, radar engineers have been using AI since the invention of the first automated tracker. Even the very best automated trackers in existance today are not nearly as good as an experienced operator looking at raw returns. Someday that will probably change, but that day is still many years away. >...makes me think you aero guys are still in hand-engineering land. As I said before, you're making a huge mistake by lumping "you aero guys" into a single group. "Aeronautical engineering" is really "every other kind of engineering, applied to aviation." When I was in grad school, one of my buddies' thesis was pure AI: he developed a learning algorithm for choosing the optimum path for a jet to taxi around a crowded flight deck, using DGPS as the only position source. Another guy combined machine learning with CFD in an attempt to design better supersonic lifting surfaces (the results were not good, but his thesis was still a "success" in the sense that he expanded human knowledge and the general concept showed promise). There are some applications where AI is the way to go, and there are some applications where what you derisively call "hand engineering" is infinitely superior. >I will offer another example. Why did the aerospace dudes not be able to autonomously fly a helicopter. in 2004, andrew ng decided to tackle this. He completely ignored any previous work, just using a dumb algo (reinforcement learning) and laptop managed to get amazing autonomous performance out it. Why was it him (ai researcher), and not people from the field of flying. Time for a history lesson: The Navy deployed the first effective autonomous rotorcraft in the 1960s: http://en.wikipedia.org/wiki/Drone_Anti-Submarine_Helicopter http://en.wikipedia.org/wiki/Drone_Anti-Submarine_Helicopter So the "aerospace dudes" were "able to autonomously fly a helicopter" before Andrew Ng was born, and they did it using "hand engineering." Not autonomous enough for you? Firescout flew autonomously four years before Andrew Ng flew his helicotper autonomously: http://en.wikipedia.org/wiki/Firescout http://en.wikipedia.org/wiki/Firescout Firescout was also developed by "people from the field of flying."

marshallp 15y ago
There's quite a difference between firescout and ng's helicopter. The latter gives superhuman performance (there are videos on his homepage, andrew ng stanford). Anyway, i don't know what algo firescout uses, it might well be ai behind the scenes proving my general point. Your point about complicated mathematical equations lies at the root problem of you "unified engineering" guys. modern ai (machine learning) is where you give up on the assumption that you (puny human) can impart "wisdom" to your system. You simply throw a random set of equations (a neural network) that are large enough/not too large (overfitting) to capture physical reality. Getting the errors low is a matter of getting enough data and experimentally adjusting the size of your nnet. Yes, there mught be grad students and profs trying ai to solve aero problems, however, if enough resources are not devoted, they will not yield good enough results. For example, spend a billion dollars (gathering data/computation) to solve your radar problem. A billion dollars in your field is pocket change.
- lucasjung 15y ago
  >There's quite a difference between firescout and ng's helicopter. The latter gives superhuman performance (there are videos on his homepage, andrew ng stanford). You have absolutely no idea what the performance and handling characteristics of Firescout are; in fact, it is apparent that you lack the domain knowledge to understand their meaning even if they were presented to you. Nevertheless, you assert without hesitation that Andrew Ng's helicopter is superior. This is the epitome of fanboyism. >Your point about complicated mathematical equations lies at the root problem of you "unified engineering" guys. You clearly don't know what you're talking about here. Go read Skolnik's Radar Handbook, then try to tell me with a straight face that random processes are going to derive those equations for you, and somehow magically come up with a way to sidestep the basic reality that they have to be solved. >modern ai (machine learning) is where you give up on the assumption that you (puny human) can impart "wisdom" to your system. You simply throw a random set of equations (a neural network) that are large enough/not too large (overfitting) to capture physical reality. Getting the errors low is a matter of getting enough data and experimentally adjusting the size of your nnet. Yes, there mught be grad students and profs trying ai to solve aero problems, however, if enough resources are not devoted, they will not yield good enough results. For example, spend a billion dollars (gathering data/computation) to solve your radar problem. A billion dollars in your field is pocket change. In order to use a tool effectively, you have to understand both it's capabilities and it's limitations. Even though you have indicated that you are an expert on machine learning and I have admitted that I am not, it is now abundantly clear to me that you have absolutely no understanding of the limitations of machine learning. If only every problem could be solved optimally by simply throwing enough data and a big enough net at it. Unfortunately, that's not how the real world works. One problem is that machine learning algorithms often converge on local maximums that are far less optimal than is possible. The guy who worked on the taxi routes had enormous issues with this, and only after extensive tweaking was he able to come up with solutions that were on par with human path-choosing. An even bigger problem stems from the fact that a machine learning algorithm is only as good as the model it works in. I mentioned that the guy working on lifting surfaces in CFD did not get great results. His problem was that his algorithms pretty much always found the places where the CFD models diverged from reality: they would find the optimum shapes for the model they were working within, but those shapes always performed terribly in the wind tunnel because the algorithm was finding optimums at points where the model diverged significantly from reality. You can't solve this problem with "better models," because every model diverges from reality. If a model doesn't diverge from reality, it's no longer a model, it's reality. Where he really impressed his review board was when he detailed a follow-on experiment of using this phenomenon to develop better CFD models, within which human engineers would be able to come up with better designs. Finally, a billion dollars is not "pocket change" in any field. Even if someone had a spare billion dollars laying around to fund R&D for radar tracking, the opportunity costs of blowing it on a machine learning experiment, instead of using to fund experienced engineers working from proven principles, would be unacceptably high.

marshallp 15y ago

There's quite a difference between firescout and ng's helicopter. The latter gives superhuman performance (there are videos on his homepage, andrew ng stanford). Anyway, i don't know what algo firescout uses, it might well be ai behind the scenes proving my general point. Your point about complicated mathematical equations lies at the root problem of you "unified engineering" guys. modern ai (machine learning) is where you give up on the assumption that you (puny human) can impart "wisdom" to your system. You simply throw a random set of equations (a neural network) that are large enough/not too large (overfitting) to capture physical reality. Getting the errors low is a matter of getting enough data and experimentally adjusting the size of your nnet. Yes, there mught be grad students and profs trying ai to solve aero problems, however, if enough resources are not devoted, they will not yield good enough results. For example, spend a billion dollars (gathering data/computation) to solve your radar problem. A billion dollars in your field is pocket change.

lucasjung 15y ago
>There's quite a difference between firescout and ng's helicopter. The latter gives superhuman performance (there are videos on his homepage, andrew ng stanford). You have absolutely no idea what the performance and handling characteristics of Firescout are; in fact, it is apparent that you lack the domain knowledge to understand their meaning even if they were presented to you. Nevertheless, you assert without hesitation that Andrew Ng's helicopter is superior. This is the epitome of fanboyism. >Your point about complicated mathematical equations lies at the root problem of you "unified engineering" guys. You clearly don't know what you're talking about here. Go read Skolnik's Radar Handbook, then try to tell me with a straight face that random processes are going to derive those equations for you, and somehow magically come up with a way to sidestep the basic reality that they have to be solved. >modern ai (machine learning) is where you give up on the assumption that you (puny human) can impart "wisdom" to your system. You simply throw a random set of equations (a neural network) that are large enough/not too large (overfitting) to capture physical reality. Getting the errors low is a matter of getting enough data and experimentally adjusting the size of your nnet. Yes, there mught be grad students and profs trying ai to solve aero problems, however, if enough resources are not devoted, they will not yield good enough results. For example, spend a billion dollars (gathering data/computation) to solve your radar problem. A billion dollars in your field is pocket change. In order to use a tool effectively, you have to understand both it's capabilities and it's limitations. Even though you have indicated that you are an expert on machine learning and I have admitted that I am not, it is now abundantly clear to me that you have absolutely no understanding of the limitations of machine learning. If only every problem could be solved optimally by simply throwing enough data and a big enough net at it. Unfortunately, that's not how the real world works. One problem is that machine learning algorithms often converge on local maximums that are far less optimal than is possible. The guy who worked on the taxi routes had enormous issues with this, and only after extensive tweaking was he able to come up with solutions that were on par with human path-choosing. An even bigger problem stems from the fact that a machine learning algorithm is only as good as the model it works in. I mentioned that the guy working on lifting surfaces in CFD did not get great results. His problem was that his algorithms pretty much always found the places where the CFD models diverged from reality: they would find the optimum shapes for the model they were working within, but those shapes always performed terribly in the wind tunnel because the algorithm was finding optimums at points where the model diverged significantly from reality. You can't solve this problem with "better models," because every model diverges from reality. If a model doesn't diverge from reality, it's no longer a model, it's reality. Where he really impressed his review board was when he detailed a follow-on experiment of using this phenomenon to develop better CFD models, within which human engineers would be able to come up with better designs. Finally, a billion dollars is not "pocket change" in any field. Even if someone had a spare billion dollars laying around to fund R&D for radar tracking, the opportunity costs of blowing it on a machine learning experiment, instead of using to fund experienced engineers working from proven principles, would be unacceptably high.