[lucasjung]

I hardly think it's pessimistic to say, "this problem is really hard, and it's going to take years to solve." I'm confident that they will be solved, which some of my peers might even consider a naively optimistic attitude.

>The question is - can humans do this? If yes, computers can do it eventually.

This logic is deeply, deeply flawed. I happen to believe that computers can eventually perform the tasks under discussion, but "humans can do it" is not one of the reasons why I believe that.

>So really, your problems are a question of data collection. It is not a technically difficult problem.

When it comes to tracking airborne targets with airborne radar, data collection actually is a technically difficult problem. The combination of waveform, antenna design, transmitter design, receiver design, tracker design, etc. present a set of engineering tradeoffs in effective range, range resolution, azimuth resolution, weight, size, flase positive and false negative rates on radar returns, and other performance characteristics. Even the very best airborne radars provide data which is limited, especially in terms of accuracy and precision.

>By the way, one of the problems you have in aerospace is, is that you're control theory heavy rather than pro-ai, which means you end up not being able to solve the difficult problems.

A little more about my background: my BS is in EE, with a specialization in microcomputer interfacing (I took a lot of CS classes). In grad school, my stability and control prof had actually done some pioneering work in incorporating non-linear logic into stability and control systems (don't try this at home, kids). In addition to stability and control, my other focus for my MS was avionics. The prof who taught most of my avionics classes was actually from the CS department (his undergraduate background was EE, with a specialization in radar). One of the things that kind of surprised me about aero, having come from EE, was how broad the discipline is. Before going back to school for aero, I thought that getting a degree in aeronautical engineering would be primarily about aerodynamics, with a smattering of other stuff. Instead, I discovered that everyone gets a little bit of everything (aerodynamics, propulsion, structures, stability and control, avionics), and then specializes in one or two particular areas. By the PhD level, people who have specialized in areas other than aerodynamics have largely forgotten most of what they learned about it as undergrads. It's an incredibly heterogeneous field: propulsion and structures guys have more in common with MechEs than with other AeroEs; avionics and stability and control guys have more in common with EEs than with other AeroEs; etc. So to characterize the discipline, or any individual within it, as "control theory heavy rather than pro-ai," displays a deep misunderstanding of the character of the community. I guarantee you that there are plenty of AI experts working in the aero field.

>Also, the eu 80s project ended around early 90s, and the grandle challenge win was only 15 years later, not 30. Had the challenge been tried 5 years earlier, it would have worked. The algorithms and hardware was already sufficient.

This just reinforces my broader point: success came not as a result of some innovative genius applying a novel new approach but rather because the technology had matured--over the course of several years--to the point where success had become not only possible but likely. Radar tracking is currently experiencing big advances for that same reason. The theory behind Space-Time Adaptive Processing (STAP) has been around for decades, but the available technology has not been up to the task of implementing it effectively. In the past we've resorted to less effective tracking methods such as MTI, but in the last decade or so the technology has finally made STAP reasonable to implement.

[marshallp]

It seems like you are not well versed in ai. There was a debate about whether "hand engineering" vs "dumb simple algo's" would get results. Dumb algo's won. Your mentioning of MTI and STAP, and how difficult radar design is etc. etc., makes me think you aero guys are still in hand-engineering land.

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.

[lucasjung]

>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

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

Firescout was also developed by "people from the field of flying."

[marshallp]

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]

>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]

It is quite clear that you are a person who likes to revel in appeal to authority arguments and casually throw off insults. Throwing a textbook, or your phd buddy's anecdotes in my face does not negate what i say.

Of course, dr andrew ng, head of the stanford ai lab is pussying around with his autonomous helicopter, after all problems were apparently solved by your defence contractor buddies in the 60s. The fact that helicopter pilots still exist is because society is too rich and we need to lighten our wallets. There, i mirrored your appeal to authority argument.

Mirroring your insults, it's clear you don't know what you're talking about. The fact that you've somehow been granted a doctorate further confirms my suspicions about the quality of education these days. The simplicity/non-pioneering-ness of your phd buddies's theses' is further confirmation of that. And the fact that you have been assigned to evaluate important technologies in your sector says a lot about the general competence level in it.

Of course searching can result in local minima. -That Exactly- is why you have to keep to keep running computers and getting more data. You can keep chanting to yourself - i am clever, i am clever, i write equations - and tell everyone the problem is difficult, years out from solution - or you can switch on the damn computers and let them find your answer.

If a billion dollars is too much for a system that can finally allow you to have autonomous planes, that you hope somehow your big brains will solve it, despite not having done so for a few decades, means that you, or your industry, does not have a clear grasp of the meaning of the term opportunity cost.

[lucasjung]

>It is quite clear that you are a person who likes to revel in appeal to authority arguments and casually throw off insults. Throwing a textbook, or your phd buddy's anecdotes in my face does not negate what i say.

I did not cite the textbook as an appeal to authority: I cited it because you repeatedly demonstrated that you don't understand what I'm saying, and kept making ludicrous arguments as a result, and reading that book (or a similar one) would be the only way for you to gain the necessary domain knowledge in order to say something meaningful on this subject.

Similarly, I raised the issues of my peer's thesis work not as an "appeal to authority," but as a concrete example of the limitations of machine learning as an engineering tool.

You, on the other hand, used Andrew Ng's repeatedly as an appeal to authority. The worst part is that your primary example was factually incorrect: you initially stated that he was some kind of wunder-kind who was able to easily solve a problem that had supposedly been impossible for regular aero engineers to solve; when I pointed out that regular aero engineers had, in fact, solved the problem two decades before his birth, you responded with the absurd claim that his work was somehow superior, despite a complete lack of evidence to support that position.

Moreover, saying, "you do not know what you are talking about on this subject" is not an insult. I tried saying it more subtly at first, with attempts to fill some of the gaps in your domain knowledge, and yet you persisted in making arguments based on terribly insufficient knowledge of the subject under discussion, so I came out and said it explicitly. When I did so, I even provided a text you could read in order to correct your ignorance, but you chose to reject that as "appeal to authority."

>The fact that you've somehow been granted a doctorate further confirms my suspicions about the quality of education these days.

I never claimed to have a PhD. I have clearly stated that I have a MS in Aero.

>The simplicity/non-pioneering-ness of your phd buddies's theses' is further confirmation of that.

Just as you claimed that Andrew Ng's helicopter was somehow superior to other autonomous helicopters, even though you know nothing about those other helicopters, you now claim that the graduate thesis of two complete strangers are "simple" and "non-pioneering" based on a few sentences I wrote. Throughout this conversation, you have displayed this habit of reaching unreasonable conclusions based on insufficient evidence. Your arguments would be much more plausible of you would get rid of this habit.

>Of course searching can result in local minima. -That Exactly- is why you have to keep to keep running computers and getting more data. You can keep chanting to yourself - i am clever, i am clever, i write equations - and tell everyone the problem is difficult, years out from solution - or you can switch on the damn computers and let them find your answer.

This sums up the fundamental problem with your views. I have stated repeatedly that machine learning has its uses, but that it also has its limits, and that many aspects of engineering and design are still best conducted by human beings. I have given several examples to demonstrate this. You have this inexplicable faith that any problem can be solved just by throwing enough data and computers at it. It would be wonderful if only all engineering problems were that easy to solve. Unfortunately, it's just not true. If it were true, people would be disrupting the industry en masse by having computers design superior products faster and cheaper than human engineers can. You even add a touch of "No True Scotsman" to your reasoning: if you don't get magical results from your machine learning, it must be because you're doing it wrong: not enough data, or not enough computers, or you didn't spend enough time tweaking it; just throw more time and money at it, and then you'll get the answer.