| >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. |
Yes, you are correct, I have come to a viewpoint that all problems can be solved by throwing enough computers and data at it. This was informed by arguments in ai. See for example, jurgen schmidhuber's website, or genetic programming at john koza's website.
I agree, many problems are in a sense "easy", and human's can solve them. However, my belief is that those are the problems that have already been solved. The difficult problems, the ones that have not yet been solved, might well be too difficult for humans to comprehend. Computation is cheap enough now that it should be the default first step to try to brute force a solution. Even in high school, teach students how to describe problems as an optimization. Don't bother teaching them equation solving. Analytical solutions are sometimes needed, let that be a specialization for advanced undergraduates or even graduate school.
This is kind of like the reductionism vs non-reductionism argument. In physics simple laws were discovered, however, in biology this will not be possible.