That’s because the “field” was a sandbox back when this experiment was performed. It was one of the first successful neural nets, and it had to be handled by people who knew the field it was dabbling in - satellite photo reconnaissance.
Hindsight is 20-20 in most instances, but sometimes the people in charge aren’t looking at the history one would like them two.
For many applications, I think it’s fine to use self-teaching software. At this point, I don’t agree that the aero field is ready to take the risk.
Have a great evening - NCIS is coming on, so I’m going offline. (A man has to have a FEW vices, right?!? ;-P)
NCIS has been one of my favorites. Can’t keep my interest up in re-runs forever though (which is what we get were I am).
There are certain basic scientific rules that aren’t violated when you switch the form of implementation. Pattern recognition is one of those things I focused on as a student; and even though I did different things in the real world, there were plenty of bits of scientific wisdom from that part of my education that served me well.
Just to bring you up-to-date, there are now techniques using genetic programming, sometimes combined with neural networks that do quite well.
Also, consider the fact that you - as a human - automatically notice things in your peripheral vision that grab your attention: a highly effective sort of early warning system. You don’t have to be staring right at something that should get your attention and thinking consciously about it. You don’t have to look at it clearly and for a long time, studying the many subtleties to have your early warning (sort of, it doesn’t only apply to bad things) alarm tripped.
This has led to some interesting experiments in improving recognition by actually reducing - filtering - the information being processed. Counter-intuitive; but it’s improved reliability - and it’s faster of course.