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There's even hierarchical models with an equation giving the probability that an item will be observed at all, conditioned to known features.
Those who don't know their statistical models are bound to reinvent statistical theory.
Umm, but then a story linked from the article as "alternative example" (thus presumably "better" than the tank story), and it being one from HN by the way, seems to have a nearly identical gist, at least for me as a layman: https://news.ycombinator.com/item?id=6269114 - only not about neural nets, but genetic/evolutionary algorithms. Or is it somehow drastically different and I just don't understand that?
And there is a big difference between something that happened and something that did not happen.
I always heard the version that went the other way around. After it was shown that single layer perceptrons were unable to deal with data sets that weren't linearly separable, there was an effort to figure out how the single layer tank classifier was working.
>I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning I don't see how this story gives a "misleading" view of deep learning. From my (admittedly limited) experience with self-driving RC cars, this type of mistake is quite easy for a neural net to make while being quite difficult to detect. In our case, after utilizing a visual back-prop meth…
Another more modern and well-documented example of this would seem to occur in a 2015 write-up of the "Right Whale" competition in Kaggle: http://felixlaumon.github.io/2015/01/08/kaggle-right-whale.h... Contrary to this author's claims, despite using data augmentation and a fancy modern CNN, a neural network trained to identify whales hit a local optimum where it looked at patterns in waves on the water to identify t…
> This naive approach yielded a validation score of just ~5.8 (logloss, lower the better) which was barely better than a random guess.
which is different from the tank story. For the tanks, the neural network appeared to perform well, but was actually not looking at the tanks. Here, it never performed well, and when debugging why not he found that it was not looking at the whales.
Earlier quoted context omitted.
>That comment section took an immediate and unexpected turn for the worse. What the heck is going on there?
Terry Davis - he occasionally chimes in here with similarly themed posts (but only if you have show dead enabled). He's schizophrenic, is famous for TempleOS and infamous for the contents of his posts on the internet.
All this speculation is silly. Just generate your own data set (since the story is from the early 90s, if not earlier, the number of training examples would have been quite small compared to today's data sets) and see if today's networks make the same mistake.
I always heard the version that went the other way around. After it was shown that single layer perceptrons were unable to deal with data sets that weren't linearly separable, there was an effort to figure out how the single layer tank classifier was working.
That's an interesting variant - none of the versions I've seen so far link it to Minsky's perceptron book. Any chance you recall where you saw that one?
I first encountered this idea in a sci-fi story (I want to say it was one of Peter Watts' "Rifters" novels, but I can't find it now). The idea was that someone trained a neural network to look at live video feeds of passengers moving through a subway station, and control the station's ventilation system. Unfortunately, the movements of individual people were fairly random, whereas the large-scale traffic patterns wer…