i'm envisioning a new kind of black-swan style mistake which i'm going to call the allegory of the neural net in the cave. people will feed neural nets data, a

i'm envisioning a new kind of black-swan style mistake which i'm going to call the allegory of the neural net in the cave. people will feed neural nets data, and ask it to describe the specific data set that the data is coming from -- without having the majority of that data set in hand. in this instance, it would be showing the neural net a picture of a 3D area, and then waiting for it to extrapolate the details of the rest. on average, the neural net's prediction may line up with reality. that is to say, the simulated data set is identical to the real data. that is what we are seeing in the OP link. but as soon as this method can apprehend and predict things of more complexity, that's where the differences will start to show. sure, it isn't the neural net's fault -- any one worth its salt will place a confidence estimate on its extrapolated data points. but people don't understand how to interpret those confidence estimates. they'll round up to 100%, or round down to 0% accuracy. once people start using these techniques to guide serious decisions in business or elsewhere, that's where those dastardly percentages between 0 and 100 come into play. imagine using this neural net as a way to generate returns in the context of trading stocks on wall st. it's a misuse of the tool, of course. but that won't stop people from making a decision based on a 95% probability of being correct; of course, 5% of the time, it will result in disaster. nor will it stop people from getting screwed by unknown unknowns. this is the stuff which the consulting businesses of the future are built on -- scolding people about abusing models while trying to preserve the power of the model as a tool. needless to say, i'm interested in where this goes.