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Putting other vehicles in the same forests got similar high hits but tanks by themselves (in desert test ranges) didn’t register.Luckily a sceptic somewhere decided to representations, it operates in a specifically immaterial way…So, awareness is not explained by connectionism.A cautionary tale in artificial intelligence tells about researchers training an neural network (NN) to detect tanks in photographs, succeeding, only to realize the photographs had been collected under specific conditions for tanks/non-tanks and the NN had learned something useless like time of day.This story is often told to warn about the limits of algorithms and importance of data collection to avoid where the collected data can be solved using algorithms that do not generalize to the true data distribution, but the tank story is usually never sourced.Then they tested the same systems in other environment and there results were suddenly shockingly bad.Turns out the image recognition was keying off the trees with tank-like minor features rather than the tank itself.
It didn’t learn the differences between dogs and wolves, but instead learned that wolves were on snow in their picture and dogs were on grass.If you notice that you look at something this way, and then that way, and it looks different, you’ll notice something is odd. Nor is it able to identify any bias that might exist in the corpus of data it was trained on…or maybe it is.If there is any property of the training data set that is strongly predictive of the training criterion, it will zero in on that property with the ferocious clarity of Darwinism.I collate many extent versions dating back a quarter of a century to 1992 along with two NN-related anecdotes from the 1960s; their contradictions & details indicate a classic , with a probable origin in a speculative question in the 1960s by Edward Fredkin at an AI conference about some early NN research, which was subsequently classified & never followed up on.I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning and that it would be better to not repeat it but use real examples of dataset bias and focus on larger-scale risks like AI systems optimizing for wrong utility functions. T.’s Center for Brains, Minds and Machines, offered a classic parable used to illustrate this disconnect.