Convolutional neural networks are now applied to not just ordinary tasks like sorting cucumbers by quality but everything from predicting the best Go move to where in the world it was taken to whether a photograph is “interesting” or “pretty”, not to mention supercharging traditional tasks like radiology interpretation or facial recognition which have reached levels of accuracy that could only be dreamed of decades ago. 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.ĭeep learning’s rise over the past decade and dominance in image processing tasks has led to an explosion of applications attempting to infer high-level semantics locked up in raw sensory data like photographs. 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 “urban legend”, 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 then classified & never followed up on.
This story is often told to warn about the limits of algorithms and importance of data collection to avoid “dataset bias”/“data leakage” 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. 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.
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