As deep learning moves from the lab into production use in mission critical fields from medicine to driverless cars, we must recognize its very real limitations as nothing more than a pile of software code and statistics, rather than the learning and thinking intelligences we describe them as. In the end, as we ascribe our own aspirations to mundane piles of code, anthropomorphizing them into living breathing silicon humans, rather than merely statistical representations of patterns in data, we lose track of their very real limitations and think in terms of utopian hyperbole rather then the very real risk calculus needed to ensure their safe and robust integration into our lives.
Thomas Dietterich offered his commentary on Twitter about the piece.