Authoritative Intelligence: How Data Labelling Increases the Accuracy of AI Models

Whereas many still cling to the former, some, including Artificial Intelligence (AI) luminary Andrew Ng, fervently argue that data, not models, must be at the core of the advancement of AI. It turns out he's got a point; in fact, more than one. But let's start at the beginning. While all ML models essentially try to make predictions, the labels' accuracy determines whether these predictions hold true in real life. In other words, the labeled aspects of data need to be invariably consistent with the "outside world," i.e. the actual conditions for which the model was designed. For this very reason, as far as the model is concerned, data labels come before everything else.