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" ... If we feed into the ML/DL a slew of examples that have a person saying certain words and then have lots of people that also state those words, the mathematical calculations can attempt to ascertain a type of correlation between how the lips moved and the words that were being uttered. A difficulty sometimes though is that the ML/DL can overfit the data being used, becoming only capable of detecting words as spoken by the type of language and speakers chosen for the training data set. ... "
" ... Many times, models run into problems not because of bad data or even poorly selected data, but because there’s some inherent bias in the training data. The word “bias” is somewhat overloaded in the machine learning sense since we use it in three different ways: in the sense of the weights and “biases” set in a neural network, in the separate sense of the “bias”-variance tradeoff that balance overfit or underfit, and in the more widely understood sense of informational “bias” that is imposed by humans making decisions based on their own preconceived notions. It’s the last sense that we are most concerned about here. ... "
" ... There should be less tuning involved as the AI becomes more accurate, and the product should always limit the potential for user impact. Users can inadvertently degrade the model by training it in the wrong direction — causing the AI to return increasingly inaccurate predictions. This is called "model drift" and often happens when a model is overfit on new data and stops working on data it used to work well on. ... "
" ... “It's very easy to overfit the data capture part of the system to solve a specific problem, but it's important to resist the impulse to optimize for performance and efficiency too early,” Oostendorp said. “Instead be thinking about optionality -- once you've thrown data away, you can't get it back." ... "