Towards A More Transparent AI

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One cornerstone of making AI work is machine learning – the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it’s been the explosion in research and application of machine learning that’s made AI the hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning to data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data.Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. Bad data results in bad models. In many cases, these bad models are easy to spot since they perform poorly. For example, if you built a machine learning model to identify cats in images, if the model identifies butterflies as cats or fails to spot obvious cats in …

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