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By Sean McPherson
Jul 16, 2021
If you’re like many people, you view more streaming content now than ever. To keep you watching, content providers rely on machine-learning algorithms that recommend relevant new content.
But when the COVID-19 hit, viewing habits changed radically. Suddenly, different people were streaming different content at different times and in different ways. Were the ML algorithms now making less-relevant recommendations? And were they falsely confident in the accuracy of their less-precise predictions?
Such are the vagaries of “concept drift,” an issue few users of artificial intelligence are aware of. As government organizations leverage more AI in more far-flung locations, concept drift is a problem they’ll have to address. Particularly when deploying AI at the network’s edge, concept drift presents challenges.