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At some point, every one of us has had the feeling that online applications like YouTube and Amazon and Spotify seem to know us better than ourselves, recommending content that we like even before we say it. At the heart of these platforms’ success are artificial intelligence algorithms—or more precisely, machine learning models—that can find intricate patterns in huge sets of data.
Corporations in different sectors leverage the power of machine learning along with the availability of big data and compute resources to bring remarkable enhancement to all sorts of operations, including content recommendation, inventory management, sales forecasting, and fraud detection. Yet, despite their seemingly magical behavior, current AI algorithms are very efficient statistical engines that can predict outcomes as long as they don’t deviate too much from the norm.
But during the coronavirus pandemic, things are anything but normal. We’re working and studying from home, commuting less, shopping more online and less from brick-and-mortar stores, Zooming instead of meeting in person, and doing anything we can to stop the spread of COVID-19.
The coronavirus lockdown has broken many things, including the AI algorithms that seemed to be working so smoothly before. Warehouses that depended on machine learning to keep their stock filled at all times are no longer able to predict the right items that need to be replenished. Fraud detection systems that home in on anomalous behavior are confused by new shopping and spending habits. And shopping recommendations just aren’t as good as they used to be.
This article has been published from a wire agency feed without modifications to the text. Only the headline has been changed.