The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior.<\/p>\n<\/p>\n
The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports.<\/p>\n
And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed.<\/p>\n
For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools.<\/p>\n
An online retailer found that its AI was ordering stock that no longer matched with what was selling. And a firm that uses AI to recommend investments based on sentiment analysis of news stories was confused by the generally negative tone throughout the media.<\/p>\n
\"The situation is so volatile,\" Rael Cline, CEO of the algorithmic marketing consulting firm Nozzle, told MIT Tech.<\/p>\n
\"You're trying to optimize for toilet paper last week, and this week everyone wants to buy puzzles or gym equipment.\"<\/p>\n
While some companies are dedicating more time and resources to manually steering their algorithms, others see this as an opportunity to improve.<\/p>\n
\"A pandemic like this is a perfect trigger to build better machine-learning models,\" Sharma said.<\/p>\n
READ MORE: Our weird behavior during the pandemic is messing with AI models<\/p>\n
This article was originally published by Futurism. Read the original article.<\/p>\n<\/p>\n
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