According to a UN report on the State of Food Security and Nutrition in the World, 811 million people in the world went hungry in 2020. The same report estimates that 118 million people in the world suffer from chronic hunger. A new study from the University of Illinois explores how machine learning can help improve forecasting when used appropriately. Tech Xplore brought this interesting topic to our attention in their article, “How machine learning can improve food insecurity predictions.”
The study asserts machine learning models can help provide critical information to assist the forecasting process, making it more objective, focused, and transparent. But the authors emphasize data must be used in a thoughtful way and interpreted correctly in conjunction with policymakers from the start.
Some innovators are aware and dreaming up ways to leverage new technology to address these harsh realities. In fact, machine learning and other artificial intelligence (AI)-driven technologies aren’t just in the dream phase of providing solutions to global food insecurity, they are on the ground and in action.
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Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
