It’s been most famously used to swap celebrities into movies and even erase unwanted moustaches, but the real power of artificial intelligence is its ability to spot patterns in large amounts of data and make startlingly accurate predictions.
Researchers at the Boston University School of Public Health have now trained a deep learning AI to find dangerous food items potentially needing a recall by analysing reviews on Amazon’s website.
In a study published yesterday in the Journal of the American Medical Informatics Association, the researchers detail the steps they went through to train their neural network, which started with the arduous task of collecting 1,297,156 reviews of food products sold on Amazon.com and then matched 5,149 of them to products that had been officially recalled by the US Food and Drug Administration (FDA) between 2012 and 2014.
The next step was to teach a type of deep learning AI known as a Bidirectional Encoder Representation from Transformations—or BERT, for short—to spot telltale terminology in these reviews that could indicate a product was legitimately unsafe.
That required real people to sort 6,000 of the collected reviews that contained the same words and terminology the FDA used to justify recalls (like “sick,” “rotten,” and even “label”) into four different categories.
Those included if the reviewer got sick, had an allergic reaction, or found an error in the product’s labelling; the product looked or tasted bad, was expired, or needed further inspection; the reviewer made no claims the product was unsafe; or none of the previous three categorisations.