Since the start of the pandemic, AI developers have deployed hundreds of machine learning tools to help diagnose COVID-19. The promise: to find patterns in the medical data like an algorithmic version of the television character Dr House. Recently, researchers have discovered that these AI tools were overhyped. Instead of discovering relevant connections between cases, the algorithms were making a litany of false assumptions, including predicting COVID cases based on the text font that hospitals happened to use in their documents.
This does not mean that machine learning is useless. It means that we need to better understand the strengths and limitations of AI.
To a human, it's obvious that a text font is not a good predictor for infectious diseases. But to a machine, that's not obvious at all. AI may be able to use informational input to make predictions, but it's not aware of what it's doing. It doesn't understand concepts or context, and is easily thrown off by biased or mislabelled data that wouldn't fool a four-year-old. As machine learning expert Janelle Shane explains in her AI weirdness book You Look Like A Thing And I Love You, the mistakes machines make feel absurd to us because they don't perceive the world like we do.
This story is from the June 2022 edition of BBC Science Focus.
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This story is from the June 2022 edition of BBC Science Focus.
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