My other SIAM News contributions are necessarily math-focused. This one is a bit different: a review of a very good and funny popular-science book about machine learning and its failures.
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The Threat of AI Comes from Inside the House
For SIAM News:
Artificial intelligence (AI) will either destroy us or save us, depending on who you ask. Self-driving cars might soon be everywhere, if we can prevent them from running over pedestrians. Public cameras with automated face recognition technology will either avert crime or create inescapable police states. Some tech billionaires are even investing in projects that aim to determine if we are enslaved by computers in some type of Matrix-style simulation.
In reality, the truest dangers of AI arise from the people creating it. In her new book, You Look Like a Thing and I Love You, Janelle Shane describes how machine learning is often good at narrowly-defined tasks but usually fails for open-ended problems.
Shane—who holds degrees in physics and electrical engineering—observes that we expect computers to be better than humans in areas where the latter often fail. This seems unreasonable, considering that we are the ones teaching the machines how to do their jobs. Problems in AI often stem from these very human failings.