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Inside the maths that drives AI

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2024-07-04 02:00:03

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People usually talk about the race to the bottom in artificial intelligence as a bad thing. But it’s different when you’re discussing loss functions.

Loss functions are a crucial but frequently overlooked component of useful artificial intelligence (AI), and they’re all about getting to the bottom — albeit of a literal curve on a graph — as quickly as possible. When training an algorithm to automate tedious data analysis, such as looking for specific features in millions of photographs, you need a way of measuring its performance. That’s the ‘loss function’: it measures an algorithm’s error relative to the ‘ground truth’ of the data — information that is known to be real or true. Then you adjust the algorithm’s parameters, rinse and repeat, and hope the error is smaller next time. “You’re trying to find a minimum: the point where the error is as small as possible — hopefully zero,” says Anna Bosman, a computational-intelligence researcher at the University of Pretoria.

Dozens of off-the-shelf loss functions have been written. But choose the wrong one, or just handle it badly, and the algorithm can lead you astray. It could blatantly contradict human observations, make random fluctuations (known as experimental noise) look like data, or even obscure the central results of an experiment. “There are lots of things that can go wrong,” Bosman says. And worst of all, the opacity of AI means you might not even know that you’ve been misled.

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