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The Problem with Machine Learning

Machine learning is built upon sample data sets: what could go wrong?

I’ve been talking about machine learning and smart APIs recently, where I think there is great potential to make pre-editing tasks much easier. But they are not without their downside. They are built on sample data sets to ‘train’ the algorithm. If that training set is not truly representative of the whole data set, then the results will go horribly wrong.

Cory Doctorow at Boing Boing uses the Trump campaign as an example of how this can play out in ‘the real world’.