An anonymous reader shares a post: If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies — including Google, Microsoft, IBM, and Facebook — have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes. Microsoft Azure’s computer vision API added the above caption and tags. But there are no sheep in the image. None. I zoomed all the way in and inspected every speck. It also tagged sheep in this image. I happen to know there were sheep nearby. But none actually present. Here’s one more example. In fact, the neural network hallucinated sheep every time it saw a landscape of this type. What’s going on here? Are neural networks just hyper-vigilant, finding sheep everywhere? No, as it turns out. They only see sheep where they expect to see them. They can find sheep easily in fields and mountainsides, but as soon as sheep start showing up in weird places, it becomes obvious how much the algorithms rely on guessing and probabilities. Bring sheep indoors, and they’re labeled as cats. Pick up a sheep (or a goat) in your arms, and they’re labeled as dogs.

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Source: Slashdot – Do Neural Nets Dream of Electric Sheep?