Google Researchers Have a New Alternative to Traditional Neural Networks

AI has enjoyed huge growth in the past few

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years, and much of that success is owed to deep neural networks, which provide the smarts behind impressive tricks like image recognition. But there is growing concern that some of the fundamental principles that have made those systems so successful may not be able to overcome the major problems facing AI—perhaps the biggest of which is a need for huge quantities of data from which to learn (for a deep dive on this, check out our feature "Is AI Riding a One-Trick Pony?").

Google’s Geoff Hinton appears to be among those fretting about AI's future. As Wired reports, Hinton has unveiled a new take on traditional neural networks that he calls capsule networks. In a pair of new papers—one published on the arXIv, the other on OpenReview—Hinton and a handful of colleagues explain how they work.

Their approach uses small groups of neurons, collectively known as capsules, which are organized into layers to identify things in video or images. When several capsules in one layer agree on having detected something, they activate a capsule at a higher level—and so on, until the network is able to make a judgment about what it sees. Each of those capsules is designed to detect a specific feature in an image in such a way that it can recognize them in different scenarios, like from varying angles.

https://www.technologyreview.com/the-download/609297/google-researchers-have-a-new-alternative-to-traditional-neural-networks/