AI has enjoyed huge growth in the past few
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.
