Geoffrey Hinton has a hunch about what’s next for AI Back in November, the computer scientist and cognitive psychologist Geoffrey Hinton had a hunch. After a half-century’s worth of attempts—some wildly successful—he’d arrived at another promising insight into how the brain works and how to replicate its circuitry in a computer. “It’s my current best bet about how things fit together,” Hinton says from his home office in Toronto. If his bet pays off, it might spark the next generation of artificial neural networks—mathematical computing systems, loosely inspired by the brain’s neurons and synapses, that are at the core of today’s artificial intelligence. It could also lead to more reliable and more trustworthy AI. Hinton wrote up his hunch, and posted a 44-page paper on the arXiv preprint server in February. He began with a disclaimer: “This paper does not describe a working system,” he wrote. Rather, it presents an “imaginary system" named "GLOM." Hinton thinks of GLOM as a way to model human perception in a machine. It also gets at the elusive goal of modelling intuition—our ability to effortlessly make analogies to make sense of the world. Hinton hopes GLOM might be one of several breakthroughs that he reckons are needed before AI is capable of truly nimble problem solving. Read the full story. —Siobhan Roberts This story is for subscribers only. The good news? You can subscribe and gain access to all of our award-winning journalism for just $50 a year. Sign up.
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