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, where he’s been sequestered during the pandemic. 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. His “honest motivation,” as he puts it, is curiosity. But the practical motivation—and, ideally, the consequence—is more reliable and more trustworthy AI.

A Google engineering fellow and cofounder of the Vector Institute for Artificial Intelligence, Hinton wrote up his hunch in fits and starts, and at the end of February announced via Twitter that he’d posted a 44-page paper on the arXiv preprint server. He began with a disclaimer: “This paper does not describe a working system,” he wrote. Rather, it presents an “imaginary system.” He named it, “GLOM.” The term derives from “agglomerate” and the expression “glom together.”

Hinton thinks of GLOM as a way to model human perception in a machine—it offers a new way to process and represent visual information in a neural network. On a technical level, the guts of it involve a glomming together of similar vectors. Vectors are fundamental to neural networks—a vector is an array of numbers that encodes information. The simplest example is the xyz coordinates of a point—three numbers that indicate where the point is in three-dimensional space. A six-dimensional vector contains three more pieces of information—maybe the red-green-blue values for the point’s color. In a neural net, vectors in hundreds or thousands of dimensions represent entire images or words. And dealing in yet higher dimensions, Hinton believes that what goes on in our brains involves “big vectors of neural activity.”

By way of analogy, Hinton likens his glomming together of similar vectors to the dynamic of an echo chamber—the amplification of similar beliefs. “An echo chamber is a complete disaster for politics and society, but for neural nets it’s a great thing,” Hinton says. The notion of echo chambers mapped onto neural networks he calls “islands of identical vectors,” or more colloquially, “islands of agreement”—when vectors agree about the nature of their information, they point in the same direction.

“If neural nets were more like people, at least they can go wrong the same ways as people do, and so we’ll get some insight into what might confuse them.”

Geoffrey Hinton

In spirit, GLOM also gets at the elusive goal of modelling intuition—Hinton thinks of intuition as crucial to perception. He defines intuition as our

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By: Siobhan Roberts
Title: Geoffrey Hinton has a hunch about what’s next for AI
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Published Date: Fri, 16 Apr 2021 10:00:00 +0000

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