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A decade ago, the artificial-intelligence pioneer transformed the field with a major breakthrough. Now he’s working on a new imaginary system named GLOM.

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.
Hinton thinks of GLOM (“Geoff’s Last Original Model.”) 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.
Hinton hopes GLOM might be one of several breakthroughs that he reckons are needed before AI is capable of truly nimble problem solving—the kind of human-like thinking that would allow a system to make sense of things never before encountered; to draw upon similarities from past experiences, play around with ideas, generalize, extrapolate, understand.
GLOM is designed to sound philosophically plausible. But will it work?

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CONTEXT

A decade ago, the artificial-intelligence pioneer transformed the field with a major breakthrough. Now he’s working on a new imaginary system named GLOM.
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.
Hinton thinks of GLOM (“Geoff’s Last Original Model.”) 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.
Hinton hopes GLOM might be one of several breakthroughs that he reckons are needed before AI is capable of truly nimble problem solving—the kind of human-like thinking that would allow a system to make sense of things never before encountered; to draw upon similarities from past experiences, play around with ideas, generalize, extrapolate, understand.
GLOM is designed to sound philosophically plausible. But will it work?

Category 2: The Big Picture of the Digital Age

[genioux fact deduced or extracted from MIT Technology Review]

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Type of essential knowledge of this “genioux fact”: Essential Deduced and Extracted Knowledge (EDEK).

Type of validity of the "genioux fact".

Inherited from sources + Supported by the knowledge of one or more experts + Supported by research.

Authors of the genioux fact

Fernando Machuca

References

Geoffrey Hinton has a hunch about what’s next for AI, Siobhan Roberts, April 16, 2021, MIT Technology Review.

ABOUT THE AUTHORS

Siobhan Roberts (@sioroberts)

I’m a senior editor at MIT Technology Review, writing about computing (among other things). Over the years I’ve reported on everything from crumpled paper to knitting as coding to the 120-sided dice you never knew you needed—for The New York Times, The New Yorker, Quanta, Nautilus, The Walrus, and beyond. My latest book is Genius at Play: The Curious Mind of John Horton Conway (Bloomsbury).