Economics Nobel Prize Winner Sees No Singularity on the Horizon

William Nordhaus looked for leading indicators of a Singularity back in 2015 but couldn't find any

2 min read
Illustration of William Nordhaus holding up puzzle pieces with text on them.
Illustration: Johan Harbestad/The Royal Swedish Academy of Sciences

The two economists who today were awarded the Nobel Prize have both written extensively on the role that technology plays in economic growth, and one of them has even investigated what enthusiasts in Silicon Valley call the Singularity.

We called it “the rapture of the geeks” in our special issue on the topic 10 years ago, because it envisages not merely an explosive increase in computational prowess that would greatly increase economic output but also the uploading of human minds into a kind of cosmic cloud. Thus embodied, our intellects would expand and our life spans would become godlike. That’s heady stuff for an engineering culture that still can’t get a smartphone battery to last all day.

Of the two winners of what is technically known as the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred NobelWilliam Nordhaus was honored for research in environmental economics and Paul Romer for his work on economic growth. But though the Singularity is the ultimate in economic growth, it was Nordhaus who tackled it (although “in this area his work intersects with Romer’s quite closely,” writes economist Tyler Cowen, in a blog post this morning).

Nordhaus illustration Illustration: Niklas Elmehad/Nobel Media

In a 2015 paper, Nordhaus reasoned that the Singularity would be necessarily preceded by ever greater technological progress that would accelerate the replacement of human labor by automation. More work would be accomplished with less labor, so the level of productivity would rise. But in actual fact, he noted, productivity has been in the doldrums for a long time, and there seems to be no systematic rise in unemployment.

True, he wrote that paper three years ago, and three years is a long time to the Singulatarians (yes, it’s a word). They argue that as machines become smarter, they’ll make it easier for engineers to design still smarter machines. The process will feed on itself until machines do all the research, and then, all at once, we’ll reach takeoff velocity. You might turn out the lights in the laboratory on Friday evening, then turn them back on again on Monday only to find a big surprise waiting for you there. 

But don’t hold your breath.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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