MIT Argues That The Era of Deep Learning In AI May Soon End


First, if you are interested in technology and AI and haven’t joined the MIT Technology Review email lists, do so now! To follow the hyperlinks go to the full article here. The Algorithm email I received today studied more than 16,000 papers in the arXiv library published on AI and found waves of AI technology that displace older tech:

“Hello Algorithm readers,

Almost everything you hear about artificial intelligence today is thanks to deep learning. (We’ve talked about this in the Algorithm before.) This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities now power Google’s search, Facebook’s news feed, and Netflix’s recommendation engine—and are transforming industries like health care and education.

But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity’s quest to replicate our own intelligence. When you zoom out on the whole history of the field, it’s easy to realize that it could soon be on its way out.

AI research has long been characterized by the sudden rise and fall of different techniques, and every decade has seen a heated competition between different ideas. Then, once in a while, a switch flips, and everyone in the community converges on a specific one.

I wanted to visualize these fits and starts, so I turned to one of the largest open-source databases of scientific papers, known as the arXiv (pronounced “archive”). I downloaded the abstracts of all 16,625 papers available in the “artificial intelligence” section through November 18, 2018, and tracked the words mentioned through the years to see how the field has evolved.

Through my analysis, I found three major trends: a shift toward machine learning during the late 1990s and early 2000s, a rise in the popularity of neural networks beginning in the early 2010s, and the growth in reinforcement learning in the past few years.

Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group

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MIT Argues That The Era of Deep Learning In AI May Soon End

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MIT Argues That The Era of Deep Learning In AI May Soon End

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The Algorithm email I received today studied more than 16,000 papers in the arXiv library published on AI and found waves of AI technology that displace older tech

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Tim Sloane

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PaymentsJournal

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