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We analyzed 16,625 papers to figure out where AI is headed next

Our study of 25 years of artificial-intelligence research suggests the era of deep learning is coming to an end – “Almost everything you hear about artificial intelligence today is thanks to deep learning. 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 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. It’s been at the forefront of that effort for less than 10 years. 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.

At MIT Technology Review, we wanted to visualize these fits and starts. So we turned to one of the largest open-source databases of scientific papers, known as the arXiv (pronounced “archive”). We 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…”

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