Analysis of 16,625 Papers Points to the Future of AI
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.
Although 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.
“If somebody had written in 2011 that this was going to be on the front page of newspapers and magazines in a few years, we would’ve been like, ‘Wow, you’re smoking something really strong,’” said Pedro Domingos, a professor of computer science at the University of Washington and author of The Master Algorithm.
The sudden rise and fall of different techniques has characterized AI research for a long time, he said. 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.
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 18 November 2018 and tracked the words mentioned through the years to see how the field has evolved.
Through our analysis, we 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 growth in reinforcement learning in the past few years.
There are a couple of caveats. First, the arXiv’s AI section goes back only to 1993, while the term “artificial intelligence” dates to the 1950s, so the database represents just the latest chapters of the field’s history. Second, the papers added to the database each year represent a fraction of the work being done in the field at that moment. Nonetheless, the arXiv offers a great resource for gleaning some of the larger research trends and for seeing the push and pull of different ideas.
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