The sudden rise and fall of different techniques has characterized AI research for a long time. 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 November 18, 2018, and tracked the words mentioned through the years to see how the field has evolved.
The biggest shift we found was a transition away from knowledge-based systems by the early 2000s. These computer programs are based on the idea that you can use rules to encode all human knowledge. In their place, researchers turned to machine learning—the parent category of algorithms that includes deep learning.
Among the top 100 words mentioned, those related to knowledge-based systems—like “logic,” “constraint,” and “rule”—saw the greatest decline. Those related to machine learning—like “data,” “network,” and “performance”—saw the highest growth.
Under the new machine-learning paradigm, the shift to deep learning didn’t happen immediately. Instead, as our analysis of key terms shows, researchers tested a variety of methods in addition to neural networks, the core machinery of deep learning. Some of the other popular techniques included Bayesian networks, support vector machines, and evolutionary algorithms, all of which take different approaches to finding patterns in data.
Through the 1990s and 2000s, there was steady competition between all of these methods. Then, in 2012, a pivotal breakthrough led to another sea change. During the annual ImageNet competition, intended to spur progress in computer vision, a researcher named Geoffrey Hinton, along with his colleagues at the University of Toronto, achieved the best accuracy in image recognition by an astonishing margin of more than 10 percentage points.
The technique he used, deep learning, sparked a wave of new research—first within the vision community and then beyond. As more and more researchers began using it to achieve impressive results, its popularity—along with that of neural networks—exploded.