We analyzed every single AI paper ever uploaded to a major research database and found the era of deep learning may soon end.
We analyzed 16,625 papers to figure out where AI is headed next
...THE NEXT DECADE
Our analysis provides only the most recent snapshot of the competition among ideas that characterizes AI research. But it illustrates the fickleness of the quest to duplicate intelligence. “The key thing to realize is that nobody knows how to solve this problem,” Domingos says.
Many of the techniques used in the last 25 years originated at around the same time, in the 1950s, and have fallen in and out of favor with the challenges and successes of each decade. Neural networks, for example, peaked in the ’60s and briefly in the ’80s but nearly died before regaining their current popularity through deep learning.
Every decade, in other words, has essentially seen the reign of a different technique: neural networks in the late ’50s and ’60s, various symbolic approaches in the ’70s, knowledge-based systems in the ’80s, Bayesian networks in the ’90s, support vector machines in the ’00s, and neural networks again in the ’10s.
The 2020s should be no different, says Domingos, meaning the era of deep learning may soon come to an end. But characteristically, the research community has competing ideas about what will come next—whether an older technique will regain favor or whether the field will create an entirely new paradigm.
“If you answer that question,” Domingos says, “I want to patent the answer.”
