The rise of reinforcement learning

“In the few years since the rise of deep learning, our analysis reveals, a third and final shift has taken place in AI research.

As well as the different techniques in machine learning, there are three different types: supervised, unsupervised, and reinforcement learning. Supervised learning, which involves feeding a machine labeled data, is the most commonly used and also has the most practical applications by far. In the last few years, however, reinforcement learning, which mimics the process of training animals through punishments and rewards, has seen a rapid uptick of mentions in paper abstracts.

The idea isn’t new, but for many decades it didn’t really work. “The supervised-learning people would make fun of the reinforcement-learning people,” Domingos says. But, just as with deep learning, one pivotal moment suddenly placed it on the map.

That moment came in October 2015, when DeepMind’s AlphaGo, trained with reinforcement learning, defeated the world champion in the ancient game of Go. The effect on the research community was immediate. 

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.”

https://www.technologyreview.com/s/612768/we-analyzed-16625-papers-to-figure-out-where-ai-is-headed-next/