An analysis of all 16,625 papers available in the "artificial intelligence" section of the arXiv open-source database of scientific papers 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.
Much current news about artificial intelligence is thanks to deep learning. But deep learning represents just a small blip in the history of humanity's quest to replicate intelligence. 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.
Every decade 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 Pedro Domingos, a professor of computer science at the University of Washington, meaning the era of deep learning may soon come to an end.
From Technology Review
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