Out With the Old
Artificial intelligence developers may soon find themselves on the brink of a paradigm shift. Deep learning has dominated the field for several years — but may be on its way out.
The field of AI has shifted focus roughly every decade since it began in the 1950s. Now, a new analysis suggests that the 2020s will be no different.
A team from MIT Technology Review scanned all 16,625 research papers in the artificial intelligence section of arXiv, an open-source repository for sharing research, that were published between 1993 and November 18.
MIT Tech found that on machine learning started to pick up over the last 20 years — rapidly increasing in prevalence since about 2008 — but now that research fervor seems to be dying down.
Many of the new developments in artificial intelligence that we hear about nowadays are actually just applications of machine learning techniques that have been hammered out for years.
And as the research community’s attention shifts from deep learning, it remains unclear what will take its place, according to MIT Tech. In the past, older types of artificial intelligence that didn’t really take off when they were first developed later resurfaced and taken off with new life. For instance, scientists first developed machine learning decades ago, but it only became commonplace about a decade ago.
MIT Tech didn’t predict what will come next. It may be that some form of existing technology will finally hits its stride, but it’s also possible that an AI engineer will develop some brand-new type of AI that’ll shape the future.
READ MORE: We analyzed 16,625 papers to figure out where AI is headed next [MIT Technology Review]