Deep Learning Reaching Computational Limits, Warns New MIT Study

The study states that deep learning's impressive progress has come with a "voracious appetite for computing power." Researchers at the Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia have found that we are reaching computational limits for deep learning. The new study states that deep learning's progress has come with a "voracious appetite for computing power" and that continued development will require "dramatically" more computationally efficient methods. "We show deep learning is not computationally expensive by accident, but by design. The same flexibility that makes it excellent at modeling diverse phenomena and outperforming expert models also makes it dramatically more computationally expensive," the coauthors wrote.