MIT researchers warn that deep learning is reaching its computational limit

The rising demand for Deep Learning is so massive and complex that we are reaching the computational limits of the technology. A recent study suggests that progress in deep learning is heavily dependent on the increase in computational abilities. Researchers from Massachusetts Institute of Technology (MIT), MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia found in a recent study that deep learning is strong reliant on the increase in compute. The researchers believe that the continuous progress in Deep Learning will require dramatically more computational methods. In the research paper, co-authors wrote, "We show deep learning is not computationally expensive by accident, but by design. The same flexibility that makes it excellent at modelling diverse phenomena and outperforming expert models also makes it dramatically more computationally expensive. Despite this, we find that the actual computational burden of deep learning models is scaling more rapidly than (known) lower bounds from theory, suggesting that substantial improvements might be possible."