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.

Artificial intelligence (AI) represents a powerful but double-edged sword as nations confront global warming, poverty and issues of peace and justice. An international team of scientists this week released a first-ever study of how AI can help--as well as hinder--sustainable development worldwide. Published today in Nature Communications, the analysis focuses on how AI impacts the 17 goals for sustainable development adopted by the United Nations in 2015. The study was co-authored by a diverse group of researchers led by Ricardo Vinuesa and Francesco Fuso Nerini, assistant professors at KTH Royal Institute of Technology. They were joined by Max Tegmark, professor at Massachusetts Institute of Technology (MIT) and author of the bestselling book Life 3.0, as well as Virginia Dignum, professor of AI Ethics at Umeå University, among other authors.