The voracious appetite for energy from the world's computers and communications technology presents a clear threat for the globe's warming climate. That was the blunt assessment from presenters in the intensive two-day Climate Implications of Computing and Communications workshop held on March 3 and 4, hosted by MIT's Climate and Sustainability Consortium (MCSC), MIT-IBM Watson AI Lab, and the Schwarzman College of Computing. The virtual event featured rich discussions and highlighted opportunities for collaboration among an interdisciplinary group of MIT faculty and researchers and industry leaders across multiple sectors -- underscoring the power of academia and industry coming together. "If we continue with the existing trajectory of compute energy, by 2040, we are supposed to hit the world's energy production capacity. The increase in compute energy and demand has been increasing at a much faster rate than the world energy production capacity increase," said Bilge Yildiz, the Breene M. Kerr Professor in the MIT departments of Nuclear Science and Engineering and Materials Science and Engineering, one of the workshop's 18 presenters.

OUTLINE: 0:00 - Introduction 0:43 - Talk overview 1:18 - Compute for deep learning 5:48 - Power consumption for deep learning, robotics, and AI 9:23 - Deep learning in the context of resource use 12:29 - Deep learning basics 20:28 - Hardware acceleration for deep learning 57:54 - Looking beyond the DNN accelerator for acceleration 1:03:45 - Beyond deep neural networks CONNECT: - If you enjoyed this video, please subscribe to this channel.

The common narrative of artificial intelligence is that it has finally taken off in recent years because there was enough data -- from mega repositories like Google -- and enough computing power through racks of servers equipped with fast processors and GPUs. That's not incorrect, but it's too simplistic to describe the future of machine learning and other forms of AI. That was the message from Intel's CTO of AI products, Amir Khosrowshahi, at VentureBeat's Transform 2018 conference outside San Francisco today. The challenge now is optimizing the whole process. Better algorithms require less computing and can draw accurate inferences from less data, said Khosrowshahi, cofounder of AI company Nervana Systems, which Intel acquired in August 2016.