New AI Paradigm May Reduce a Heavy Carbon Footprint

Artificial intelligence (AI) machine learning can have a considerable carbon footprint. Deep learning is inherently costly, as it requires massive computational and energy resources. Now researchers in the U.K. have discovered how to create an energy-efficient artificial neural network without sacrificing accuracy and published the findings in Nature Communications on August 26, 2020. The biological brain is the inspiration for neuromorphic computing--an interdisciplinary approach that draws upon neuroscience, physics, artificial intelligence, computer science, and electrical engineering to create artificial neural systems that mimic biological functions and systems. The human brain is a complex system of roughly 86 billion neurons, 200 billion neurons, and hundreds of trillions of synapses.