New physics-based self-learning machines could replace current artificial neural networks and save energy
Artificial intelligence not only affords impressive performance, but also creates significant demand for energy. The more demanding the tasks for which it is trained, the more energy it consumes.
Artificial intelligence (AI) models like ChatGPT run on algorithms and have great appetites for data, which they process through machine learning, but what about the limits of their data-processing abilities? Researchers led by Professor Sun Zhong from Peking University's School of Integrated Circuits and Institute for Artificial Intelligence set out…
Addressing the staggering power and energy demands of artificial intelligence, engineers at the University of Houston have developed a revolutionary new thin-film material that promises to make AI devices significantly faster while dramatically cutting energy consumption.
As artificial intelligence systems grow larger and more powerful, their energy demands are rising dramatically. But recent research from the University of Massachusetts Amherst published in Nature Communications suggests that advanced AI capabilities may be achievable with dramatically lower energy consumption.